Fault diagnosis of power converters in a grid connected photovoltaic system using artificial neural networks
Introduction. The widespread use of photovoltaic systems in various applications has spotlighted the pressing requirement for reliability, efficiency and continuity of service. The main impediment to a more effective implementation has been the reliability of the power converters. Indeed, the presen...
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| Date: | 2023 |
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National Technical University "Kharkiv Polytechnic Institute" and Аnatolii Pidhornyi Institute of Power Machines and Systems of NAS of Ukraine
2023
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eiekhpieduua-article-2574892023-01-04T13:48:14Z Fault diagnosis of power converters in a grid connected photovoltaic system using artificial neural networks Fault diagnosis of power converters in a grid connected photovoltaic system using artificial neural networks Mimouni, A. Laribi, S. Sebaa, M. Allaoui, T. Bengharbi, A. A. фотогальванічна система, підключена до мережі штучна нейронна мережа силові перетворювачі відмова IGBT при обриві кола виявлення несправностей grid connected photovoltaic system artificial neural network power converters open circuit failure of IGBT fault detection Introduction. The widespread use of photovoltaic systems in various applications has spotlighted the pressing requirement for reliability, efficiency and continuity of service. The main impediment to a more effective implementation has been the reliability of the power converters. Indeed, the presence of faults in power converters that can cause malfunctions in the photovoltaic system, which can reduce its performance. Novelty. This paper presents a technique for diagnosing open circuit failures in the switches (IGBTs) of power converters (DC-DC converters and three-phase inverters) in a grid-connected photovoltaic system. Purpose. To ensure supply continuity, a fault-diagnosis process is required throughout all phases of energy production, transfer, and conversion. Methods. The diagnostic approach is based on artificial neural networks and the extraction of features corresponding to the open circuit fault of the IGBT switch. This approach is based on the Clarke transformation of the three-phase currents of the inverter output as well as the calculation of the average value of these currents to determine the exact angle of the open circuit fault. Results. This method is able to effectively identify and localize single or multiple open circuit faults of the DC-DC converter IGBT switch or the three-phase inverter IGBT switches. Вступ. Широке використання фотоелектричних систем у різних застосуваннях висунуло на перший план нагальні вимоги до надійності, ефективності та безперервності обслуговування. Основною перешкодою для ефективнішого застосування була надійність силових перетворювачів. Справді, наявність несправностей у силових перетворювачах може спричинити збої в роботі фотоелектричної системи, що може знизити її продуктивність. Новизна. У цій статті представлена методика діагностики обриву кола в перемикачах (IGBT) силових перетворювачів (перетворювачів постійного струму та трифазних інверторів) у фотоелектричній системі, підключеній до мережі. Мета. Для забезпечення безперервності постачання потрібен процес діагностики несправностей на всіх етапах виробництва, передачі та перетворення енергії. Методи. Діагностичний підхід заснований на штучних нейронних мережах та вилучення ознак, що відповідають обриву кола IGBT-перемикача. Цей підхід ґрунтується на перетворенні Кларка трифазних струмів на виході інвертора, а т акож розрахунку середнього значення цих струмів для визначення точного кута обриву кола. Результати. Цей метод дозволяє ефективно ідентифікувати та локалізувати одиночні або множинні несправності розімкнутого кола IGBT-перемикача DC-DC перетворювача або IGBT-перемикача трифазного інвертора. National Technical University "Kharkiv Polytechnic Institute" and Аnatolii Pidhornyi Institute of Power Machines and Systems of NAS of Ukraine 2023-01-04 Article Article application/pdf http://eie.khpi.edu.ua/article/view/257489 10.20998/2074-272X.2023.1.04 Electrical Engineering & Electromechanics; No. 1 (2023); 25-30 Электротехника и Электромеханика; № 1 (2023); 25-30 Електротехніка і Електромеханіка; № 1 (2023); 25-30 2309-3404 2074-272X en http://eie.khpi.edu.ua/article/view/257489/266657 Copyright (c) 2022 A. Mimouni, S. Laribi, M. Sebaa, T. Allaoui, A. A. Bengharbi http://creativecommons.org/licenses/by-nc/4.0 |
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Electrical Engineering & Electromechanics |
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| datestamp_date |
2023-01-04T13:48:14Z |
| collection |
OJS |
| language |
English |
| topic |
grid connected photovoltaic system artificial neural network power converters open circuit failure of IGBT fault detection |
| spellingShingle |
grid connected photovoltaic system artificial neural network power converters open circuit failure of IGBT fault detection Mimouni, A. Laribi, S. Sebaa, M. Allaoui, T. Bengharbi, A. A. Fault diagnosis of power converters in a grid connected photovoltaic system using artificial neural networks |
| topic_facet |
фотогальванічна система підключена до мережі штучна нейронна мережа силові перетворювачі відмова IGBT при обриві кола виявлення несправностей grid connected photovoltaic system artificial neural network power converters open circuit failure of IGBT fault detection |
| format |
Article |
| author |
Mimouni, A. Laribi, S. Sebaa, M. Allaoui, T. Bengharbi, A. A. |
| author_facet |
Mimouni, A. Laribi, S. Sebaa, M. Allaoui, T. Bengharbi, A. A. |
| author_sort |
Mimouni, A. |
| title |
Fault diagnosis of power converters in a grid connected photovoltaic system using artificial neural networks |
| title_short |
Fault diagnosis of power converters in a grid connected photovoltaic system using artificial neural networks |
| title_full |
Fault diagnosis of power converters in a grid connected photovoltaic system using artificial neural networks |
| title_fullStr |
Fault diagnosis of power converters in a grid connected photovoltaic system using artificial neural networks |
| title_full_unstemmed |
Fault diagnosis of power converters in a grid connected photovoltaic system using artificial neural networks |
| title_sort |
fault diagnosis of power converters in a grid connected photovoltaic system using artificial neural networks |
| title_alt |
Fault diagnosis of power converters in a grid connected photovoltaic system using artificial neural networks |
| description |
Introduction. The widespread use of photovoltaic systems in various applications has spotlighted the pressing requirement for reliability, efficiency and continuity of service. The main impediment to a more effective implementation has been the reliability of the power converters. Indeed, the presence of faults in power converters that can cause malfunctions in the photovoltaic system, which can reduce its performance. Novelty. This paper presents a technique for diagnosing open circuit failures in the switches (IGBTs) of power converters (DC-DC converters and three-phase inverters) in a grid-connected photovoltaic system. Purpose. To ensure supply continuity, a fault-diagnosis process is required throughout all phases of energy production, transfer, and conversion. Methods. The diagnostic approach is based on artificial neural networks and the extraction of features corresponding to the open circuit fault of the IGBT switch. This approach is based on the Clarke transformation of the three-phase currents of the inverter output as well as the calculation of the average value of these currents to determine the exact angle of the open circuit fault. Results. This method is able to effectively identify and localize single or multiple open circuit faults of the DC-DC converter IGBT switch or the three-phase inverter IGBT switches. |
| publisher |
National Technical University "Kharkiv Polytechnic Institute" and Аnatolii Pidhornyi Institute of Power Machines and Systems of NAS of Ukraine |
| publishDate |
2023 |
| url |
http://eie.khpi.edu.ua/article/view/257489 |
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2025-07-17T11:49:06Z |
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2025-07-17T11:49:06Z |
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