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1
Basharat, Arooj, and Zilly Huma. "Enhancing Resilience: Smart Grid Cybersecurity and Fault Diagnosis Strategies." Asian Journal of Research in Computer Science 17, no. 6 (2024): 1–12. http://dx.doi.org/10.9734/ajrcos/2024/v17i6453.
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The increasing integration of advanced technologies within the power grid infrastructure has led to significant advancements in efficiency, reliability, and sustainability. However, this integration also introduces new vulnerabilities, particularly in the realm of cybersecurity. This paper presents an overview of smart grid cybersecurity challenges and proposes strategies for enhancing resilience through fault diagnosis techniques. Firstly, the paper examines the evolving threat landscape facing smart grids, encompassing cyber-attacks, insider threats, and natural disasters. It highlights the critical need for robust cybersecurity measures to safeguard grid operations and prevent potentially catastrophic disruptions. Next, the paper delves into various cybersecurity frameworks and standards tailored specifically for smart grids, emphasizing the importance of comprehensive risk assessment, intrusion detection systems, and secure communication protocols. Additionally, it discusses the role of machine learning and artificial intelligence in augmenting cyber defense capabilities, enabling proactive threat detection and rapid response. Furthermore, the paper explores fault diagnosis strategies aimed at maintaining grid resilience in the face of cyber incidents or physical faults. It discusses the integration of data analytics, predictive modeling, and real-time monitoring to identify and mitigate potential grid disturbances swiftly.
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Upadhyay, Aakanksha. "Investigate Advanced Techniques for Power System Protection, Fault Detection, and Restoration to Enhance the Reliability and Resilience of Electrical Grids." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 02 (2025): 1–9. https://doi.org/10.55041/ijsrem41405.
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Investigate Advanced Techniques for Power System Protection, Fault Detection, and Restoration is a critical area of study aimed at enhancing the reliability and resilience of electrical grids. As modern power systems face increasing complexities due to the integration of renewable energy sources, aging infrastructure, and growing demand for electricity, innovative protection strategies have become essential to mitigate risks associated with electrical faults and disturbances. The significance of this topic lies not only in improving the operational efficiency of power systems but also in minimizing economic losses and ensuring public safety. Advanced techniques in power system protection encompass a range of methodologies, including the implementation of digital protective relays, circuit breakers, and sophisticated communication systems. These technologies enable real-time monitoring and rapid fault detection, which are vital for maintaining continuous power supply. The evolution of machine learning (ML) and artificial intelligence (AI) has further revolutionized this field by facilitating self-healing grids that can autonomously detect, diagnose, and restore service after outages, significantly reducing outage durations and enhancing reliability metrics such as the System Average Interruption Duration Index (SAIDI). Despite the advancements, challenges remain, particularly concerning cyber security risks, regulatory compliance, and the adaptation of protection schemes to accommodate fluctuating energy sources. These complexities necessitate ongoing research and development to create adaptive systems capable of responding to dynamic operational conditions while safeguarding infrastructure integrity. Moreover, the integration of emerging technologies must balance innovation with practical application, ensuring that protective measures not only meet current needs but also anticipate future demands within the energy sector. This investigation into advanced power system protection techniques is pivotal for addressing the pressing issues of system reliability and resilience in the face of evolving electrical grid dynamics. It highlights the critical role of technology in modernizing utility operations and lays the groundwork for future advancements in grid management and outage restoration strategies. Key Words: Power system protection, Fault detection, Distributed Energy Resources (DERs), Islanding, Grid reconnection, Fault localization, Microgrid protection, Fault current variability, System resilience
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Almasoudi, Fahad M. "Enhancing Power Grid Resilience through Real-Time Fault Detection and Remediation Using Advanced Hybrid Machine Learning Models." Sustainability 15, no. 10 (2023): 8348. http://dx.doi.org/10.3390/su15108348.
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Ensuring a reliable and uninterrupted supply of electricity is crucial for sustaining modern and advanced societies. Traditionally, power systems analysis was mostly dependent on formal commercial software, mathematical models produced via a mix of data analysis, control theory, and statistical methods. As power grids continue to grow and the need for more efficient and sustainable energy systems arises, attention has shifted towards incorporating artificial intelligence (AI) into traditional power grid systems, making their upgrade imperative. AI-based prediction and forecasting techniques are now being utilized to improve power production, transmission, and distribution to industrial and residential consumers. This paradigm shift is driven by the development of new methods and technologies. These technologies enable faster and more accurate fault prediction and detection, leading to quicker and more effective fault removal. Therefore, incorporating AI in modern power grids is critical for ensuring their resilience, efficiency, and sustainability, ultimately contributing to a cleaner and greener energy future. This paper focuses on integrating artificial intelligence (AI) in modern power generation grids, particularly in the fourth industrial revolution (4IR) context. With the increasing complexity and demand for more efficient and reliable power systems, AI has emerged as a possible approach to solve these difficulties. For this purpose, real-time data are collected from the user side, and internal and external grid faults occurred during a time period of three years. Specifically, this research delves into using state-of-the-art machine learning hybrid models at end-user locations for fault prediction and detection in electricity grids. In this study, hybrid models with convolution neural networks (CNN) have been developed, such as CNN-RNN, CNN-GRU, and CNN-LSTM. These approaches are used to explore how these models can automatically identify and diagnose faults in real-time, leading to faster and more effective fault detection and removal with minimum losses. By leveraging AI technology, modern power grids can become more resilient, efficient, and sustainable, ultimately contributing to a cleaner and greener energy future.
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Aoun, Alain, Mehdi Adda, Adrian Ilinca, Mazen Ghandour, and Hussein Ibrahim. "Centralized vs. Decentralized Electric Grid Resilience Analysis Using Leontief’s Input–Output Model." Energies 17, no. 6 (2024): 1321. http://dx.doi.org/10.3390/en17061321.
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Escalating events such as extreme weather conditions, geopolitical incidents, acts of war, cyberattacks, and the intermittence of renewable energy resources pose substantial challenges to the functionality of global electric grids. Consequently, research on enhancing the resilience of electric grids has become increasingly crucial. Concurrently, the decentralization of electric grids, driven by a heightened integration of distributed energy resources (DERs) and the imperative for decarbonization, has brought about significant transformations in grid topologies. These changes can profoundly impact flexibility, operability, and reliability. However, there is a lack of research on the impact of DERs on the electric grid’s resilience, as well as a simple model to simulate the impact of any disturbance on the grid. Hence, to analyze the electric grid’s resilience, this study employs an extrapolation of Leontief’s input–output (IO) model, originally designed to study ripple effects in economic sectors. Nodes are treated as industries, and power transmission between nodes is considered as the relationship between industries. Our research compares operability changes in centralized, partially decentralized, and fully decentralized grids under identical fault conditions. Using grid inoperability as a key performance indicator (KPI), this study tests the three grid configurations under two fault scenarios. The results confirm the efficacy of decentralization in enhancing the resilience and security of electric grids.
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SERVICE, TRAVIS, and DANIEL TAURITZ. "INCREASING INFRASTRUCTURE RESILIENCE THROUGH COMPETITIVE COEVOLUTION." New Mathematics and Natural Computation 05, no. 02 (2009): 441–57. http://dx.doi.org/10.1142/s1793005709001416.
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The world is increasingly dependent on critical infrastructures such as the electric power grid, water, gas and oil transport systems. Due to this increasing dependence and inadequate infrastructure expansion, these systems are becoming increasingly stressed. These additional stresses leave these systems less resilient to external faults, both accidental and malicious than ever before. As a result of this increased vulnerability, many critical infrastructures are becoming susceptible to cascading failures, where an initial fault caused by an external force may induce a domino-effect of further component failures. An important implication is that traditional infrastructure risk analysis methods, often relying on Monte Carlo sampling of fault scenarios, are no longer sufficient. Instead, systematic analysis based on worst-case attacks by intelligent adversaries is essential. This paper describes a coevolutionary methodology to simultaneously discover low-effort high-impact faults and corresponding means of hardening infrastructures against them. We empirically validate our methodology through an electric power transmission system case study.
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Alao, Agboola Benjamin, Olatunji Matthew Adeyanju, Manohar Chamana, Stephen Bayne, and Argenis Bilbao. "Optimized Universal Droop Control Framework for Enhancing Stability and Resilience in Renewable-Dense Power Grids." Electronics 14, no. 11 (2025): 2149. https://doi.org/10.3390/electronics14112149.
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High penetration of green energy sources presents substantial challenges to grid stability and resilience, primarily due to inherent voltage and frequency variability, which worsens during critical events. This study proposes an integrated framework for stability and resilience enhancement in renewable-dense power grids by designing optimized universal droop controllers (UDCs) tailored for grid-forming operations under high-impact contingencies. The UDC incorporates fault localization functionality via grid-forming inverters embedded with phasor measuring capabilities (phase voltage magnitude and angle) to facilitate real-time fault detection and response, thus augmenting operational reliability. Leveraging integrated solution environments, the developed framework employs numerical optimization routines for resource allocation, load prioritization, economic dispatch of distributed energy resources (DERs), and adaptive network reconfiguration under constrained conditions and during critical events that may necessitate decentralized network configurations in the wake of main grid failures. Validation conducted on the IEEE 123-node distribution network indicates that the optimized UDC framework achieves superior voltage and frequency regulation compared to conventional droop-based methods, ensuring optimal resource distribution and sustained load support.
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P. S. Patil. "Mathematical Analysis of Novel Method to Solve Protection Issues Pertaining To Solar PV Integration." Panamerican Mathematical Journal 34, no. 4 (2024): 159–77. http://dx.doi.org/10.52783/pmj.v34.i4.1876.
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The integration of Solar Photovoltaic (PV) systems into modern power grids presents protection challenges, such as voltage fluctuations, fault detection complexities, and bidirectional power flow issues. These challenges compromise grid reliability, requiring advanced methods to address them. This study introduces a novel mathematical analysis approach to solve protection issues in solar PV integration, focusing on developing an adaptive protection scheme using advanced mathematical models. The proposed approach utilizes differential equations, optimization techniques, and machine learning algorithms to create dynamic protection settings responsive to varying grid conditions. The method integrates fault detection and classification algorithms based on wavelet transform and support vector machines (SVM), allowing for rapid and accurate identification of fault types and locations. The findings demonstrate that the proposed adaptive protection scheme significantly enhances fault detection accuracy, reducing false trip rates by over 30% compared to conventional protection systems. Moreover, the method efficiently distinguishes between transient and permanent faults, ensuring swift isolation and minimizing disruption to solar PV operations. The developed model also exhibits robustness in handling variations in solar irradiance and load fluctuations, making it suitable for real-world grid applications. This research provides a substantial contribution to the field of solar PV integration by offering a mathematically grounded, adaptive protection solution that ensures improved reliability and resilience in power systems. The proposed method paves the way for the development of more advanced protection schemes, ensuring the seamless integration of renewable energy sources into modern grids while maintaining system stability and security.
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Tunde, Adejumo Wahid, Emmanuel M. Eronu, and Babawale B. Folajinmi. "Optimized ANN-Based Methodology for Fault Detection and Localization in Power Transmission Networks." Journal of Engineering Research and Reports 27, no. 1 (2025): 140–54. https://doi.org/10.9734/jerr/2025/v27i11374.
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Transmission lines are integral to transporting electrical power from generation sites to consumers. Transmission lines are subject to various faults that disrupt service and threaten system integrity. Fault analysis (identification, classification, and localization) is essential to minimize downtime and operational costs. Improved fault control raises grid dependability, decreases outages, and optimizes operations, promoting renewable integration and cost savings. It enhances safety, power quality, and resilience while facilitating innovative grid modernization and scalability for future demands. Such developments strengthen consumer trust and lead to more sustainable, efficient, and resilient power systems. This research employs Artificial Neural Networks (ANN) to enhance fault detection on high-voltage transmission lines. Simulations were conducted on a 132 kV, 50 Hz, 100 km transmission line model using MATLAB/Simulink, generating data from various fault scenarios. The ANNs, trained with these datasets, effectively and accurately analyzed the faults. The most effective neural network architecture was identified, assuring dependable operation in various fault scenarios and showcasing a strong strategy to enhance power transmission efficiency. Configuration 2 achieved the best fault identification accuracy of 97.99%, demonstrating the system's low error rate in accurately detecting the flaw. Fault classification with Configuration 1 attained a 95.65% accuracy rate. This indicates that the system can effectively categorize various fault types. The fault location was at an accuracy of 94.51% using Configuration 1.
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Pawar, Dr Suvarna, Sahil Gannarpwar, Aman War, Rahul Khandvi, and Aditya Talokar. "An Intelligent Approach of Faults Detection and Location Methods for Power Transmission Line." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 07, no. 11 (2023): 1–11. http://dx.doi.org/10.55041/ijsrem27007.
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This paper presents an innovative approach for the simultaneous detection and localization of electric faults in transmission lines through machine learning (ML) techniques. By harnessing supervised learning algorithms, the system is trained on a comprehensive dataset comprising normal and fault scenarios. Extracting relevant features from critical parameters such as voltage, current, and phase angle, the ML model is equipped to discern between fault and non- fault states. Additionally, a localization algorithm is incorporated to pinpoint the exact location of the identified faults. Real-time monitoring facilitates rapid response, minimizing downtime and enhancing the overall reliability of the power grid. The proposed ML- based framework not only advances fault detection accuracy but also provides a precise spatial assessment, contributing to the optimization of maintenance efforts and the resilience of the transmission infrastructure. Key Words: Decision tree, Electric Faults, Transmission line, Fault Detection, Feature Extraction, Reliability.
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Jia, Lu. "Application of Anti-Voltage Sag Technology and Traveling Wave Fault Location in Oilfield Power Grids." Journal of Innovation and Development 11, no. 1 (2025): 64–68. https://doi.org/10.54097/839h0346.
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With the expansion of power system scales and increasing load complexity, voltage sags ("flicker") and line faults pose severe challenges to industrial production and grid security. Anti-voltage sag technology effectively mitigates equipment shutdowns caused by voltage fluctuations through core measures such as contactor delayed release protection and UPS/DC-BANK systems, ensuring power supply reliability in continuous production scenarios like oilfields. Traveling wave fault location technology leverages the high-frequency transient characteristics of fault-induced traveling waves to achieve rapid and precise fault localization in transmission and distribution lines, significantly reducing fault diagnosis time. This paper systematically analyzes the principles and typical applications of these two technologies: anti-voltage sag technology reduces unplanned downtime risks in oilfield distribution networks through multi-level protections (e.g., low-voltage motor retrofitting, high-voltage equipment delayed tripping, and system-level fast bus transfer devices); traveling wave fault location combines dual-terminal methods, wavelet transforms, and intelligent algorithms to achieve sub-1% localization errors in complex scenarios such as HVDC lines and multi-branch cable networks. Research demonstrates that these technologies enhance grid resilience from "prevention to recovery" perspectives. Future advancements in intelligent coordination (e.g., digital twins and edge computing) could further optimize the efficiency of power system fault management, providing critical technical support for smart grid development.
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Mar, Adriana, Pedro Pereira, and João Martins. "Energy Community Flexibility Solutions to Improve Users’ Wellbeing." Energies 14, no. 12 (2021): 3403. http://dx.doi.org/10.3390/en14123403.
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Energy communities, mostly microgrid based, are a key stakeholder of modern electrical power grids. Operating a microgrid based energy community is a challenging topic due to the involved uncertainties, complexities and often conflicting objectives. The aim of this paper is to present a novel methodology demonstrating that energy community flexibility can contribute to each community member’s wellbeing when a grid fault occurs. A three-house energy community will be modelled considering as consumption sources non-controllable and controllable devices in each house. As power supply sources, PV systems installed in a community’s houses are considered, as well as the power obtained from main grid. Each house’s flexibility inside the community will be studied to improve the management of loads during a fault occurrence. Moreover, three different scenarios will be considered with different available power in the community. With these simulations, it was possible to understand that houses’ energy flexibility can be used under a fault situation, either to maintain the users’ wellbeing or to change the energy flow. Furthermore, energy flexibility can be used to create better energy price markets, to improve the resilience of the grid, or even to consider electrical vehicles’ connection to a community’s grid.
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Khaleefah, Shihab Hamad, Salama A. Mostafa, Saraswathy Shamini Gunasekaran, Umar Farooq Khattak, Siti Salwani Yaacob, and Alde Alanda. "A Deep Learning-based Fault Detection and Classification in Smart Electrical Power Transmission System." JOIV : International Journal on Informatics Visualization 8, no. 2 (2024): 812. http://dx.doi.org/10.62527/joiv.8.2.2701.
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Progressively, the energy demands and responsibilities to control the demands have expanded dramatically. Subsequently, various solutions have been introduced, including producing high-capacity electrical generating power plants, and applying the grid concept to synchronize the electrical power plants in geographically scattered grids. Electrical Power Transmission Networks (EPTN) are made of many complex, dynamic, and interrelated components. The transmission lines are essential components of the EPTN, and their fundamental duty is to transport electricity from the source area to the distribution network. These components, among others, are continually prone to electrical disturbance or failure. Hence, the EPTN required fault detection and activation of protective mechanisms in the shortest time possible to preserve stability. This research focuses on using a deep learning approach for early fault detection to improve the stability of the EPTN. Early fault detection swiftly identifies and isolates faults, preventing cascading failures and enabling rapid corrective actions. This ensures the resilience and reliability of the grid, optimizing its operation even in the face of disruptions. The design of the deep learning approach comprises a long-term and short-term memory (LSTM) model. The LSTM model is trained on an electrical fault detection dataset that contains three-phase currents and voltages at one end serving as inputs and fault detection as outputs. The proposed LSTM model has attained an accuracy of 99.65 percent with an error rate of just 1.17 percent and outperforms neural network (NN) and convolutional neural network (CNN) models.
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Manu K P. "Embedded system design for fault detection in power distribution networks." World Journal of Advanced Research and Reviews 13, no. 2 (2022): 625–32. https://doi.org/10.30574/wjarr.2022.13.2.0069.
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Power distribution networks are critical for ensuring a stable and uninterrupted supply of electricity. However, faults in these networks can lead to severe disruptions, increased maintenance costs, and potential safety hazards. Rapid and accurate fault detection is essential to minimize downtime, enhance grid reliability, and prevent large-scale power failures. This research paper presents the design and implementation of an embedded system for real-time fault detection in power distribution networks. The proposed system integrates advanced sensing technologies, microcontrollers, and communication modules to detect, classify, and localize faults efficiently. The system employs voltage and current sensors to monitor network conditions and utilizes wireless communication protocols to transmit fault data to a central monitoring unit. Additionally, machine learning algorithms are implemented for predictive maintenance, enabling early fault prediction and proactive intervention. Performance evaluation is conducted through experimental simulations and real-time testing, demonstrating the system’s capability to enhance fault detection accuracy and response speed. The paper includes comprehensive analyses of system performance, fault classification accuracy, and efficiency improvements through figures, tables, and bar charts. The findings suggest that integrating embedded systems with intelligent fault detection techniques can significantly improve the resilience and efficiency of modern power distribution networks.
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Salehimehr, Sirus, Seyed Mahdi Miraftabzadeh, and Morris Brenna. "A Novel Machine Learning-Based Approach for Fault Detection and Location in Low-Voltage DC Microgrids." Sustainability 16, no. 7 (2024): 2821. http://dx.doi.org/10.3390/su16072821.
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DC microgrids have gained significant attention in recent years due to their potential to enhance energy efficiency, integrate renewable energy sources, and improve the resilience of power distribution systems. However, the reliable operation of DC microgrids relies on the early detection and location of faults to ensure an uninterrupted power supply. This paper aims to develop fast and reliable fault detection and location mechanisms for DC microgrids, thereby enhancing operational efficiency, minimizing environmental impact, and contributing to resource conservation and sustainability goals. The fault detection method is based on compressed sensing (CS) and Regression Tree (RT) techniques. Besides, an accurate fault location method using the feature matrix and long short-term memory (LSTM) model combination has been provided. To implement the proposed fault detection and location method, a DC microgrid equipped with photovoltaic (PV) panels, the vehicle-to-grid (V2G) charging station, and a hybrid energy storage system (ESS) are used. The simulation results represent the proposed methods’ superiority over the recent studies. The fault occurrence in the studied DC microgrid is detected in 1 ms, and the proposed fault location method locates the fault with an accuracy of more than 93%. The presented techniques enhance DC microgrid reliability while conserving renewable resources, vital to promoting a greener and more sustainable power grid.
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Raghad Hameed Ahmed and Ahmed Said Nouri. "Enhanced Transient Stability in Power Systems via Intelligent Control of SVCs Using Neural Networks." Electrical Engineering Technical Journal 2, no. 2 (2025): 17–24. https://doi.org/10.51173/eetj.v2i2.23.
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This paper investigates the application of Static VAR Compensators (SVCs) with neural network control to enhance power system grid stability, particularly in multi-source energy systems. SVCs, as Flexible alternating current Transmission Systems (FACTS) devices, are crucial for reactive power compensation and voltage regulation. The study models and simulates an SVC controlled by a neural network in MATLAB/Simulink, assessing its performance under three-phase fault conditions. The fault a 3-phase to-ground short circuit fault is introduced at location in close proximity to the wind energy sults demonstrate that the proposed control scheme effectively reduces system oscillations and improves dynamic response, leading to faster fault recovery and enhanced overall grid stability. The superior dynamic performance of the SVC-based neural network controller confirms its potential for improving power system resilience.
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Judy Lhyn Porlaje Sarmiento, Jam Cyrex De Villa Delfino, and Edwin Romeroso Arboleda. "Machine learning advances in transmission line fault detection: A literature review." International Journal of Science and Research Archive 12, no. 1 (2024): 2880–87. http://dx.doi.org/10.30574/ijsra.2024.12.1.1150.
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Fault detection in transmission lines plays a role in maintaining the dependability and steadiness of power networks. Traditional methods for identifying faults often struggle to handle the diverse nature of real world fault situations. Machine learning (ML) algorithms offer a data centered approach that can adjust and learn from datasets potentially overcoming the limitations of traditional approaches. This document presents a review of progress in using ML for detecting faults in transmission lines. By drawing insights from a variety of studies we explore how ML algorithms have evolved in fault detection, including techniques like networks, recurrent neural networks featuring Long Short Term Memory and convolutional neural networks. We delve into the spectrum of applications where ML is used for fault detection across fault scenarios and operational settings. Additionally we discuss the obstacles and prospects linked to putting ML based fault detection systems into practice such as challenges with data quality, model interpretability and integration with existing grid monitoring systems. Lastly we outline future research paths focused on pushing forward the boundaries of fault detection, in power transmission systems through approaches and collaborative endeavors involving academia, industry players and policymakers. In general, this review highlights how machine learning has the power to revolutionize fault detection methods enhancing the resilience and dependability of power systems.
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Yang, Mingshuo, Lixia Zhang, Xiaoying Song, Wei Kang, and Zhongjian Kang. "A Transient Control Strategy for Grid-Forming Photovoltaic Systems Based on Dynamic Virtual Impedance and RBF Neural Networks." Electronics 14, no. 4 (2025): 785. https://doi.org/10.3390/electronics14040785.
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This paper proposes a grid-forming (GFM) photovoltaic system transient control strategy based on the combination of dynamic virtual impedance and the radial basis function (RBF) algorithm. First, the virtual synchronous generator (VSG) model is analyzed to understand how virtual impedance affects current surges and system stability during faults. By using dynamic virtual impedance throughout the fault, the strategy suppresses current spikes and improves stability. The RBF neural network dynamically adjusts virtual inertia and damping coefficients to optimize transient power-angle characteristics and speed up recovery during fault restoration. Simulation results show that the strategy reduces transient current surges, improves angle recovery, and boosts system stability during voltage sag. This approach offers an effective solution for low-voltage ride-through (LVRT) and transient control in photovoltaic grid-connected systems, ensuring the resilience and stability of renewable energy integration into the grid.
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Soualah, Hassen, Gurvan Jodin, Roman Le Goff Latimier, and Hamid Ben Ahmed. "Energy Not Exchanged: A Metric to Quantify Energy Resilience in Smart Grids." Sustainability 15, no. 3 (2023): 2596. http://dx.doi.org/10.3390/su15032596.
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In high-impact, low-probability (HILP) events, resilience is defined as the ability of a system to return to a normal operating state after a failure. The generalization of information technologies and distributed renewable production is transforming the power grid into the so-called smart grid, thus allowing for new mitigation methods to address failures. After illustrating the limits of currently existing metrics, this paper proposes a method to quantify the resilience of smart grids during physical line faults while identifying the most impactful failures. For this purpose, a new resilience metric is defined in order to quantify Energy Not Exchanged (ENE). The calculation of this metric in a power grid via the optimal power flow (OPF) serves, therefore, to quantify the extreme resilience of the grid. In addition, various mitigation strategies, which enable maintaining a high level of resilience, despite the presence of failure, are simulated and then compared to one another (tie switch and microgrid formation).
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Zhang, Geng, Chenxu Liu, Hao Jiang, and Jiye Wang. "Fault Recovery Methods for a Converged System Comprised of Power Grids, Transportation Networks and Information Networks." Electronics 12, no. 21 (2023): 4508. http://dx.doi.org/10.3390/electronics12214508.
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Recently, triple-network convergence systems (TNCS) have emerged from the deep integration of the power grid, transportation networks, and information networks. Fault recovery research in the TNCS is important since this system’s complexity and interactivity can expand the fault’s scale and increase the fault’s impact. Currently, fault recovery focuses primarily on single power grids and cyber–physical systems, but there are certain shortcomings, such as ignoring uncertainties, including generator start-up failures and the occurrence of new faults during recovery, energy supply–demand imbalances leading to system security issues, and communication delays caused by network attacks. In this study, we propose a recovery method based on the improved twin-delayed deep deterministic algorithm (TD3), factoring in the shortcomings of the existing research. Specifically, we establish a TNCS model to analyze interaction mechanisms and design a state matrix to represent the uncertainty changes in the TNCS, a negative reward to reflect the impact of unit start-up failures, a special reward to reflect the impact of communication delay, and an improved actor network update mechanism. Experimental results show that our method obtains the optimal recovery decisions, maximizes restoration benefits in power grid failure scenarios, and demonstrates a strong resilience against communication delays caused by DoS attacks.
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Rivera Torres, Pedro Juan, Carlos Gershenson García, María Fernanda Sánchez Puig, and Samir Kanaan Izquierdo. "Reinforcement Learning with Probabilistic Boolean Network Models of Smart Grid Devices." Complexity 2022 (June 13, 2022): 1–15. http://dx.doi.org/10.1155/2022/3652441.
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The area of smart power grids needs to constantly improve its efficiency and resilience, to provide high quality electrical power in a resilient grid, while managing faults and avoiding failures. Achieving this requires high component reliability, adequate maintenance, and a studied failure occurrence. Correct system operation involves those activities and novel methodologies to detect, classify, and isolate faults and failures and model and simulate processes with predictive algorithms and analytics (using data analysis and asset condition to plan and perform activities). In this paper, we showcase the application of a complex-adaptive, self-organizing modeling method, and Probabilistic Boolean Networks (PBNs), as a way towards the understanding of the dynamics of smart grid devices, and to model and characterize their behavior. This work demonstrates that PBNs are equivalent to the standard Reinforcement Learning Cycle, in which the agent/model has an interaction with its environment and receives feedback from it in the form of a reward signal. Different reward structures were created to characterize preferred behavior. This information can be used to guide the PBN to avoid fault conditions and failures.
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Islam, Saif Ul, and Soobae Kim. "Design of an Optimal Adoptive Fault Ride through Scheme for Overcurrent Protection of Grid-Forming Inverter-Based Resources under Symmetrical Faults." Sustainability 15, no. 8 (2023): 6705. http://dx.doi.org/10.3390/su15086705.
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As the integration of inverter-based resources (IBRs) is rapidly increasing in regard to the existing power system, switching from grid-following (GFL) to grid-forming (GFM) inverter control is the solution to maintain grid resilience. However, additional overcurrent protection, especially during fault transition, is required due to limited overcurrent capability and the high magnitude of spikes during fault recovery in IBRs, specifically in the GFM control mode. Furthermore, the power system stability should not be compromised by the employment of additional fault ride through (FRT) schemes. This article presents the design and implementation of an adoptive fault ride through (FRT) scheme for grid-forming inverters under symmetrical fault conditions. The proposed adoptive FRT scheme is comprised of two cascaded power electronic-based circuits, i.e., fault current ride through and a spikes reactor. This adoptive FRT scheme optimizes the fault variables during the fault time and suppresses the fault clearing spikes, without affecting system stability. A three-bus inverter-based grid-forming model is used in MATLAB/Simulink for the implementation of the proposed scheme. Further, a conventionally used FRT scheme, which includes fault current reactors, is simulated in the same test environment for justification of the proposed adoptive scheme. The adoptive FRT scheme is simulated for both time domain and frequency domain to analyze the response of harmonic distortion with the suppression of the fault current. Moreover, the proposed scheme is also simulated under the GFL mode of IBRs to justify the reliability of the scheme. The overall simulation results and performance evaluation indices authenticate the optimal, fault tolerant, harmonic, and spike-free behavior of the proposed scheme at both the AC and DC side of the grid-forming inverters.
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Rana, Sohel. "AI-DRIVEN FAULT DETECTION AND PREDICTIVE MAINTENANCE IN ELECTRICAL POWER SYSTEMS: A SYSTEMATIC REVIEW OF DATA-DRIVEN APPROACHES, DIGITAL TWINS, AND SELF-HEALING GRIDS." American Journal of Advanced Technology and Engineering Solutions 1, no. 01 (2025): 258–89. https://doi.org/10.63125/4p25x993.
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The increasing complexity of electrical power systems necessitates advanced fault detection and predictive maintenance strategies to enhance operational efficiency and grid reliability. Traditional maintenance approaches, such as reactive and preventive maintenance, have proven insufficient in mitigating unplanned outages and optimizing asset utilization. Recent advancements in artificial intelligence (AI) have introduced data-driven solutions that significantly improve fault classification, failure prediction, and automated recovery processes. This study conducts a systematic review of 180 high-quality peer-reviewed articles, following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines to ensure a rigorous and transparent research methodology. The findings reveal that AI-driven predictive maintenance methods, including machine learning, deep learning, digital twin technology, IoT-enabled sensor networks, and self-healing grids, have outperformed traditional fault detection techniques in terms of accuracy, adaptability, and cost-effectiveness. AI-based fault detection models achieve an average accuracy of 85% to 95%, reducing false alarms by 50% and minimizing power restoration times by up to 60%. The integration of IoT sensors with real-time analytics has improved anomaly detection rates by 28%, while digital twin technology has enhanced predictive maintenance efficiency, reducing unplanned outages by 35%. Additionally, self-healing grid mechanisms, powered by reinforcement learning algorithms, have demonstrated the ability to autonomously isolate faults and reconfigure energy distribution, preventing nearly 45% of potential service disruptions. Despite these advancements, challenges such as the black-box nature of deep learning models, cybersecurity vulnerabilities, and interoperability with legacy systems continue to pose barriers to large-scale AI adoption. The study highlights the need for explainable AI frameworks, standardized data governance policies, and enhanced cybersecurity measures to ensure the sustainable deployment of AI in power grid management. The findings provide valuable insights for researchers, utility companies, and policymakers seeking to enhance the resilience and efficiency of modern electrical power systems through AI-driven fault detection and predictive maintenance strategies.
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Ge, Xiaojun, Minyu Chen, Yaming Mao, Lizhen Lu, Xing He, and Qian Ai. "A Novel Fault Location Algorithm in Distribution Network Based on Power-on Signal and Power-off Signal." Journal of Physics: Conference Series 2031, no. 1 (2021): 012033. http://dx.doi.org/10.1088/1742-6596/2031/1/012033.
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Abstract In recent years, with the continuous promotion of clean and low-carbon energy strategy, power system’s operation mode has been transformed gradually. Consequently, the extended time of fault location when it occurs will jeopardize the fault restoration of distribution system and reduce the grid resilience. Hence, this paper proposes a novel fault location algorithm based on the combination of power-on signal and power-off signal, which can improve the efficiency of the fault location. First, the principle of numbering node is presented, making it convenient for computer to identify the relationship between device in the distribution network. Then, some assumptions and principles of the algorithm are shown, together with the termination condition of the proposed algorithm. Finally, case study involving an actual distribution network is carried out. Results show that the proposed algorithm is able to achieve accurate fault location meanwhile improve the efficiency of fault location.
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Rahman, Khaliqur, Jun Hashimoto, Kunio Koseki, Dai Orihara, and Taha Selim Ustun. "Coordinated Control of Grid-Forming Inverters for Adaptive Harmonic Mitigation and Dynamic Overcurrent Control." Electronics 14, no. 14 (2025): 2793. https://doi.org/10.3390/electronics14142793.
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This paper proposes a coordinated control strategy for grid-forming inverters (GFMs) to address two critical challenges in evolving power systems. These are the active harmonic mitigation under nonlinear loading conditions and dynamic overcurrent control during grid disturbances. The proposed framework integrates a shunt active filter (SAF) mechanism within the GFM control structure to achieve a real-time suppression of harmonic distortions from the inverter and grid currents. In parallel, a virtual impedance-based dynamic current limiting strategy is incorporated to constrain fault current magnitudes, ensuring the protection of power electronic components and maintaining system stability. The SAF operates in a current-injection mode aligned with harmonic components, derived via instantaneous reference frame transformations and selective harmonic extraction. The virtual impedance control (VIC) dynamically modulates the inverter’s output impedance profile based on grid conditions, enabling adaptive response during fault transients to limit overcurrent stress. A detailed analysis is performed for the coordinated control of the grid-forming inverter. Supported by simulations and analytical methods, the approach ensures system stability while addressing overcurrent limitations and active harmonic filtering under nonlinear load conditions. This establishes a viable solution for the next-generation inverter-dominated power systems where reliability, power quality, and fault resilience are paramount.
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Chukwudi Josiah, Ojimba,, Okpo, Ekom Enefiok, and Attah, Imoh Christopher. "Fault Resistance and Adaptive Analysis of Induction Motors under Voltage Variations in Industrial Applications." Journal of Engineering Research and Reports 26, no. 12 (2024): 231–48. https://doi.org/10.9734/jerr/2024/v26i121354.
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The resilience of three-phase induction motors under fault conditions is critical for the stability and reliability of power systems, especially in industrial and commercial settings where voltage disturbances are common. Induction motors are highly susceptible to under-voltage faults caused by grid instability, load imbalances, or transient faults, which can lead to severe operational issues, reduced motor life, and potential system failures if not properly managed. Understanding how induction motors respond to varying levels of under-voltage disturbances provides essential insights into their adaptive limits and helps identify the thresholds at which protective measures become necessary. This research underscores the importance of integrating advanced fault tolerance designs and adaptive mechanisms to improve system reliability in voltage-sensitive industrial applications. The specification of induction motor used in this analysis are, 7.5Kw rated power, line voltage of 400 volts, and rated speed of 1440 RPM. MATLAB/Simulink simulations software were utilized for this analytical investigation, induction motors were subjected to controlled under-voltage variations applied to single-phase and three-phase systems. The study specifically analyzed the rotor speed, electromagnetic torque, and stator current to evaluate how the motors adapted to faults and how these parameters behaved during fault recovery. Faults were simulated with voltage reductions of 20, 40, and 60%, examining both motor resilience and system recovery upon fault clearance. The findings revealed that at moderate under-voltage levels (20% and 40%), the induction motors adapted effectively by increasing current to maintain adequate performance, with system parameters returning to normal post-fault. However, under extreme 60% under-voltage conditions, the motors experienced significant current surges, causing thermal stress and a failure to return to nominal operating conditions after fault clearance, highlighting the motor’s adaptive limit under severe fault scenarios. Additionally, Ohm’s law analysis demonstrated how voltage drops and compensatory current increases temporarily reduced effective resistance, facilitating fault resilience under moderate conditions was also analyzed. The study shows that, as the voltage reduce, current increase which cause the resistance to reduce. This study provides insight into the adaptive limits of induction motors in power systems, offering guidelines for implementing effective protection mechanisms in power networks prone to voltage instabilities. The results support improved fault management strategies and motor protection in industrial applications, particularly under varying fault conditions.
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Koo, Geun Wan, DongMyoung Joo, and Byoung Kuk Lee. "Enhanced Threshold Point Calculation Algorithm for Switch Fault Diagnosis in Grid Connected 3-Phase AC–DC PWM Converters." Energies 12, no. 10 (2019): 1979. http://dx.doi.org/10.3390/en12101979.
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The resilience of systems with alternating current (AC)–direct current (DC) converters has been investigated with the aim of improving switch fault diagnosis. To satisfy this aim, this paper proposes a switch fault diagnosis algorithm for three-phase AC–DC converters. The proposed algorithm operates using the phase current instead of the average current to reduce the calculation time required for fault switch detection. Moreover, a threshold point calculation method is derived using a theoretical analysis, which was lacking in previous research. Using the calculated threshold point, a switch fault diagnosis algorithm is obtained to detect faults independent of the load condition. Using the proposed algorithm, switch faults can be detected within 4 ms, which is the recommended value for uninterruptible power supply (UPS). The theoretical analysis, the operating principle, and the experimental results on a 3-kW grid-tied AC–DC converter test-bed are presented herein, which verify the performance of the proposed algorithm.
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Palau-Mayo, Anna, Mikel de Prada, and José Luís Domínguez-García. "Towards more resilient electrical networks in urban areas." E3S Web of Conferences 61 (2018): 00015. http://dx.doi.org/10.1051/e3sconf/20186100015.
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The requirement of system decarbonisation fixed by the EU 2050 plan is leading to an increased establishment of renewable energy sources. Additionally, the emergence of power electronics and ICT technologies has played a decisive role towards a novel distribution electric grid allowing new monitoring, operation and control. In parallel to the energetic transition, an increasing occurrence of extreme weather events and a reinforced concern on climate change leads to the concept of resilience, which is the capacity to adapt and recover from disruptive events in a coordinated procedure. After a fault event, assuming the objective of the system operator is to minimize the load unsupplied, the present study aims at outlining an early research state on the concept of self-healing through the development of a power flow optimization algorithm within a meshed network. Moreover, the effects of integrating Distributed Energy Resources (DER) in order to increase distribution grid resilience as well as to ensure and secure power supply to the system leads to the clusterization of the power system. With controllable technologies, the on-outaged areas are able to disconnect from the main grid, creating islanded microgrids (MGs) which can work autonomously and consequently, increase grid resilience.
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De La Cruz, Jorge, Eduardo Gómez-Luna, Majid Ali, Juan C. Vasquez, and Josep M. Guerrero. "Fault Location for Distribution Smart Grids: Literature Overview, Challenges, Solutions, and Future Trends." Energies 16, no. 5 (2023): 2280. http://dx.doi.org/10.3390/en16052280.
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Thanks to smart grids, more intelligent devices may now be integrated into the electric grid, which increases the robustness and resilience of the system. The integration of distributed energy resources is expected to require extensive use of communication systems as well as a variety of interconnected technologies for monitoring, protection, and control. The fault location and diagnosis are essential for the security and well-coordinated operation of these systems since there is also greater risk and different paths for a fault or contingency in the system. Considering smart distribution systems, microgrids, and smart automation substations, a full investigation of fault location in SGs over the distribution domain is still not enough, and this study proposes to analyze the fault location issues and common types of power failures in most of their physical components and communication infrastructure. In addition, we explore several fault location techniques in the smart grid’s distribution sector as well as fault location methods recommended to improve resilience, which will aid readers in choosing methods for their own research. Finally, conclusions are given after discussing the trends in fault location and detection techniques.
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İnci, Mustafa, Özgür Çelik, Abderezak Lashab, Kamil Çağatay Bayındır, Juan C. Vasquez, and Josep M. Guerrero. "Power System Integration of Electric Vehicles: A Review on Impacts and Contributions to the Smart Grid." Applied Sciences 14, no. 6 (2024): 2246. http://dx.doi.org/10.3390/app14062246.
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In recent years, electric vehicles (EVs) have become increasingly popular, bringing about fundamental shifts in transportation to reduce greenhouse effects and accelerate progress toward decarbonization. The role of EVs has also experienced a paradigm shift for future energy networks as an active player in the form of vehicle-to-grid, grid-to-vehicle, and vehicle-to-vehicle technologies. EVs spend a significant part of the day parked and have a remarkable potential to contribute to energy sustainability as backup power units. In this way, EVs can be connected to the grid as stationary power units, providing a range of services to the power grid to increase its reliability and resilience. The available systems show that EVs can be used as alternative energy sources for various network systems like smart grids, microgrids, and virtual power plants besides transportation. While the grid–EV connection offers various contributions, it also has some limitations and effects. In this context, the current study highlights the power system impacts and key contributions of EVs connected to smart grids. Regarding the power system impacts in case of EV integration into smart grids, the challenges and difficulties are categorized under the power system stability, voltage/current distortions, load profile, and power losses. Voltage/current distortions like sags, unbalances, harmonics, and supraharmonics are also detailed in the study. Subsequently, the key contributions to the smart grid in terms of energy management, grid-quality support, grid balancing, and socio-economic impacts are explained. In the energy management part, issues such as power flow, load balancing, and renewable energy integration are elaborated. Then, the fault ride-through capability, reactive power compensation, harmonic mitigation, and grid loss reduction are presented to provide information on power quality enhancement. Lastly, the socio-economic impacts in terms of employment, net billing fees, integration with renewable energy sources, and environmental effects are elucidated in the present study.
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Purushottam Kumar Maurya. "Self-Healing Grids: AI Techniques for Automatic Restoration after Outages." Power System Technology 48, no. 1 (2024): 494–510. http://dx.doi.org/10.52783/pst.302.
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The reliability and resilience of power grids are paramount for sustaining modern society's energy demands. However, power outages resulting from natural disasters, equipment failures, or human errors remain persistent challenges. Traditional approaches to power grid restoration, relying heavily on manual intervention, often lead to delays and inefficiencies in restoring services. [1],[2] Recent advancements in artificial intelligence (AI) have spurred the development of self-healing grids capable of autonomously detecting, diagnosing, and restoring power after outages. This paper presents a comprehensive overview of AI techniques employed in self-healing grids and their applications in automatic restoration following outages. The traditional methods of power grid restoration, characterized by manual inspection and decision-making processes, are discussed, highlighting their limitations and challenges. Subsequently, the paper delves into various AI techniques employed in self-healing grids. Machine learning algorithms, such as supervised and unsupervised learning, are utilized for outage detection by analyzing historical data to identify patterns indicative of faults or anomalies. Fault diagnosis is facilitated through the application of Bayesian networks, neural networks, and fuzzy logic systems, enabling operators to accurately identify the root cause of outages and prioritize restoration efforts. Optimization algorithms, including evolutionary algorithms and reinforcement learning, play a crucial role in planning and coordinating restoration efforts to minimize downtime and maximize efficiency. The benefits of self-healing grids, including improved reliability, reduced downtime, and enhanced safety, are discussed alongside the challenges posed by data quality, scalability, and cybersecurity concerns. Finally, the paper outlines future directions, emphasizing advancements in AI techniques, integration with emerging technologies, and the importance of standardization and regulatory frameworks for the future of power grid management. DOI: https://doi.org/10.52783/pst.302
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Zhang, Jing, Huilin Cheng, Peng Yang, Bingyan Zhang, Shiqi Zhang, and Zhigang Lu. "Comprehensive Evaluation Index System and Application of Low-Carbon Resilience of Power Grid Containing Phase-Shifting Transformer under Ice Disaster." Processes 11, no. 9 (2023): 2633. http://dx.doi.org/10.3390/pr11092633.
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In view of the high impact of extreme disasters, this paper comprehensively evaluates power grid performance from a new low-carbon toughness perspective. First, considering the increase in carbon emissions and the recovery time of carbon emissions, low-carbon resilience indicators are proposed. At the same time, considering the power-regulation effect of the phase-shifter transformer, the fault and response model of a power grid under an ice disaster is established, and then, a comprehensive evaluation index system of low-carbon toughness of the power grid is constructed. The weight determination is carried out using the fuzzy analytic hierarchy process-entropy-based weight method, while the fuzzy comprehensive evaluation center of gravity method is used to evaluate the power grid comprehensively. Finally, examples are presented to verify the feasibility of the proposed method, emphasizing its potential for evaluating the comprehensive performance of low-carbon and toughness of the power grid in the future.
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Babu,, M. A. Suresh. "Applications of Artificial Intelligence in (Machine Learning /Deep learning) Smart Grid." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 03 (2025): 1–9. https://doi.org/10.55041/ijsrem42202.
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Artificial Intelligence (AI), particularly Machine Learning (ML) and Deep Learning (DL), plays a significant role in enhancing the efficiency, reliability, and sustainability of smart grids. One of its key applications is load forecasting and demand response, where Machine Learning models predict electricity demand based on historical consumption patterns, weather conditions, and economic factors - helping in real-time energy optimization. AI also enables renewable energy integration, grid fault detection and maintenance, energy theft detection, voltage and frequency stability control, AI predicts EV charging demand and prevents grid overload, Another breakthrough is automated grid control and self-healing networks, where AI enables self-healing smart grids that detect outages and reroute power automatically. Machine Learning models predict failures and adjust grid operations accordingly, enhancing grid resilience. Overall, AI-driven smart grids improve energy efficiency, reduce operational costs, and ensure a more reliable and sustainable power distribution system. This paper discusses a comprehensive review of AI-based modeling, an AI-enabled smart grid for demand forecasting that leverages machine learning (ML) and artificial intelligence (AI) techniques to predict electricity demand with high accuracy and efficiency. It integrates advanced data analytics with the grid's operational systems to enable better decision-making, enhance grid management, and improve energy efficiency. Keywords: Artificial Intelligence ・ Machine Learning ・ Deep Learning ・
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Alasali, Feras, Awni Itradat, Salah Abu Ghalyon, et al. "Smart Grid Resilience for Grid-Connected PV and Protection Systems under Cyber Threats." Smart Cities 7, no. 1 (2023): 51–77. http://dx.doi.org/10.3390/smartcities7010003.
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In recent years, the integration of Distributed Energy Resources (DERs) and communication networks has presented significant challenges to power system control and protection, primarily as a result of the emergence of smart grids and cyber threats. As the use of grid-connected solar Photovoltaic (PV) systems continues to increase with the use of intelligent PV inverters, the susceptibility of these systems to cyber attacks and their potential impact on grid stability emerges as a critical concern based on the inverter control models. This study explores the cyber-threat consequences of selectively targeting the components of PV systems, with a special focus on the inverter and Overcurrent Protection Relay (OCR). This research also evaluates the interconnectedness between these two components under different cyber-attack scenarios. A three-phase radial Electromagnetic Transients Program (EMTP) is employed for grid modeling and transient analysis under different cyber attacks. The findings of our analysis highlight the complex relationship between vulnerabilities in inverters and relays, emphasizing the consequential consequences of affecting one of the components on the other. In addition, this work aims to evaluate the impact of cyber attacks on the overall performance and stability of grid-connected PV systems. For example, in the attack on the PV inverters, the OCR failed to identify and eliminate the fault during a pulse signal attack with a short duration of 0.1 s. This resulted in considerable harmonic distortion and substantial power losses as a result of the protection system’s failure to recognize and respond to the irregular attack signal. Our study provides significant contributions to the understanding of cybersecurity in grid-connected solar PV systems. It highlights the importance of implementing improved protective measures and resilience techniques in response to the changing energy environment towards smart grids.
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Mar, Adriana, Pedro Pereira, and João F. Martins. "A Survey on Power Grid Faults and Their Origins: A Contribution to Improving Power Grid Resilience." Energies 12, no. 24 (2019): 4667. http://dx.doi.org/10.3390/en12244667.
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One of the most critical infrastructures in the world is electrical power grids (EPGs). New threats affecting EPGs, and their different consequences, are analyzed in this survey along with different approaches that can be taken to prevent or minimize those consequences, thus improving EPG resilience. The necessity for electrical power systems to become resilient to such events is becoming compelling; indeed, it is important to understand the origins and consequences of faults. This survey provides an analysis of different types of faults and their respective causes, showing which ones are more reported in the literature. As a result of the analysis performed, it was possible to identify four clusters concerning mitigation approaches, as well as to correlate them with the four different states of the electrical power system resilience curve.
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S, K. Kabilesh, Anto S. Vibin, M. S. Veshnu, Sai Teja Yeluvu, and Bharath Pullakomala. "LoRaWAN-Based Smart Transformer Monitoring and Control Using Machine Learning for Predictive Maintenance." Advancement of Signal Processing and its Applications 8, no. 1 (2025): 22–36. https://doi.org/10.5281/zenodo.15119031.
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<em>Effective monitoring and control of power transformers are crucial to maintaining the reliability and stability of electricity grids. This paper introduces a LoRaWAN-based transformer control and monitoring system with machine learning (ML) algorithms integrated for predictive maintenance and fault detection. The system utilizes low-power, long-range (LoRa) communication to transfer real-time transformer parameters such as voltage, current, temperature, oil level, humidity, and vibration to a cloud-based server. A Random Forest algorithm is applied to sensor data analysis, anomaly detection, and prognostics of probable failures at high accuracy levels. Machine learning is used to implement condition-based maintenance, which lowers downtime, operating expenses, and unforeseen transformer failure by a great extent. The system also includes a secure and scalable architecture that provides real-time data processing and remote-control support, making it applicable to both urban and rural substations. Experimental results show the capability of the system to effectively perform early fault detection than traditional monitoring methods, resulting in improved grid resilience, operation efficiency, and cost savings. This novel solution helps the creation of a smart, data-infused power grid with overall system safety and reliability enhanced.</em>
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S. Venkateshwarlu. "Improved Power Transfer Capability of Micro-Grid Using Deep Learning Algorithms." Journal of Information Systems Engineering and Management 10, no. 20s (2025): 581–600. https://doi.org/10.52783/jisem.v10i20s.3192.
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Deep learning excellence for boosting micro-grid power (new, novel, and contributive advantages over existing methods study is used to optimize the energy distribution, minimize the transmission loss, and maintain stable power flow in dynamic micro-grid surroundings. To maximize the efficiency of power transfer, a predictive model based on deep learning is developed, which integrates real-time grid parameters, load variability, and renewable sources. Unlike past work that is trained on historical data, the proposed framework is trained on the fly utilizing real-time grid data and uses neural networks to facilitate adaptive decision-making and fault detection. We ground our conclusions based on extensive simulations and experimental validations showing a considerable improvement in voltage stability, frequency regulation, and overall grid resilience. Results show that deep learning model transfer power at a higher efficiency, resulting in a lower energy loss, as compared to classical control strategies. The superiority of the proposed approach is further exemplified through comparative evaluative analysis against traditional optimization methods. You may not use this study and can scale up for intelligent grid management using this process that would satisfactorily integrate renewable energy sources managing every peak in the operation and sustainably increase responsiveness to this infrastructure. The results highlight how deep learning could transform smart grid functions and enable future energy systems to be more reliable, efficient, and self-sustaining through micro-grids.
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Sasilatha, T., Adolf Asih Suprianto, and Hamdani Hamdani. "AI-Driven Approaches to Power Grid Management: Achieving Efficiency and Reliability." International Journal of Advances in Artificial Intelligence and Machine Learning 2, no. 1 (2025): 27–37. https://doi.org/10.58723/ijaaiml.v2i1.380.
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The main objective of this research is to improve the efficiency, reliability, and security of the power grid through the integration of artificial intelligence (AI) techniques. The research method involves developing an integrated AI-SGMS framework, including: (1) AI-based Load Forecasting using LSTM and transformer models; (2) Reinforcement Learning for Network Optimization with deep reinforcement learning (DRL) agents; (3) AI-enabled Fault Detection using CNN and autoencoder; (4) AI-driven Intrusion Detection System (IDS) for cybersecurity; and (5) Edge Computing for Decentralized Decision Making. The results show that AI-SGMS is able to optimize energy distribution, improve predictive maintenance, strengthen cybersecurity, and enhance network resilience. The system reduces waste, prevents congestion, detects potential failures, and mitigates cyber threats. Decentralized decision-making ensures rapid response and network resilience. The conclusion of this research is that the application of AI in power grid management, such as AI-SGMS, has the potential to revolutionize energy distribution, reduce operational costs, and support the transition to a sustainable, resilient, and efficient power grid. This research provides a foundation for broader development of AI solutions in power grid management.
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Kumar Chillappagari, Pavan, Karthick Nagaraj, and Madhukar Rao Airineni. "Open-circuit fault resilient ability multi level inverter with reduced switch count for off grid applications." International Journal of Electrical and Computer Engineering (IJECE) 12, no. 3 (2022): 2353. http://dx.doi.org/10.11591/ijece.v12i3.pp2353-2362.
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<p>In a multi-level inverter (MLI), the switching component number effect on volume and reliability is a major concern in on-grid and off-grid applications. The recent trend in MLI, reduced component number of power switches, and capacitors in multi-level inverter topologies have been driven for power conversion. The concept of fault tolerance is not considered in many such configurations; due to this the reliability of the MLI is very low. So now it is a major research concern, to develop a strong fault resilient ability power electronic converter. In this work, a novel configuration of a multilevel inverter with a lower switch count is proposed and analyzed with fault tolerance operation for improvement of reliability. Generally, the fault-tolerant operation is analyzed in only any one of the switches in MLI. But the proposed topology is concerned with multiple switch fault tolerance. Further, the phase disposition pulse width modulation (PDPWM) control scheme is utilized for the operation of the proposed inverter topology. The proposed inverter topology is simulated in MATLAB/Simulink environment under normal and faulty condition; the results are obtained and validated.</p>
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Pavan, Kumar Chillappagari, Nagaraj Karthick, and Rao Airineni Madhukar. "Open-circuit fault resilient ability multi level inverter with reduced switch count for off grid applications." International Journal of Electrical and Computer Engineering (IJECE) 12, no. 3 (2022): 2353–62. https://doi.org/10.11591/ijece.v12i3.pp2353-2362.
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In a multi-level inverter (MLI), the switching component number effect on volume and reliability is a major concern in on-grid and off-grid applications. The recent trend in MLI, reduced component number of power switches, and capacitors in multi-level inverter topologies have been driven for power conversion. The concept of fault tolerance is not considered in many such configurations; due to this the reliability of the MLI is very low. So now it is a major research concern, to develop a strong fault resilient ability power electronic converter. In this work, a novel configuration of a multilevel inverter with a lower switch count is proposed and analyzed with fault tolerance operation for improvement of reliability. Generally, the fault-tolerant operation is analyzed in only any one of the switches in MLI. But the proposed topology is concerned with multiple switch fault tolerance. Further, the phase disposition pulse width modulation (PDPWM) control scheme is utilized for the operation of the proposed inverter topology. The proposed inverter topology is simulated in MATLAB/Simulink environment under normal and faulty condition; the results are obtained and validated.
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Ibrahim, Mariam, and Asma Alkhraibat. "Resiliency Assessment of Microgrid Systems." Applied Sciences 10, no. 5 (2020): 1824. http://dx.doi.org/10.3390/app10051824.
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Measuring resiliency of smart grid systems is one of the vital topics towards maintaining a reliable and efficient operation under attacks. This paper introduces a set of factors that are utilized for resiliency quantification of microgrid (MG) systems. The level of resilience (LoR) measure is determined by examining the voltage sag percentage, the level of performance reduction (LoPR) as measured by percentage of reduction of load served, recovery time (RT), which is the time system takes to detect and recover from an attack/fault, and the time to reach Power Balance state (Tb) during the islanded mode. As an illustrative example, a comparison based on the resiliency level is presented for two topologies of MGs under an attack scenario.
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Kishor, Saikia. "Fault Analysis and Recovery in Power Grids Using Synchro-Phasor Technology and Phasor Measurement Units." International Journal of Innovative Technology and Exploring Engineering (IJITEE) 14, no. 3 (2025): 1–8. https://doi.org/10.35940/ijitee.B1040.14030225.
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<strong>Abstract: </strong>This research proposes a comprehensive synchrophasor-based fault analysis framework for power grids, aimed at enhancing fault detection, localization, and system recovery. The framework models a real-world power network using Simulink, incorporating essential components such as buses, generators, three-phase transmission lines, and load systems. Phasor Measurement Units (PMUs) are strategically deployed across the network to provide synchronized measurements of voltage and current phasors, including magnitude, phase angle, and frequency, referenced to a common GPS-based time signal. This setup enables precise monitoring and analysis of system behavior under steady-state and fault conditions. The study examines two fault scenarios: single-line-to-ground (SLG) and three-phase faults, introduced at Bus B4. The results reveal significant deviations in system parameters during fault conditions, including voltage collapse, current surges, phase angle shifts, and frequency disturbances. Single-line-to-ground faults exhibited faster recovery times for voltage (0.8 s), frequency (0.6 s), and phase angle (0.7 s) compared to three-phase faults, where recovery times extended to 2.5 s, 2.8 s, and 3.0 s, respectively. The rate of change of phase angle (ROCOA) was identified as a key indicator for fault detection and localization, with PMUs capturing sharp ROCOA spikes at the fault location. The proposed framework successfully validates the effectiveness of PMU-based synchro-phasor technology in detecting and localizing faults in real time. The analysis highlights the differences in system response between single-line-to-ground and three-phase faults, demonstrating the severity of the latter. The findings underscore the need for rapid recovery strategies, especially for severe faults, to ensure system stability and reliability. This framework contributes to the development of Wide Area Monitoring Systems (WAMS) for modern smart grids, offering enhanced situational awareness, faster fault response, and improved system resilience.
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Mu, Longfei. "Adaptive low voltage crossing strategy for symmetrical faults of networked inverters." Journal of Physics: Conference Series 2785, no. 1 (2024): 012040. http://dx.doi.org/10.1088/1742-6596/2785/1/012040.
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Abstract Due to the inability of grid-following control methods to compensate for the comprehensive reduction in system inertia, strength, and short-circuit capacity caused by the decreased proportion of synchronous machines, the grid exhibits insufficient resilience to disturbances, leading to severe stability risks in terms of rotor angle, frequency, and voltage. Grid-forming inverters have emerged to address this issue. During symmetrical faults, the low-voltage ride-through of grid-forming inverters often involves freezing the reactive power loop, resulting in a direct output of higher voltage and reactive current. However, an increase in reactive power command leads to a sharp rise in the internal potential magnitude of grid-forming inverters. Upon clearance of the grid fault, the excessively high internal potential may cause a rapid, short-term increase in terminal voltage, potentially resulting in overvoltage, which is detrimental to the operation of grid-connected equipment. In response to this issue, this paper proposes an adaptive low-voltage ride-through control strategy for grid-forming inverters under symmetrical faults controlled by a virtual synchronous machine control method. Initially, a virtual synchronous machine control model is established. Limiting current is achieved through the construction of a fixed virtual impedance loop. The issues associated with fixed virtual impedance current limiting are analyzed, leading to the proposal of an adaptive virtual impedance control strategy. Finally, the feasibility of the proposed method is verified through simulation experiments.
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Ngang, N. B., M. Ogharandukun, and C. C. Nwagu. "Enhancing Power System Resilience against Cyber Attack using Blockchain and Ai-Based Security Solution." International Journal of Cryptocurrency and Blockchain Theories 5, no. 1 (2025): 1–17. https://doi.org/10.5281/zenodo.14801590.
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This study investigates the application of advanced control techniques in enhancing the efficiency and reliability of electrical power systems. With increasing global energy demand and the integration of renewable energy sources, modern power grids face challenges such as instability, fluctuating supply, and growing complexities in load management. The research aims to address these issues through the design and implementation of robust controllers that optimize power flow, reduce system losses, and enhance overall stability. Using a hybrid control framework that integrates fuzzy logic, proportional-integral-derivative (PID) controllers, and artificial neural networks (ANN), this study explores novel methodologies for dynamic system control. Simulations and real-time experiments were conducted to evaluate the performance of these controllers under varying conditions, including fault occurrences and load fluctuations. Results demonstrated significant improvements in power quality, faster response times to disturbances, and reduced total harmonic distortion (THD) compared to conventional control methods. Additionally, this work examines the role of smart grid technologies in facilitating real-time monitoring and adaptive control in power systems. Internet of Things (IoT) devices and machine learning algorithms were employed to enable predictive maintenance and enhance fault tolerance. This multidisciplinary approach highlights the synergy between modern control theories and technological innovations in addressing current challenges in the energy sector. The findings underscore the potential of advanced control systems to revolutionize power systems, paving the way for smarter, more sustainable grids. Practical implications for policymakers and industry stakeholders are discussed, emphasizing the need for investment in research and development, as well as capacity building in engineering expertise. This research contributes to the growing body of knowledge in electrical and electronic engineering, particularly in the Nigerian context, where the reliability of power supply is critical for socioeconomic development. Future work will focus on scaling these solutions for broader applications in sub-Saharan Africa.
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Mahmoud, Moamin A., Naziffa Raha Md Nasir, Mathuri Gurunathan, Preveena Raj, and Salama A. Mostafa. "The Current State of the Art in Research on Predictive Maintenance in Smart Grid Distribution Network: Fault’s Types, Causes, and Prediction Methods—A Systematic Review." Energies 14, no. 16 (2021): 5078. http://dx.doi.org/10.3390/en14165078.
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With the exponential growth of science, Internet of Things (IoT) innovation, and expanding significance in renewable energy, Smart Grid has become an advanced innovative thought universally as a solution for the power demand increase around the world. The smart grid is the most practical trend of effective transmission of present-day power assets. The paper aims to survey the present literature concerning predictive maintenance and different types of faults that could be detected within the smart grid. Four databases (Scopus, ScienceDirect, IEEE Xplore, and Web of Science) were searched between 2012 and 2020. Sixty-five (n = 65) were chosen based on specified exclusion and inclusion criteria. Fifty-seven percent (n = 37/65) of the studies analyzed the issues from predictive maintenance perspectives, while about 18% (n = 12/65) focused on factors-related review studies on the smart grid and about 15% (n = 10/65) focused on factors related to the experimental study. The remaining 9% (n = 6/65) concentrated on fields related to the challenges and benefits of the study. The significance of predictive maintenance has been developing over time in connection with Industry 4.0 revolution. The paper’s fundamental commitment is the outline and overview of faults in the smart grid such as fault location and detection. Therefore, advanced methods of applying Artificial Intelligence (AI) techniques can enhance and improve the reliability and resilience of smart grid systems. For future direction, we aim to supply a deep understanding of Smart meters to detect or monitor faults in the smart grid as it is the primary IoT sensor in an AMI.
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Vamsi, Krishna Kokku. "AI-Powered Fault Detection and Load Imbalance Mitigation in Smart Grids Using Hybrid Deep Learning Architectures." Research and Reviews: Advancement in Cyber Security 2, no. 2 (2025): 26–32. https://doi.org/10.5281/zenodo.15281369.
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<em>In the era of rapidly evolving smart grid technologies, fault detection and load imbalance management remain paramount to ensuring energy efficiency, reliability, and sustainability. This study presents a comprehensive exploration of hybrid deep learning architectures to identify and mitigate faults and load imbalances in smart grids. Leveraging Graph Neural Networks, Generative Adversarial Networks, Recurrent Neural Networks, and advanced optimization techniques such as the Chameleon Optimization Algorithm, the research demonstrates the strength of AI integration in enhancing power system diagnostics. A multi-domain literature review of 29 peer-reviewed articles authored or co-authored by leading experts, including Veeramachaneni, Bittla, and Yarram, showcases a converging trend of AI in energy systems, cybersecurity, edge computing, and software engineering. The study provides empirical validation through synthetic and real-time datasets to evaluate fault classification accuracy, load prediction performance, and system resilience. Results show marked improvements in precision, detection time, and energy optimization. The paper concludes by emphasizing the transformative role of AI in cyber-physical energy systems and outlines future research directions.</em>
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Wanjari,, Keshao C., Shreyash S. Bansod, Rohit R. Jumde, Dipendra H. Dhande, Zaid W. Qureshi,, and Dr Muneeb Ahmad. "Synchronous Buck Converter-Based Solar Power for Utility Purpose." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 04 (2025): 1–9. https://doi.org/10.55041/ijsrem43687.
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This project explores the potential of synchronous buck converters as a vital technology for integrating utility-scale solar power. We focus on the design, implementation, and performance evaluation of a high-efficiency synchronous buck converter tailored for grid-connected photovoltaic (PV) systems. Unlike traditional designs that use diodes, our approach leverages MOSFETs as switching elements, significantly reducing conduction losses and improving efficiency—key factors for maximizing solar energy harvest. The primary objective is to optimize the converter for large-scale applications, emphasizing conversion efficiency, power density, and dynamic response. We develop a detailed mathematical model to analyse its steady-state and dynamic behaviour, forming the basis for an advanced control strategy. This strategy precisely regulates output voltage while incorporating Maximum Power Point Tracking (MPPT) to ensure optimal energy extraction despite variations in solar irradiance and temperature. Beyond efficient power conversion, we explore the converter’s role in grid stability. Our research investigates grid support functionalities, including reactive power injection and harmonic mitigation, which enhance voltage stability and power quality. Additionally, we assess the converter’s performance under grid disturbances, evaluating its fault ride-through capability and contribution to overall grid resilience as renewable energy penetration grows.
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Kumar, Dileep, Wajiha Shireen, and Nanik Ram. "Grid Integration of Offshore Wind Energy: A Review on Fault Ride Through Techniques for MMC-HVDC Systems." Energies 17, no. 21 (2024): 5308. http://dx.doi.org/10.3390/en17215308.
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Over the past few decades, wind energy has expanded to become a widespread, clean, and sustainable energy source. However, integrating offshore wind energy with the onshore AC grids presents many stability and control challenges that hinder the reliability and resilience of AC grids, particularly during faults. To address this issue, current grid codes require offshore wind farms (OWFs) to remain connected during and after faults. This requirement is challenging because, depending on the fault location and power flow direction, DC link over- or under-voltage can occur, potentially leading to the shutdown of converter stations. Therefore, this necessitates the proper understanding of key technical concepts associated with the integration of OWFs. To help fill the gap, this article performs an in-depth investigation of existing alternating current fault ride through (ACFRT) techniques of modular multilevel converter-based high-voltage direct current (MMC-HVDC) for OWFs. These techniques include the use of AC/DC choppers, flywheel energy storage devices (FESDs), power reduction strategies for OWFs, and energy optimization of the MMC. This article covers both scenarios of onshore and offshore AC faults. Given the importance of wind turbines (WTs) in transforming wind energy into mechanical energy, this article also presents an overview of four WT topologies. In addition, this article explores the advanced converter topologies employed in HVDC systems to transform three-phase AC voltages to DC voltages and vice versa at each terminal of the DC link. Finally, this article explores the key stability and control concepts, such as small signal stability and large disturbance stability, followed by future research trends in the development of converter topologies for HVDC transmission such as hybrid HVDC systems, which combine current source converters (CSCs) and voltage source converters (VSCs) and diode rectifier-based HVDC (DR-HVDC) systems.
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Buitrón-Barros, Henry Orlando. "Integración de inteligencia artificial en redes eléctricas inteligentes y su potencial transformador." Horizon Nexus Journal 2, no. 2 (2024): 29–42. https://doi.org/10.70881/hnj/v2/n2/37.
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The integration of artificial intelligence (AI) in smart grids redefines energy management, optimizing power system stability, efficiency, and sustainability. This article explores the applications of AI in demand forecasting, power flow optimization, adaptive demand response and early fault detection, highlighting how these techniques improve decision making and grid resilience in the face of variability from renewable sources. Methodologically, a comprehensive literature review is conducted on academic bases, assessing the transformative impact of AI on electricity infrastructure. The findings point to regulatory barriers and high infrastructure costs as critical obstacles to its implementation, in addition to technical and security challenges inherent to digitization. It concludes that, despite the challenges, AI has the potential to transform power grids into adaptive and robust systems, being key to a sustainable energy future. This research provides a relevant conceptual framework for the development and optimization of smart grids through AI, urging a collaborative approach that embraces regulation, technological innovation, and cybersecurity.
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Chalishazar, Vishvas H., Ted K. A. Brekken, Darin Johnson, et al. "Connecting Risk and Resilience for a Power System Using the Portland Hills Fault Case Study." Processes 8, no. 10 (2020): 1200. http://dx.doi.org/10.3390/pr8101200.
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Active seismic faults in the Pacific Northwest area have encouraged electric utilities in the region to deeply contemplate and proactively intervene to support grid resilience. To further this effort this research introduces Monte Carlo (MC)-based power system modeling as a means to inform the Performance Based Earthquake Engineering method and simulates 100,000 sample earthquakes of a 6.8 magnitude (M6.8) Portland Hills Fault (PHF) scenario in the Portland General Electric (PGE) service territory as a proof of concept. This paper also proposes the resilience metric Seismic Load Recovery Factor (SLRF) to quantify the recovery of a downed power system and thus can be used to quantify earthquake economic risk. Using MC results, the SLRF was evaluated to be 19.7 h and the expected economic consequence cost of a M6.8 PHF event was found to be $180 million with an annualized risk of $90,000 given the event’s 1 in 2000 year probability of occurrence. The MC results also identified the eight most consequential substations in the PGE system—i.e., those that contributed to maximum load loss. This paper concludes that retrofitting these substations reduced the expected consequence cost of a M6.8 PHF event to $117 million.
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Demoulias, Charis S., Kyriaki-Nefeli D. Malamaki, Spyros Gkavanoudis, et al. "Ancillary Services Offered by Distributed Renewable Energy Sources at the Distribution Grid Level: An Attempt at Proper Definition and Quantification." Applied Sciences 10, no. 20 (2020): 7106. http://dx.doi.org/10.3390/app10207106.
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The gradual displacement of synchronous generators driven by conventional power plants, due to the increasing penetration of distributed renewable energy sources (DRES) in distribution grids, is creating a shortage of crucial ancillary services (AS) which are vital for the frequency and voltage stability of the grid. These AS, and some new ones, could now be offered by the DRES, particularly those that are converter interfaced, in a coordinated way in order to preserve the grid stability and resilience. Although recent standards and grid codes specify that the DRES exhibit some system support functions, there are no specifications on how to measure and quantify (M & Q) them both at DRES level and in aggregated form. The M & Q of AS is crucial, since it would allow the AS to be treated as tradable AS in the current and future AS markets. This paper attempts to define a number of AS that can be offered by converter-interfaced DRES and suggests methods for their M & Q. The new AS addressed are: (1) inertial response; (2) primary frequency response; (3) active power smoothing (ramp-rate limitation); (4) exchange of reactive power for voltage regulation; (5) fault-ride-through (FRT) and contribution to fault clearing; (6) voltage harmonic mitigation. Additionally, a rough estimation of the additional investment and operational cost, as well as the financial benefits associated with each AS is provided in order to form the basis for the development of business models around each AS in the near future.