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Journal articles on the topic 'PV system fault detection'

Author: Grafiati

Published: 13 August 2024

Last updated: 31 July 2025

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1

Boubaker, Sahbi, Souad Kamel, Nejib Ghazouani, and Adel Mellit. "Assessment of Machine and Deep Learning Approaches for Fault Diagnosis in Photovoltaic Systems Using Infrared Thermography." Remote Sensing 15, no. 6 (2023): 1686. http://dx.doi.org/10.3390/rs15061686.

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Nowadays, millions of photovoltaic (PV) plants are installed around the world. Given the widespread use of PV supply systems and in order to keep these PV plants safe and to avoid power losses, they should be carefully protected, and eventual faults should be detected, classified and isolated. In this paper, different machine learning (ML) and deep learning (DL) techniques were assessed for fault detection and diagnosis of PV modules. First, a dataset of infrared thermography images of normal and failure PV modules was collected. Second, two sub-datasets were built from the original one: The f

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Basnet, Barun, Hyunjun Chun, and Junho Bang. "An Intelligent Fault Detection Model for Fault Detection in Photovoltaic Systems." Journal of Sensors 2020 (June 9, 2020): 1–11. http://dx.doi.org/10.1155/2020/6960328.

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Effective fault diagnosis in a PV system requires understanding the behavior of the current/voltage (I/V) parameters in different environmental conditions. Especially during the winter season, I/V characters of certain faulty states in a PV system closely resemble that of a normal state. Therefore, a normal fault detection model can falsely predict a well-operating PV system as a faulty state and vice versa. In this paper, an intelligent fault diagnosis model is proposed for the fault detection and classification in PV systems. For the experimental verification, various fault state and normal

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N., Muhammad, Zainuddin H., Jaaper E., and Idrus Z. "An early fault detection approach in grid-connected photovoltaic (GCPV) system." Indonesian Journal of Electrical Engineering and Computer Science (IJEECS) 17, no. 2 (2020): 671–79. https://doi.org/10.11591/ijeecs.v17.i2.pp671-679.

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Faults in any components of PV system shall lead to performance degradation and if prolonged, it can leads to fire hazard. This paper presents an approach of early fault detection via acquired historical data sets of gridconnected PV (GCPV) systems. The approach is a developed algorithm comprises of failure detection on AC power by using Acceptance Ratio (AR) determination. Specifically, the implemented failure detection stage was based on the algorithm that detected differences between the actual and predicted AC power of PV system. Furthermore, the identified alarm of system failure was a de

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Lipták, Róbert, and István Bodnár. "Simulation of fault detection in photovoltaic arrays." Analecta Technica Szegedinensia 15, no. 2 (2021): 31–40. http://dx.doi.org/10.14232/analecta.2021.2.31-40.

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In solar systems, faults in the module and inverter occur in proportion to increased operating time. The identification of fault types and their effects is important information not only for manufacturers but also for investors, solar operators and researchers. Monitoring and diagnosing the condition of photovoltaic (PV) systems is becoming essential to maximize electric power generation, increase the reliability and lifetime of PV power plants. Any faults in the PV modules cause negative economic and safety impacts, reducing the performance of the system and making unwanted electric connectio

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Muhammad, N., H. Zainuddin, E. Jaaper, and Z. Idrus. "An early fault detection approach in grid-connected photovoltaic (GCPV) system." Indonesian Journal of Electrical Engineering and Computer Science 17, no. 2 (2020): 671. http://dx.doi.org/10.11591/ijeecs.v17.i2.pp671-679.

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<span>Faults in any components of PV system shall lead to performance degradation and if prolonged, it can leads to fire hazard. This paper presents an approach of early fault detection via acquired historical data sets of grid-connected PV (GCPV) systems. The approach is a developed algorithm comprises of failure detection on AC power by using Acceptance Ratio (AR) determination. Specifically, the implemented failure detection stage was based on the algorithm that detected differences between the actual and predicted AC power of PV system. Furthermore, the identified alarm of system fai

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Benmouiza, Khalil. "Grid Connected PV Systems Fault Detection using K-Means Clustering Algorithm." International Journal of Emerging Technology and Advanced Engineering 13, no. 5 (2023): 73–83. http://dx.doi.org/10.46338/ijetae0523_07.

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—Efficiency in photovoltaic (PV) energy production is significantly influenced by various electrical, environmental, and manufacturing-related factors. These variables often lead to a range of PV generator faults, compromising the system's performance and the overall grid's safety. The current fault detection methods can be complex and resource-intensive. In this paper, we propose a novel and efficient grid-connected PV system fault detection mechanism using the k-means clustering algorithm. Our approach categorizes the possible faults based on clustering the output PV and grid powers under he

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Amiri, Ahmed Faris, Sofiane Kichou, Houcine Oudira, Aissa Chouder, and Santiago Silvestre. "Fault Detection and Diagnosis of a Photovoltaic System Based on Deep Learning Using the Combination of a Convolutional Neural Network (CNN) and Bidirectional Gated Recurrent Unit (Bi-GRU)." Sustainability 16, no. 3 (2024): 1012. http://dx.doi.org/10.3390/su16031012.

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The meticulous monitoring and diagnosis of faults in photovoltaic (PV) systems enhances their reliability and facilitates a smooth transition to sustainable energy. This paper introduces a novel application of deep learning for fault detection and diagnosis in PV systems, employing a three-step approach. Firstly, a robust PV model is developed and fine-tuned using a heuristic optimization approach. Secondly, a comprehensive database is constructed, incorporating PV model data alongside monitored module temperature and solar irradiance for both healthy and faulty operation conditions. Lastly, f

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Al-Katheri, Ahmed A., Essam A. Al-Ammar, Majed A. Alotaibi, Wonsuk Ko, Sisam Park, and Hyeong-Jin Choi. "Application of Artificial Intelligence in PV Fault Detection." Sustainability 14, no. 21 (2022): 13815. http://dx.doi.org/10.3390/su142113815.

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The rapid revolution in the solar industry over the last several years has increased the significance of photovoltaic (PV) systems. Power photovoltaic generation systems work in various outdoor climate conditions; therefore, faults may occur within the PV arrays in the power system. Fault detection is a fundamental task needed to improve the reliability, efficiency, and safety of PV systems, and, if not detected, the cost associated with the loss of power generated from PV modules will be quite high. Moreover, maintenance staff will take more time and effort to fix undetermined faults. Due to

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Osmani, Khaled, Ahmad Haddad, Thierry Lemenand, Bruno Castanier, and Mohamad Ramadan. "Material Based Fault Detection Methods for PV Systems." Key Engineering Materials 865 (September 2020): 111–15. http://dx.doi.org/10.4028/www.scientific.net/kem.865.111.

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The overall efficiency of a PV system is strongly affected by the PV cell raw materials. Since a reliable renewable energy source is expected to produce maximum power with longest lifetime and minimum errors, a critical aspect to bear in mind is the occurrence of PV faults according to raw material types. The different failure scenarios occurring in PV system, decrease its output power, reduce its life expectancy and ban the system from meeting load demands, yielding to severe consecutive blackouts. This paper aims first to present different core materials types, material based fault occurring

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Zaki, Sayed A., Honglu Zhu, and Jianxi Yao. "Fault detection and diagnosis of photovoltaic system using fuzzy logic control." E3S Web of Conferences 107 (2019): 02001. http://dx.doi.org/10.1051/e3sconf/201910702001.

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Among several renewable energy resources, Solar has great potential to solve the world’s energy problems. With the rapid expansion and installation of PV system worldwide, fault detection and diagnosis has become the most significant issue in order to raise the system efficiency and reduce the maintenance cost as well as repair time. This paper presented a method for monitoring, identifying, and detecting different faults in PV array. This method is built based on comparing the measured electrical parameters with its theoretical parameters in case of normal and faulty conditions of PV array. F

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Judith, Jancy D. "Photovoltaic module failure detection using machine vision and lazy learning technique." i-manager's Journal on Circuits and Systems 12, no. 1 (2024): 27. https://doi.org/10.26634/jcir.12.1.21292.

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Solar module efficiency and dependability can be improved by detecting faults and monitoring their condition. This study examines the limitations and challenges of diagnosing solar module malfunctions. The various issues associated with solar module failure are thoroughly discussed. A monitoring tool that combines thermography and intelligent computing is developed to detect issues in photovoltaic cells while filtering out trivial anomalies based on a review of relevant studies. Given the rapid growth of solar energy, the Photovoltaic (PV) component's fault detection plays a crucial role in en

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Bello, Rotimi-Williams, Pius A. Owolawi, Chunling Tu, and Etienne A. van Wyk. "Automated fault detection and analysis for large photovoltaic systems using photovoltaic module fault detection in drone vision system." Edelweiss Applied Science and Technology 9, no. 2 (2025): 603–26. https://doi.org/10.55214/25768484.v9i2.4542.

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This study presents an innovative approach to fault detection in large-scale photovoltaic (PV) systems by leveraging the capabilities of drones and machine vision technologies. The proposed method is unsupervised, eliminating the need for manual intervention in identifying and analyzing faults in PV installations. By employing drone vision techniques equipped with high-resolution cameras and advanced image processing algorithms, comprehensive visual data of solar panels were captured. The collected images were processed for automatic detection and classification of various faults such as crack

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Aissat, Miloud Elhadj Ali, Abdelkader Mostefa, Mohamed Khodja, Karim Belalia, and Bouziane Meliani. "Detection of partial shading and short-circuit faults in a pv system using the artificial neural network multilayer perceptron technique." STUDIES IN ENGINEERING AND EXACT SCIENCES 5, no. 2 (2024): e11814. https://doi.org/10.54021/seesv5n2-684.

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Photovoltaic (PV) systems play a crucial role in renewable energy production but face challenges due to environmental factors such as partial shading and short circuits, which reduce efficiency and reliability. Continuous monitoring and early fault detection are essential to mitigate these issues. In this work, we propose a fault detection method that focuses on identifying partial shading and short-circuit faults using an artificial neural network (ANN), specifically a Multi-Layer Perceptron (MLP). The MLP model is trained to classify these faults under various environmental scenarios, includ

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Pei, Tingting, and Xiaohong Hao. "A Fault Detection Method for Photovoltaic Systems Based on Voltage and Current Observation and Evaluation." Energies 12, no. 9 (2019): 1712. http://dx.doi.org/10.3390/en12091712.

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Photovoltaic (PV) power generation systems work chronically in various climatic outdoor conditions, therefore, faults may occur within the PV arrays in PV systems. Online fault detection for the PV arrays are important to improve the system’s reliability, safety and efficiency. In view of this, a fault-detection method based on voltage and current observation and evaluation is presented in this paper to detect common PV array faults, such as open-circuit, short-circuit, degradation and shading faults. In order to develop this detection method, fault characteristic quantities (e.g., the open-ci

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Thakfan, Ali, and Yasser Bin Salamah. "Artificial-Intelligence-Based Detection of Defects and Faults in Photovoltaic Systems: A Survey." Energies 17, no. 19 (2024): 4807. http://dx.doi.org/10.3390/en17194807.

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The global shift towards sustainable energy has positioned photovoltaic (PV) systems as a critical component in the renewable energy landscape. However, maintaining the efficiency and longevity of these systems requires effective fault detection and diagnosis mechanisms. Traditional methods, relying on manual inspections and standard electrical measurements, have proven inadequate, especially for large-scale solar installations. The emergence of machine learning (ML) and deep learning (DL) has sparked significant interest in developing computational strategies to enhance the identification and

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Hussain, Imran, Ihsan Ullah Khalil, Aqsa Islam, et al. "Unified Fuzzy Logic Based Approach for Detection and Classification of PV Faults Using I-V Trend Line." Energies 15, no. 14 (2022): 5106. http://dx.doi.org/10.3390/en15145106.

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Solar photovoltaic PV plants worldwide are continuously monitored and carefully protected to ensure safe and reliable operation through detecting and isolating faults. Faults are very common in modern solar PV systems which interrupt normal system operation adversely affecting the performance of the PV systems. When undetected, faults not only cause significant reduction in the efficiency and life span of the PV system, but also result in damage and fire hazards compromising their reliability. Therefore, early fault detection and diagnosis of photovoltaic plants is a necessity for safe and rel

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Mahjabeen, Farhana. "Intelligence in Photovoltaic Fault Identification and Diagnosis: A Systematic Review." Formosa Journal of Science and Technology 3, no. 10 (2024): 2397–406. http://dx.doi.org/10.55927/fjst.v3i10.11552.

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Undetected photovoltaic system faults can lead to significant energy losses, often exceeding 10%, necessitating efficient fault detection and diagnosis. Artificial intelligence, particularly machine learning and deep learning, offers promising solutions for real-time, high-volume fault detection and complex pattern recognition in PV systems. This research analyzes various PV fault detection studies, examining their objectives, methods, results, and the prevalence of ML/DL approaches. The analysis highlights the application of both classical ML algorithms, such as K-Nearest Neighbors and Random

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Gaaloul, Yasmine, Olfa Bel Hadj Brahim Kechiche, Houcine Oudira, et al. "Faults Detection and Diagnosis of a Large-Scale PV System by Analyzing Power Losses and Electric Indicators Computed Using Random Forest and KNN-Based Prediction Models." Energies 18, no. 10 (2025): 2482. https://doi.org/10.3390/en18102482.

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Accurate and reliable fault detection in photovoltaic (PV) systems is essential for optimizing their performance and durability. This paper introduces a novel approach for fault detection and diagnosis in large-scale PV systems, utilizing power loss analysis and predictive models based on Random Forest (RF) and K-Nearest Neighbors (KNN) algorithms. The proposed methodology establishes a predictive baseline model of the system’s healthy behavior under normal operating conditions, enabling real-time detection of deviations between expected and actual performance. Faults such as string disconnect

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Toche Tchio, Guy M., Joseph Kenfack, Joseph Voufo, Yves Abessolo Mindzie, Blaise Fouedjou Njoya, and Sanoussi S. Ouro-Djobo. "Diagnosing faults in a photovoltaic system using the Extra Trees ensemble algorithm." AIMS Energy 12, no. 4 (2024): 727–50. http://dx.doi.org/10.3934/energy.2024034.

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The application of machine learning techniques for monitoring and diagnosing faults in photovoltaic (PV) systems has been shown to enhance the reliability of PV power generation. This research introduced a novel machine learning classifier for fault diagnosis in PV systems, utilizing an ensemble algorithm known as extra trees (ETC). The study initially proposed a system with two PV modules and developed a low-cost Arduino-based data logger to gather data from the PV system in free-fault and faulty conditions. Subsequently, the study evaluated six other advanced classifiers for fault diagnosis

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Lazzaretti, André Eugênio, Clayton Hilgemberg da Costa, Marcelo Paludetto Rodrigues, et al. "A Monitoring System for Online Fault Detection and Classification in Photovoltaic Plants." Sensors 20, no. 17 (2020): 4688. http://dx.doi.org/10.3390/s20174688.

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Photovoltaic (PV) energy use has been increasing recently, mainly due to new policies all over the world to reduce the application of fossil fuels. PV system efficiency is highly dependent on environmental variables, besides being affected by several kinds of faults, which can lead to a severe energy loss throughout the operation of the system. In this sense, we present a Monitoring System (MS) to measure the electrical and environmental variables to produce instantaneous and historical data, allowing to estimate parameters that ar related to the plant efficiency. Additionally, using the same

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Sabah Muttashar, Halah, and Amina Mahmoud Shakir. "Enhancing PV Fault Detection Using Machine Learning: Insights from a Simulated PV System." Iraqi Journal for Electrical and Electronic Engineering 21, no. 1 (2024): 126–33. https://doi.org/10.37917/ijeee.21.1.12.

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Recently, numerous researches have emphasized the importance of professional inspection and repair in case of suspected faults in Photovoltaic (PV) systems. By leveraging electrical and environmental features, many machine learning models can provide valuable insights into the operational status of PV systems. In this study, different machine learning models for PV fault detection using a simulated 0.25 MW PV power system were developed and evaluated. The training and testing datasets encompassed normal operation and various fault scenarios, including string-to-string, on-string, and string-to

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Joseph, Easter, Pradeep Menon Vijaya Kumar, Balbir Singh Mahinder Singh, and Dennis Ling Chuan Ching. "Performance Monitoring Algorithm for Detection of Encapsulation Failures and Cell Corrosion in PV Modules." Energies 16, no. 8 (2023): 3391. http://dx.doi.org/10.3390/en16083391.

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This research work aims to develop a fault detection and performance monitoring system for a photovoltaic (PV) system that can detect and communicate errors to the user. The proposed system uses real-time data from various sensors to identify performance problems and faults in the PV system, particularly for encapsulation failure and module corrosion. The system incorporates a user interface that operates on a micro-computer utilizing Python software to show the detected errors from the PV miniature scale system. Fault detection is achieved by comparing the One-diode model with a controlled st

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Emamian, Masoud, Aref Eskandari, Mohammadreza Aghaei, Amir Nedaei, Amirmohammad Moradi Sizkouhi, and Jafar Milimonfared. "Cloud Computing and IoT Based Intelligent Monitoring System for Photovoltaic Plants Using Machine Learning Techniques." Energies 15, no. 9 (2022): 3014. http://dx.doi.org/10.3390/en15093014.

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This paper proposes an Intelligent Monitoring System (IMS) for Photovoltaic (PV) systems using affordable and cost-efficient hardware and also lightweight software that is capable of being easily implemented in different locations and having the capability to be installed in different types of PV power plants. IMS uses the Internet of Things (IoT) platform for handling data as well as Interoperability and Communication among the devices and components in the IMS. Moreover, IMS includes a personal cloud server for computing and storing the acquired data of PV systems. The IMS also consists of a

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Jhan, Jia-Zhang, Bo-Hong Li, Hsun-Tsung Chiu, Hong-Chan Chang, and Cheng-Chien Kuo. "A Heuristic Algorithm for Locating Line-to-Line Faults in Photovoltaic Systems." Applied Sciences 15, no. 11 (2025): 6366. https://doi.org/10.3390/app15116366.

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Photovoltaic (PV) systems have experienced rapid global deployment. However, line-to-line short-circuit faults pose serious safety risks and can lead to significant power losses or fire hazards. While existing fault detection methods can identify fault types, they cannot precisely locate fault positions, resulting in time-consuming and costly maintenance. This paper proposes a heuristic algorithm for accurately locating such faults in PV arrays based on module group voltage measurements. The algorithm employs a two-phase approach: fault candidate marking and fault location determination, capab

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Alqaraghuli, Omar, and Abdullahi Ibrahim. "Optimizing Photovoltaic System Diagnostics: Integrating Machine Learning and DBFLA for Advanced Fault Detection and Classification." Electronics 14, no. 8 (2025): 1495. https://doi.org/10.3390/electronics14081495.

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The rapid growth in photovoltaic (PV) power plant installations has rendered traditional inspection methods inefficient, necessitating advanced approaches for fault detection and classification. This study introduces a novel hybrid metaheuristic method, the Dung Beetle Optimization Algorithm combined with Fick’s Law of Diffusion Algorithm (DBFLA), to address these challenges. The DBFLA enhances the performance of machine learning models, including artificial neural networks (ANNs), support vector machines (SVMs), and ensemble methods, by fine-tuning their parameters to improve fault detection

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Fares, Noureddine, Chouaib Souaidia, and Tawfik Thelaidjia. "Extreme learning machine based on BDE feature selection to detect fault scenarios in grid-connected PV systems under MPPT mode." STUDIES IN ENGINEERING AND EXACT SCIENCES 5, no. 2 (2024): e9473. http://dx.doi.org/10.54021/seesv5n2-362.

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This paper considers real-time data-driven adaptive fault detection (FD) in grid-connected PV (GPV) systems under maximum power point tracking (MPPT) modes during large variations. Faults under MPPT modes remain undetected for longer periods, introducing new protection challenges and threats to the system. An intelligent FD algorithm is developed through real-time multi-sensor measurements and virtual Micro Phasor Measurement Unit (Micro-PMU) estimations. The high-dimensional and high-frequency multivariate features vary over time, and computational efficiency becomes crucial to realizing onli

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Qasim Obaidi, Marwah, and Nabil Derbel. "IoT-based monitoring and shading faults detection for a PV water pumping system using deep learning approach." Bulletin of Electrical Engineering and Informatics 12, no. 5 (2023): 2673–81. http://dx.doi.org/10.11591/eei.v12i5.4496.

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One of the major challenges facing photovoltaic (PV) systems is fault detection. Artificial intelligence (AI) is one of the main popular techniques used in error detection due to its ability to extract signal and image features. In this paper, a deep learning approach based on convolutional neural network (CNN) and internet of things (IoT) technology are used to detect and locate shading faults for a PV water pumping system. The current and voltage signals generated by the PV panels as well as temperature and radiation were used to convert them into 3D images and then upload to a deep learning

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Mahjabeen, Farhana. "Intelligence in Photovoltaic Fault Identification and Diagnosis: A Systematic Review." Formosa Journal of Applied Sciences 3, no. 10 (2024): 4175–84. http://dx.doi.org/10.55927/fjas.v3i10.11536.

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The increasing global demand for renewable energy has propelled the adoption of photovoltaic systems as a key component of sustainable energy infrastructure. Undetected photovoltaic system faults can lead to significant energy losses, often exceeding 10%, necessitating efficient fault detection and diagnosis. Artificial intelligence, particularly machine learning and deep learning, offers promising solutions for real-time, high-volume fault detection and complex pattern recognition in PV systems. This research analyzes various PV fault detection studies, examining their objectives, methods, re

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Alam, Zaheer, Malak Adnan Khan, Zain Ahmad Khan, et al. "Fault Diagnosis Strategy for a Standalone Photovoltaic System: A Residual Formation Approach." Electronics 12, no. 2 (2023): 282. http://dx.doi.org/10.3390/electronics12020282.

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The search for sustainability and green energy, in electricity production, has lead many researchers to study and improve photovoltaic (PV) systems. The PV systems, being a combination of power electronic modules and PV array, have high tendency of faults in sensors, switches, and passive devices. Thus, a reliable fault diagnosis (FD) scheme plays a significant role in protecting PV systems. In this article, a sliding mode observer (SMO)-based FD scheme is presented to figure out the sensor faults in a standalone PV system. The proposed FD scheme makes use of residual formation which in turn h

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Quiles-Cucarella, Eduardo, Pedro Sánchez-Roca, and Ignacio Agustí-Mercader. "Performance Optimization of Machine-Learning Algorithms for Fault Detection and Diagnosis in PV Systems." Electronics 14, no. 9 (2025): 1709. https://doi.org/10.3390/electronics14091709.

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The early detection of faults in photovoltaic (PV) systems is crucial for ensuring efficiency, minimizing energy losses, and extending operational lifespan. This study evaluates and compares multiple machine-learning models for fault diagnosis in PV systems, analyzing their performance across different fault types and operational modes. A dataset comprising 2.2 million measurements from a laboratory-based PV model, covering seven fault categories—including inverter failures, partial shading, and sensor faults—is used for training and validation. Models are assessed under both Maximum Power Poi

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Kumar, Prakash. "Protection Challenges and Fault Diagnosis in PV Systems: A Critical Review." International Journal for Research in Applied Science and Engineering Technology 13, no. 2 (2025): 857–61. https://doi.org/10.22214/ijraset.2025.66985.

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With the rapid expansion of global photovoltaic (PV) power capacity, ensuring the protection of PV systems has become increasingly crucial over the past few decades. Despite the incorporation of standard protection devices, certain faults within PV arrays may go undetected. Motivated by the growing demand for reliable fault detection methods, numerous advanced techniques have been proposed in recent years. This paper provides a comprehensive analysis of various fault occurrences, the challenges associated with protection, and the potential consequences of undetected faults in PV systems. Addit

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Raeisi, H. A., and S. M. Sadeghzadeh. "A Novel Experimental and Approach of Diagnosis, Partial Shading, and Fault Detection for Domestic Purposes Photovoltaic System Using Data Exchange of Adjacent Panels." International Journal of Photoenergy 2021 (September 17, 2021): 1–19. http://dx.doi.org/10.1155/2021/9956433.

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This paper presents a new detection method of fault and partial shading condition (PSC) in a photovoltaic (PV) domestic network, considering maximum power point tracking (MPPT). The MPPT has been executed by employing a boost converter using particle swarm optimization (PSO) technique. The system is composed of two photovoltaic arrays. Each PV array contains three panels connected in series, including distinct MPPT. The PSC detection exploits the neighboring PV system data. This suggested innovative algorithm is proficient in detecting these subjects: (a) fault, (b) partial shading condition,

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Hichri, Amal, Mansour Hajji, Majdi Mansouri, et al. "Genetic-Algorithm-Based Neural Network for Fault Detection and Diagnosis: Application to Grid-Connected Photovoltaic Systems." Sustainability 14, no. 17 (2022): 10518. http://dx.doi.org/10.3390/su141710518.

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Modern photovoltaic (PV) systems have received significant attention regarding fault detection and diagnosis (FDD) for enhancing their operation by boosting their dependability, availability, and necessary safety. As a result, the problem of FDD in grid-connected PV (GCPV) systems is discussed in this work. Tools for feature extraction and selection and fault classification are applied in the developed FDD approach to monitor the GCPV system under various operating conditions. This is addressed such that the genetic algorithm (GA) technique is used for selecting the best features and the artif

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Yang, Nien-Che, and Harun Ismail. "Voting-Based Ensemble Learning Algorithm for Fault Detection in Photovoltaic Systems under Different Weather Conditions." Mathematics 10, no. 2 (2022): 285. http://dx.doi.org/10.3390/math10020285.

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A photovoltaic (PV) system is one of the renewable energy resources that can help in meeting the ever-increasing energy demand. However, installation of PV systems is prone to faults that can occur unpredictably and remain challenging to detect. Major PV faults that can occur are line-line and open circuits faults, and if they are not addressed appropriately and timely, they may lead to serious problems in the PV system. To solve this problem, this study proposes a voting-based ensemble learning algorithm with linear regression, decision tree, and support vector machine (EL-VLR-DT-SVM) for PV

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Et-taleby, Abdelilah, Yassine Chaibi, Mohamed Benslimane, and Mohammed Boussetta. "Applications of Machine Learning Algorithms for Photovoltaic Fault Detection: a Review." Statistics, Optimization & Information Computing 11, no. 1 (2023): 168–77. http://dx.doi.org/10.19139/soic-2310-5070-1537.

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Over the years, the boom of technology has caused the accumulation of a large amount of data, famously known as big data, in every field of life. Traditional methods have failed to analyse such a huge pile of data due to outdated techniques. In recent times, the use of photovoltaic systems has risen worldwide. The arena Photovoltaic (PV) system has witnessed the same unprecedented expansion of data owing to the associated monitoring systems. However, the faults created within the PV system cannot be detected, classified, or predicted by using conventional techniques. This necessitates the use

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IBK, Sugirianta, IGNA Dwijaya_S, M Purbhawa, GK Sri Budarsa, and Ketut Ta. "Short and Open Circuit Fault Detection in On-Grid Photovoltaic Systems 1MWP Bangli Based on Current and Voltage Observation." Journal of Computer Science and Technology Studies 4, no. 2 (2022): 105–17. http://dx.doi.org/10.32996/jcsts.2022.4.2.13.

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Photovoltaic (PV) systems as clean and green electrical energy generators have increased sharply in the last 10 years. The installation of a PV system in an open area is one of the causes of frequent faults/damage to the PV system. Fault in the PV system causes a decrease in efficiency, weak reliability, and disruption of the continuity of the electrical power distribution, which in turn causes the low performance of the system. This research does on the on-grid PV system 1MWp Bangli which consists of 278 PV arrays and 5004 monocrystalline solar modules 200Wp, which started operation in 2013.

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Salman Zamzeer, Ali, Mansour S. Farhan, and Haider TH ALRikabi. "Fault Detection System of Photovoltaic Based on Artificial Neural Network." Wasit Journal of Engineering Sciences 11, no. 1 (2023): 93–104. http://dx.doi.org/10.31185/ejuow.vol11.iss1.399.

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Using PV systems, solar energy may be used to create electricity. Every year, the proportion of solar energy in the electric system increases significantly. On the other hand, photovoltaic cells are susceptible to malfunctions that diminish their efficiency and profitability. Due to the severity of the defects, fault detection and diagnosis (FDD) in the PV system have become difficult. Thus, the primary objective of the proposed study is to detect and diagnose particular types of PV system problems using an artificial neural network (ANN). This early operation is more effective for avoiding er

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Park, Sunme, Soyeong Park, Myungsun Kim, and Euiseok Hwang. "Clustering-Based Self-Imputation of Unlabeled Fault Data in a Fleet of Photovoltaic Generation Systems." Energies 13, no. 3 (2020): 737. http://dx.doi.org/10.3390/en13030737.

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This work proposes a fault detection and imputation scheme for a fleet of small-scale photovoltaic (PV) systems, where the captured data includes unlabeled faults. On-site meteorological information, such as solar irradiance, is helpful for monitoring PV systems. However, collecting this type of weather data at every station is not feasible for a fleet owing to the limitation of installation costs. In this study, to monitor a PV fleet efficiently, neighboring PV generation profiles were utilized for fault detection and imputation, as well as solar irradiance. For fault detection from unlabeled

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Lebreton, Carole, Fabrice Kbidi, Alexandre Graillet, et al. "PV System Failures Diagnosis Based on Multiscale Dispersion Entropy." Entropy 24, no. 9 (2022): 1311. http://dx.doi.org/10.3390/e24091311.

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Photovoltaic (PV) system diagnosis is a growing research domain likewise solar energy’s ongoing significant expansion. Indeed, efficient Fault Detection and Diagnosis (FDD) tools are crucial to guarantee reliability, avoid premature aging and improve the profitability of PV plants. In this paper, an on-line diagnosis method using the PV plant electrical output is presented. This entirely signal-based method combines variational mode decomposition (VMD) and multiscale dispersion entropy (MDE) for the purpose of detecting and isolating faults in a real grid-connected PV plant. The present method

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Rivai, Ahmad, Nasrudin Abd Rahim, Mohamad Fathi Mohamad Elias, and Jafferi Jamaludin. "Analysis of Photovoltaic String Failure and Health Monitoring with Module Fault Identification." Energies 13, no. 1 (2019): 100. http://dx.doi.org/10.3390/en13010100.

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In this paper, photovoltaic (PV) string failure analysis and health monitoring of PV modules based on a low-cost self-powered wireless sensor network (WSN) are presented. Simple and effective fault detection and diagnosis method based on the real-time operating voltage of PV modules is proposed. The proposed method is verified using the developed health monitoring system which is installed in a grid-connected PV system. Each of the PV modules is monitored via WSN to detect any individual faulty module. The analysis of PV string failure includes several electrical fault scenarios and their impa

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D., Balakrishnan, Raja J., Manikandan Rajagopal, Sudhakar K., and Janani K. "An IoT-Based System for Fault Detection and Diagnosis in Solar PV Panels." E3S Web of Conferences 387 (2023): 05009. http://dx.doi.org/10.1051/e3sconf/202338705009.

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This abstract describes an IoT-based system for fault detection and diagnosis in solar PV panels. The proposed Fuzzy logic-based fault detection algorithms aims to improve the performance and reliability of solar PV panels, which can be affected by various faults such as shading, soiling, degradation, and electrical faults. The system includes wireless sensor nodes that are deployed on the panels to collect data on their electrical parameters and environmental conditions, such as temperature, irradiance, and humidity. The collected data is then transmitted to a central server for processing an

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Navid, Qamar, Ahmed Hassan, Abbas Ahmad Fardoun, and Rashad Ramzan. "An Online Novel Two-Layered Photovoltaic Fault Monitoring Technique Based Upon the Thermal Signatures." Sustainability 12, no. 22 (2020): 9607. http://dx.doi.org/10.3390/su12229607.

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The share of photovoltaic (PV) power generation in the energy mix is increasing at a rapid pace with dramatically increasing capacity addition through utility-scale PV power plants globally. As PV plants are forecasted to be a major energy generator in the future, their reliable operation remains of primary concern due to a possibility of faults in a tremendously huge number of PV panels involved in power generation in larger plants. The precise detection of nature and the location of the faults along with a prompt remedial mechanism is deemed crucial for smoother power plant operation. The ex

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Wang, Lina, Ehtisham Lodhi, Pu Yang, et al. "Adaptive Local Mean Decomposition and Multiscale-Fuzzy Entropy-Based Algorithms for the Detection of DC Series Arc Faults in PV Systems." Energies 15, no. 10 (2022): 3608. http://dx.doi.org/10.3390/en15103608.

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DC series arc fault detection is essential for improving the productivity of photovoltaic (PV) stations. The DC series arc fault also poses severe fire hazards to the solar equipment and surrounding building. DC series arc faults must be detected early to provide reliable and safe power delivery while preventing fire hazards. However, it is challenging to detect DC series arc faults using conventional overcurrent and current differential methods because these faults produce only minor current variations. Furthermore, it is hard to define their characteristics for detection due to the randomnes

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Natsheh, Emad, and Sufyan Samara. "Tree Search Fuzzy NARX Neural Network Fault Detection Technique for PV Systems with IoT Support." Electronics 9, no. 7 (2020): 1087. http://dx.doi.org/10.3390/electronics9071087.

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The photovoltaic (PV) panel’s output energy depends on many factors. As they are becoming the leading alternative energy source, it is essential to get the best out of them. Although the main factor for maximizing energy production is proportional to the amount of solar radiation reaching the photovoltaic panel surface, other factors, such as temperature and shading, influence them negatively. Moreover, being installed in a dynamic and frequently harsh environment causes a set of reasons for faults, defects, and irregular operations. Any irregular operation should be recognized and classified

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Reda, Djeghader, Louahem Msabah Ilyes, Benzahioul Samia, and Metatla Abderrezak. "Fault diagnosis of a photovoltaic system using recurrent neural networks." Bulletin of Electrical Engineering and Informatics 11, no. 6 (2022): 3079~3090. https://doi.org/10.11591/eei.v11i6.4295.

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The developed work in this paper is a part of the detection and identification of faults in systems by modern techniques of artificial intelligence. In a first step we have developed amulti-layer perceptron (MLP), type neural network to detect shunt faults and shading phenomenon in photovoltaic (PV) systems, and in the second part of the work we developed anotherrecurrent neural network (RNN) type network in order to identify single and combined faults in PV systems. The results obtained clearly show the performance of the networks developed for the rapid detection of the appearance of faults

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Ibrahim, Mohammed Salah, Hussein K. Almulla, Anas D. Sallibi, Ahmed Adil Nafea, Aythem Khairi Kareem, and Khattab M. Ali Alheeti. "Enhanced fault detection in photovoltaic systems using an ensemble machine learning approach." International Journal of Reconfigurable and Embedded Systems (IJRES) 14, no. 2 (2025): 507. https://doi.org/10.11591/ijres.v14.i2.pp507-517.

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Malfunctioning of photovoltaic (PV) systems is a main issue affecting solar panels and other related components. Detecting such issues early leads to efficient energy production with low maintenance costs and high system performance consistency. This paper proposed an ensemble model (EM) for fault detection (FD) in PV systems. The proposed model utilized advanced machine learning algorithms containing random forest (RF), k-nearest neighbors (KNN), and gradient boosting (GB). Traditional approaches often do not handle the several situations that PV systems can have. Our EM leveraged the power o

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P., Maruthupandi, and Vikram G.D. "Metaheuristic Techniques Based Detection of Faults in a Photovoltaic System Under Partial Shading Condition – A Review." Recent Research Reviews Journal 4, no. 1 (2025): 1–15. https://doi.org/10.36548/rrrj.2025.1.001.

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The growing demand for renewable energy has led to increased adoption of photovoltaic (PV) systems. However, their efficiency and reliability are significantly affected by partial shading conditions (PSCs), which cause power losses and fault occurrences. Traditional fault detection methods often fail to provide accurate and timely identification of shading-induced issues. To address this challenge, metaheuristic techniques have emerged as effective solutions due to their optimization capabilities in complex, nonlinear environments. This review explores various metaheuristic-based fault detecti

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Jenitha, P., and A. Immanuel Selvakumar. "Fault detection in PV systems." Applied Solar Energy 53, no. 3 (2017): 229–37. http://dx.doi.org/10.3103/s0003701x17030069.

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Suliman, Fouad, Fatih Anayi, and Michael Packianather. "Electrical Faults Analysis and Detection in Photovoltaic Arrays Based on Machine Learning Classifiers." Sustainability 16, no. 3 (2024): 1102. http://dx.doi.org/10.3390/su16031102.

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Solar photovoltaic energy generation has garnered substantial interest owing to its inherent advantages, such as zero pollution, flexibility, sustainability, and high reliability. Ensuring the efficient functioning of PV power facilities hinges on precise fault detection. This not only bolsters their reliability and safety but also optimizes profits and avoids costly maintenance. However, the detection and classification of faults on the Direct Current (DC) side of the PV system using common protection devices present significant challenges. This research delves into the exploration and analys

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Pang, Ruiwen, and Wenfang Ding. "Series Arc Fault Characteristics and Detection Method of a Photovoltaic System." Energies 16, no. 24 (2023): 8016. http://dx.doi.org/10.3390/en16248016.

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The DC arc is the main cause of fire in photovoltaic (PV) systems. This is due to the fact that the DC arc has no zero-crossing point and is prone to stable combustion. Failure to detect it in a timely manner can seriously endanger the PV system. This study analyzes the influences of the series arc and the maximum power point tracking (MPPT) algorithm on the PV output characteristics based on the PV equivalent circuit module. The PV voltage and current variation characteristics are obtained when the series arc occurs. The findings indicate that the input voltage of the converter remains unchan

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