Relevant bibliographies by topics / IEEE 33-Bus Distribution Network

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Academic literature on the topic 'IEEE 33-Bus Distribution Network'

Author: Grafiati
Published: 7 June 2025
Last updated: 2 August 2025

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Journal articles on the topic "IEEE 33-Bus Distribution Network"

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Ojo, Kayode, Seyi Fanifosi, Awelewa Ayokunle, and Isaac Samuel. "Cuckoo search algorithm approach for optimal placement and sizing of distribution generation in radial distribution networks." International Journal of Electrical and Computer Engineering (IJECE) 15, no. 3 (2025): 2681. https://doi.org/10.11591/ijece.v15i3.pp2681-2696.

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<p><span>Radial distribution networks (RDNs) often experience power loss due to improper distribution generation (DG) allocation. Strategic DG placement can reduce power loss, minimize costs, and improve voltage profiles and stability. This research optimizes DG placement and sizing in RDNs using the cuckoo search algorithm (CSA). The objective function considers losses across all network branches, and CSA identifies optimal DG locations and sizes. Tested on IEEE 33-bus, IEEE 69-bus, and Nigeria's Imalefalafia 32-bus RDN, the Cuckoo Search technique results in optimal DG locations
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Doni, Donata, Rudy Gianto, and Zainal Abidin. "Voltage Stability Index Improvement In Radial Distribution Systems Using Capacitors." Journal of Electrical Engineering, Energy, and Information Technology (J3EIT) 12, no. 1 (2024): 342. http://dx.doi.org/10.26418/j3eit.v12i1.76767.

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The radial distribution network system is an important part of the electric power system because this distribution system is useful for distributing electricity to loads (customers). the existence of voltage drops and electric power losses in the provision of electrical energy is something that cannot be avoided. There are several ways that can be done to improve voltage, one of which is the installation of Shunt capacitors in the primary distribution network. Voltage drop can occur in the power system due to the loss of voltage stability in the radial distribution network. This study analyzes
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Simamora, Yoakim, Sigit Sukmajati, and Rio Afrianda. "OPTIMASI REKONFIGURASI JARINGAN DISTRIBUSI RADIAL UNTUK MEMINIMALKAN RUGI JARINGAN MENGGUNAKAN METODE SIMPLE BRANCH EXCHANGE." Energi & Kelistrikan 10, no. 2 (2019): 102–11. http://dx.doi.org/10.33322/energi.v10i2.218.

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The increasing electric energy consumption must be balanced with good electricity network quality. To overcome this, we need a distribution network reconfiguration. Reconfiguration in power distribution network is done to improve power distribution network quality. Problem that exist in the radial distribution network is power losses. To minimize the power losses can be overcome by compensating capacitor. Done by determining the location of capacitors in distribution networks. With that the capacitor compensates the distribution network is more effective and efficient in distributing power.&#x
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Okelola, M. O., O. W. Adebiyi, S. A. Salimon, S. O. Ayanlade, and A. L. Amoo. "Optimal sizing and placement of shunt capacitors on the distribution system using Whale Optimization Algorithm." Nigerian Journal of Technological Development 19, no. 1 (2022): 39–47. http://dx.doi.org/10.4314/njtd.v19i1.5.

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This paper aims at power loss minimization and reduction of cost due to losses in the distribution network as well as improvement of the network voltage stability and profile via optimal shunt capacitor allocation. The optimal shunt capacitor allocation was obtained using Whale Optimization Algorithm considering technical (real power loss and VSI) and economic objective functions as sub-optimal allocation may result in undesired effects on the network. The obtained optimal sizes of the shunt capacitors were placed on the obtained location of the distribution networks to ascertain the behavior
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Wang, Chaoxue, and Yue Zhang. "Dynamic Reconfiguration of Active Distribution Network Based on Improved Equilibrium Optimizer." Applied Sciences 15, no. 12 (2025): 6423. https://doi.org/10.3390/app15126423.

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To better address the reconfiguration problem of distribution networks with distributed generation (DG), a dynamic reconfiguration model is developed that accounts for the time-varying characteristics of both load demand and DG output. First, an enhanced fuzzy C-means clustering method is proposed for load period partitioning, which integrates spatiotemporal load features and optimal network structure similarity to improve clustering accuracy. Second, an adaptive ordered loop-based feasibility judgment model is developed to filter infeasible and low-quality solutions based on operational const
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Kazi Abdul Kader, Kazi Abdul Kader, Dr Md Abdul Mannan Md. Abdul Mannan, and Dr Md Rifat Hazari Md. Rifat Hazari. "The Distribution Network Loss Minimization by Incorporating DG Using Particle Swarm Optimization (PSO) Technique." AIUB Journal of Science and Engineering (AJSE) 23, no. 3 (2024): 200–208. https://doi.org/10.53799/ajse.v23i3.780.

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This research is based on the power system optimal planning and operation segment. This study presents a methodology for reducing power losses in distribution power systems through the incorporation of both fixed and uncertain photovoltaic (PV) generation, utilizing the Particle Swarm Optimization (PSO) technique. The proposed approach aims to address the unpredictability of PV generation and optimize the placement and size of Distributed Generation (DG) units to minimize I2R loss in the distribution network. The main focus is on how to minimize the existing losses occurring in the distributio
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Alex, Aligbe Patience E. Orukpe and Samuel O. Igbinovia. "Optimal Allocation and Sizing of Distributed Generators for Multi-Objective Function in Distribution Network Using Intelligent Water Drop Algorithm." Journal of Energy Technology and Environment 6, no. 2 (2024): 20–33. https://doi.org/10.5281/zenodo.11407940.

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<em>There is need to optimize the operation of a distribution system for power losses minimization, voltage profile improvement and network efficiency. The researchers in this work have deployed novel concept in order to achieve the above aim. The main purpose of this research is to minimize total active power losses and improve the voltage profile in IEEE 33 bus and Irrua Distribution Network (IDN). Stability Index (SI) is used to optimally allocate Distributed Generators (DGs), while the Intelligent Water Drop Algorithm (IWD) is deployed for computing the size of DGs. IWD is an algorithm whi
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Ayanlade, Samson Oladayo, Funso Kehinde Ariyo, Abdulrasaq Jimoh, et al. "Optimal Allocation of Photovoltaic Distributed Generations in Radial Distribution Networks." Sustainability 15, no. 18 (2023): 13933. http://dx.doi.org/10.3390/su151813933.

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Photovoltaic distributed generation (PVDG) is a noteworthy form of distributed energy generation that boasts a multitude of advantages. It not only produces absolutely no greenhouse gas emissions but also demands minimal maintenance. Consequently, PVDG has found widespread applications within distribution networks (DNs), particularly in the realm of improving network efficiency. In this research study, the dingo optimization algorithm (DOA) played a pivotal role in optimizing PVDGs with the primary aim of enhancing the performance of DNs. The crux of this optimization effort revolved around fo
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Araga, Idris A., Abel E. Airoboman, and Simon A. Auta. "Voltage profile improvement and losses minimization for Hayin Rigasa radial network Kaduna using distributed generation." Journal of Advances in Science and Engineering 5, no. 1 (2021): 20–36. http://dx.doi.org/10.37121/jase.v5i1.163.

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This research work has presented the application of distributed generation (DG) units in a simultaneous placement approach on IEEE 33 radial test systems for validation of the technique with further implementation on 56-Bus Hayin Rigasa feeder. The genetic algorithm (GA) is employed in obtaining the optimal sizes and load loss sensitivity index for locations of the DGs for entire active and reactive power loss reduction. The voltage profile index is computed for each bus of the networks to ascertain the weakest voltage bus of the network before and after DG and circuit breaker allocation. The
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Alshareef, Sami M., and Ahmed Fathy. "Efficient Red Kite Optimization Algorithm for Integrating the Renewable Sources and Electric Vehicle Fast Charging Stations in Radial Distribution Networks." Mathematics 11, no. 15 (2023): 3305. http://dx.doi.org/10.3390/math11153305.

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The high penetration of renewable energy resources’ (RESs) and electric vehicles’ (EVs) demands to power systems can stress the network reliability due to their stochastic natures. This can reduce the power quality in addition to increasing the network power losses and voltage deviations. This problem can be solved by allocating RESs and EV fast charging stations (FCSs) in suitable locations on the grid. So, this paper proposes a new approach using the red kite optimization algorithm (ROA) for integrating RESs and FCSs to the distribution network through identifying their best sizes and locati
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Book chapters on the topic "IEEE 33-Bus Distribution Network"

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Vela Palaquibay, Kevin Joel, Manuel Darío Jaramillo, and Diego Carrión. "Enhanced Reactive Power Compensation for Flicker Mitigation in Wind Farm-Integrated Distribution Networks Using Advanced D-STATCOM Control." In Lecture Notes in Networks and Systems. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-87065-1_22.

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Abstract This research aims to demonstrate the importance of reactive power compensation in electrical systems for mitigating fast Flicker voltage disturbances that commonly occur in systems with wind generation; thereby, this paper’s main goal is to improve the quality of distribution systems. The 34-node IEEE test system was chosen as the base system for this study. Simulations were conducted using MATLAB to verify system stability and ensure the system’s nominal voltage matches the design specifications set by the IEEE. The analysis identified bus 27 as having the greatest voltage fluctuati
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Kumar, Mahesh, Perumal Nallagownden, Irraivan Elamvazuthi, Pandian Vasant, and Luqman Hakim Rahman. "Swarm-Intelligence-Based Optimal Placement and Sizing of Distributed Generation in Distribution Network." In Advances in Systems Analysis, Software Engineering, and High Performance Computing. IGI Global, 2018. http://dx.doi.org/10.4018/978-1-5225-3129-6.ch002.

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In the distribution system, distributed generation (DG) are getting more important because of the electricity demands, fossil fuel depletion and environment concerns. The placement and sizing of DGs have greatly impact on the voltage stability and losses in the distribution network. In this chapter, a particle swarm optimization (PSO) algorithm has been proposed for optimal placement and sizing of DG to improve voltage stability index in the radial distribution system. The two i.e. active power and combination of active and reactive power types of DGs are proposed to realize the effect of DG i
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Ali, Abid, Nursyarizal Mohd Nor, Taib Ibrahim, Mohd Fakhizan Romlie, and Kishore Bingi. "Sizing and Placement of Battery-Sourced Solar Photovoltaic (B-SSPV) Plants in Distribution Networks." In Advances in Computer and Electrical Engineering. IGI Global, 2018. http://dx.doi.org/10.4018/978-1-5225-3531-7.ch011.

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This chapter proposes a mixed-integer optimization using genetic algorithm (MIOGA) for determining the optimum sizes and placements of battery-sourced solar photovoltaic (B-SSPV) plants to reduce the total energy losses in distribution networks. Total energy loss index (TELI) is formulated as the main objective function and meanwhile bus voltage deviations and PV penetrations of B-SSPV plants are calculated. To deal the stochastic behavior of solar irradiance, 15 years of weather data is modeled by using beta probability density function (Beta-PDF). The proposed algorithm is applied on IEEE 33
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Ali, Abid, Nursyarizal Mohd Nor, Taib Ibrahim, Mohd Fakhizan Romlie, and Kishore Bingi. "Sizing and Placement of Battery-Sourced Solar Photovoltaic (B-SSPV) Plants in Distribution Networks." In Research Anthology on Clean Energy Management and Solutions. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-9152-9.ch048.

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This chapter proposes a mixed-integer optimization using genetic algorithm (MIOGA) for determining the optimum sizes and placements of battery-sourced solar photovoltaic (B-SSPV) plants to reduce the total energy losses in distribution networks. Total energy loss index (TELI) is formulated as the main objective function and meanwhile bus voltage deviations and PV penetrations of B-SSPV plants are calculated. To deal the stochastic behavior of solar irradiance, 15 years of weather data is modeled by using beta probability density function (Beta-PDF). The proposed algorithm is applied on IEEE 33
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Ali, Abid, Nursyarizal Mohd Nor, Taib Ibrahim, Mohd Fakhizan Romlie, and Kishore Bingi. "Big Data Storage for the Modeling of Historical Time Series Solar Irradiations." In Advances in Data Mining and Database Management. IGI Global, 2018. http://dx.doi.org/10.4018/978-1-5225-3142-5.ch016.

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This chapter proposes Big Data Analytics for the sizing and locating of solar photovoltaic farms to reduce the total energy loss in distribution networks. The Big Data Analytics, which uses the advance statistical and computational tools for the handling of large data sets, has been adopted for modeling the 15 years of solar weather data. Total Power Loss Index (TPLI) is formulated as the main objective function for the optimization problem and meanwhile bus voltage deviations and penetrations of the PV farms are calculated. To solve the optimization problem, this study adopts the Mixed Intege
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Li, Shiyi, Bojun Li, and Jiaqi Huang. "The Impact of Demand-Side Response on Distribution Network Optimization." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2024. https://doi.org/10.3233/faia241337.

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The distribution network is directly connected to the user side, making its position critical. Demand-side response enables users to actively participate in distribution network scheduling, which plays a vital role in ensuring both the security and economic operation of the network. Therefore, incorporating demand-side response for optimal scheduling in new energy distribution networks is crucial. This paper focuses on the following aspects: First, the components and advantages of new energy active distribution networks are examined, and a detailed model of incentive-based demand-side response
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Zhang, Wei, Xueqing Song, Chaolin He, Hu Xie, Xinglang Xie, and Zhanjie Yang. "New Energy Distribution Network Scheduling Based on Optimized PSO." In Advances in Transdisciplinary Engineering. IOS Press, 2024. http://dx.doi.org/10.3233/atde231166.

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Considering the fluctuation of predicted output power of new energy units such as wind power, a strategy to strengthen the dispatching optimization of new energy distribution network is proposed. Taking the constraints such as operation cost, voltage deviation and network loss of distribution network as a reference, we provide the description of wind power active power control. Then, combined with stochastic chance constrained programming, an improved particle swarm optimization algorithm is proposed to establish the optimal dispatching model of “source-network-load” distribution network. Fina
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Nie, Wenhai, Yi Zhou, Min Yang, and Shaoqiao Dong. "Distribution Network Expansion Planning Considering Source-Load Uncertainty Coordination Based on Stochastic Programming, Monte Carlo Simulation, and Optical Sensing." In Advances in Transdisciplinary Engineering. IOS Press, 2025. https://doi.org/10.3233/atde250284.

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The rapid integration of renewable energy sources (RES) and distributed energy resources (DER) into modern power systems introduces significant challenges for distribution network expansion planning (DNEP). These challenges primarily arise from the inherent uncertainty in renewable generation and load demand, which traditional deterministic and robust optimization models struggle to address effectively. This study proposes a hybrid stochastic optimization framework that integrates Monte Carlo simulation with scenario reduction techniques to improve decision-making under uncertainty. The framew
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Zhou, Yi, Xuan Zhao, Yinan Zhao, Shunxin Li, and Min Zhao. "Impact of Distributed Generation on the Power Supply Reliability of Distribution Networks and Research on Optical Integration Optimization." In Advances in Transdisciplinary Engineering. IOS Press, 2025. https://doi.org/10.3233/atde250263.

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Advanced computational techniques are essential for tackling the issues associated with the integration of Distributed Generation (DG) into power distribution networks, especially in the context of modern smart grids and their role in the development of smart cities. This research establishes an extensive framework that integrates probabilistic modeling, multi-objective optimization, and high-performance computing to assess and improve system reliability and economic efficiency. A probabilistic reliability assessment model is introduced to encapsulate the stochastic properties of distributed g
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Hu, Jiang, Wei Li, Wenxia Liu, Xianggang He, and Yu Zhang. "Research on Identification of Power Grid Weakness Based on Bayesian Inference." In Proceedings of CECNet 2021. IOS Press, 2021. http://dx.doi.org/10.3233/faia210400.

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With the gradual reform and development of the power grid, it is of great significance to study how to effectively identify and evaluate the weak links of the power grid for the actual planning, construction, and operation of the power grid. This paper analyzed the power grid’s historical component data and real-time operation state parameters. We established a weak link identification model based on Bayesian reasoning. Firstly, we constructed the node branch Bayesian network according to the network topology relationship. The power transmission distribution factor is modified according to the
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Conference papers on the topic "IEEE 33-Bus Distribution Network"

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Gopal, Yatindra, J. Vijaychandra, P. Janaki, Karthik Tamvada, Niharika Routh, and Shubrakant Pradhan. "A New Approach for Power-Flow Solutions in Radial Distribution Network with 12, 33, and 69 IEEE Bus System." In 2025 International Conference on Sustainable Energy Technologies and Computational Intelligence (SETCOM). IEEE, 2025. https://doi.org/10.1109/setcom64758.2025.10932435.

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Yadav, Anju, Richa Priyadarshi, Nand Kishor, and Richa Negi. "Assessment of Bus Voltage Variations in the Distribution Network using PDF Estimators." In 2024 IEEE 11th Power India International Conference (PIICON). IEEE, 2024. https://doi.org/10.1109/piicon63519.2024.10995141.

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Deakin, Matthew, and Davis Montenegro. "Demonstrating Almost Linear Time Complexity of Bus Admittance Matrix-Based Distribution Network Power Flow: An Empirical Approach." In 2024 IEEE Power & Energy Society General Meeting (PESGM). IEEE, 2024. http://dx.doi.org/10.1109/pesgm51994.2024.10761076.

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Datta, Suchana, Sadhan Gope, Diptanu Das, Raj Chakraborty, and Punam Das. "Active Power Loss Minimization in IEEE-33 Distribution Bus System by Symbiotic Organisms Search Algorithm." In 2024 IEEE Students Conference on Engineering and Systems (SCES). IEEE, 2024. http://dx.doi.org/10.1109/sces61914.2024.10652464.

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Saurabh, Kumar, B. Rajanarayan Prusty, and Kanhaiya Kumar. "Unique Approach to Solve the Placement and Sizing of DG in IEEE -33 Bus Radial Network." In 2024 International Conference on Communication, Computing and Energy Efficient Technologies (I3CEET). IEEE, 2024. https://doi.org/10.1109/i3ceet61722.2024.10993793.

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Sasa, Asrafun Nahar, Tushar Kanti Roy, and Amanullah Maung Than Oo. "Enhancing DC-Bus Voltage Stability in DC Distribution Networks with Constant Power Loads Using Modified Integral Sliding Mode Controllers." In 2024 IEEE International Conference on Power, Electrical, Electronics and Industrial Applications (PEEIACON). IEEE, 2024. https://doi.org/10.1109/peeiacon63629.2024.10800232.

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Barbulescu, Constantin, Stefan Kilyeni, Cosmin Oros, Alex Bittenbinder, and Marin Salinschi. "Heuristic and Metaheuristic Techniques based Distribution Network Reconfiguration. Case study: IEEE 33 Bus System." In 2021 International Conference on Applied and Theoretical Electricity (ICATE). IEEE, 2021. http://dx.doi.org/10.1109/icate49685.2021.9465030.

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B. Luis, Cordero, and Franco B. John. "Probabilistic Power Flow Analysis Based on Point-Estimate Method for High Penetration of Photovoltaic Generation in Electrical Distribution Systems." In Congresso Brasileiro de Automática - 2020. sbabra, 2020. http://dx.doi.org/10.48011/asba.v2i1.1078.

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Environmental awareness and energy policies led to decarbonization targets, fostering the adoption of distributed energy resource in the distribution network. Particularly, photovoltaic systems have been gaining momentum due to cost-competitive option and financial benefits. However, traditional distribution networks were not designed for intermittency in power generation. This poses technical issues such as reverse power flow, overvoltage, and thermal overloading. Furthermore, the growth in intermittency and variability of distributed energy resources increases the uncertainty, hence, it brin
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MARQUES, ROBERTA CARVALHO, and DIONIZIO PASCHOARELI JUNIOR. "Detection of Voltage Unbalance in Microgrids using Artificial Neural Networks." In Seminar on Power Electronics and Control (SEPOC 2021). sepoc, 2021. http://dx.doi.org/10.53316/sepoc2021.041.

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This work proposes a methodology for detecting voltage imbalance using the OpenDSS software and its COM interface. The proposal is to detect voltage imbalance using the communication between OpenDSS and MATLAB through a Perceptron Multilayer Artificial Neural Network. For that, tests were carried out in a distribution network with distributed generators and energy stores. The studies were conducted on an IEEE 13 bus test system.
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Angeles, C. J., E. J. Mercader, G. E. Tan, M. C. Pacis, and R. F. Bersano. "Fault evaluation and performance of an IEEE Bus 30 power distribution network with distributed generation (DG)." In 2017 IEEE 9th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM). IEEE, 2017. http://dx.doi.org/10.1109/hnicem.2017.8269543.

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