Fault detection and diagnosis of photovoltaic system based on neural networks approach

• Autorzy: Rahmoune Ben Mohamed , Iratni Abdelhamid , Amari Amel Sabrine , Hafaifa Ahmed , Colak Ilhami

Identyfikatory

DOI

10.29354/diag/166428

• Języki publikacji: EN

Abstrakty

EN

Solar energy has become one of the most important renewable energies in the world. With the increasing installation of power plants in the world, the supervision and diagnosis of photovoltaic systems have become an important challenge with the increased occurrence of various internal and external faults. Indeed, this work proposes a new solar power plant diagnosis based on the artificial neural network approach. The developed model was to improve the performance and reliability of the power plant located in Tamanrasset, Algeria, which is subjected to varying weather conditions in terms of radiation and ambient temperature. By using the real data collected from the studied system, this approach allow to increase electricity production and address any issues that may arise quickly, ensuring uninterrupted power supply for the region. Neural networks have shown interesting results with high accuracy. This fault diagnosis approach allows to determine the time of occurrence of a fault affecting the examined PV system. Also, allow an early detection of failures and degradation of the system, which contributes to improving the productivity of this photovoltaic installation. With a significant reduction in the time needed to repair the damage caused by these faults and improve the reliability and continuity of the electrical energy production service.

Słowa kluczowe

EN

photovoltaic system; fault detection; neural networks; diagnostic system; residue evaluation

PL

system fotowoltaiczny; wykrywanie uszkodzeń; sieci neuronowe; system diagnostyczny

• Wydawca: Polskie Towarzystwo Diagnostyki Technicznej
• Czasopismo: Diagnostyka
• Rocznik: 2023
• Tom: Vol. 24, No. 3
• Strony: art. no. 2023303
• Opis fizyczny: Bibliogr. 34 poz., rys.

Twórcy

autor

Rahmoune Ben Mohamed

• Department of Sciences and Technology, Faulty of Sciences and Technology, University of Tamanrasset, Algeria
• Applied Automation and Industrial Diagnostics Laboratory, Faculty of Science and Technology, University of Djelfa 17000 DZ, Algeria

• https://orcid.org/0009-0000-7099-8740

autor

Iratni Abdelhamid

• Faculty of Science and Technology, University Mohamed El Bachir El Ibrahimi of Bordj Bou Arreridj, 34030 DZ, Algeria

autor

Amari Amel Sabrine

• Applied Automation and Industrial Diagnostics Laboratory, Faculty of Science and Technology, University of Djelfa 17000 DZ, Algeria

autor

Hafaifa Ahmed

• Applied Automation and Industrial Diagnostics Laboratory, Faculty of Science and Technology, University of Djelfa 17000 DZ, Algeria
• Department of Electrical and Electronics Engineering, Nisantasi University, 34398 Sarıyer, İstanbul, Turkey

autor

Colak Ilhami

• Department of Electrical and Electronics Engineering, Nisantasi University, 34398 Sarıyer, İstanbul, Turkey

Bibliografia

• 1. Mahammedi A, Kouzou A, Hafaifa A, Talbi B. A new technique for photovoltaic system efficiency under fast changing solar irradiation. Electrotehnica, Electronica, Automatica (EEA) 2019;67:12-19. http://www.eea-journal.ro/ro/d/5/p/EEA67_4_2.
• 2. Saci A, Cherroun L, Hafaifa A, Mansour O. Effective fault diagnosis method for the pitch system, the drive train, and the generator with converter in a wind turbine system. Electrical Engineering 2022;104:1967-1983. https://doi.org/10.1007/s00202-021-01446-8.
• 3. Azharuddin A, Amiruddin AM, Zabiri H, Syed TA Ammar, Tufa LD. Neural network applications in fault diagnosis and detection: an overview of implementations in engineering-related systems. Neural Computing and Applications 2020;32: 447-472. https://doi.org/10.1007/s00521-018-3911-5.
• 4. Hafaifa A, Kaid I, Guemana M, Salam A. Maximum power point tracking of photovoltaic system based on fuzzy control to increase their solar energy efficiency. EMENA-ISTL 2019. Learning and Analytics in Intelligent Systems 2020;7:563-571. https://doi.org/10.1007/978-3-030-36778-7_62.
• 5. Djeddi AZ, M'hamdi B, Kouzou A, Hafaifa A. Parameters estimation of a reliability model based on bath-shaped failure rate function using meta-heuristic algorithms. 2021I EEE 1st International Conference On Cyber Management And Engineering (CyMaEn), 2021. https://doi.org/10.1109/CyMaEn50288.2021.9497303.
• 6. Kidar A, Kouzou A, Kaddouri AM, Hafaifa A, Saadi S. The Implementation Feasibility of PV Power Plant based on Mono-Crystalline and Poly-Crystalline Technologies for Remote Regions in the Algerian Steppe. Electrotehnica, Electronica, Automatica 2021;69(3):30-38. https://doi.org/10.46904/eea.21.69.3.1108004.
• 7. Amit Dhoke, Rahul Sharma, Tapan Kumar Saha, A technique for fault detection, identification and location in solar photovoltaic systems. Solar Energy 2020;206:864-874. https://doi.org/10.1016/j.solener.2020.06.019.
• 8. Eskandari A, Aghaei M, Milimonfared J, Nedaei A. A weighted ensemble learning-based autonomous fault diagnosis method for photovoltaic systems using genetic algorithm. International Journal of Electrical Power & Energy Systems 2023; 144:108591. https://doi.org/10.1016/j.ijepes.2022.108591.
• 9. Azghandi M. Ali, Barakati S. Masoud, A Temporary overvoltage’s mitigation strategy for grid-connected photovoltaic systems based on current-source inverters. Iranian Journal of Science and Technology, Transactions of Electrical Engineering 2020;44: 1253-1262. https://doi.org/10.1007/s40998-019- 00291-7.
• 10. Balamurugan M, Sarat Kumar Sahoo, A novel islanding detection technique for grid connected photovoltaic system. Applied Solar Energy 2017; 53: 208-214. https://doi.org/10.3103/S0003701X17030057.
• 11. Baojie Li, Claude Delpha, Demba Diallo, A. MiganDubois. Application of artificial neural networks to photovoltaic fault detection and diagnosis: A review. Renewable and Sustainable Energy Reviews 2021;138:110512. https://doi.org/10.1016/j.rser.2020.110512.
• 12. Barun Basnet, Hyunjun Chun, Junho Bang, An Intelligent fault detection model for fault detection in photovoltaic systems. Journal of Sensors 2020: 960328. https://doi.org/10.1155/2020/6960328.
• 13. Fan Jia, Liwen Luo, Shiyue Gao, Jian Ye, Logistic regression based arc fault detection in photovoltaic systems under different conditions. Journal of Shanghai Jiaotong University (Science) 2019;24: 459-470. https://doi.org/10.1007/s12204-019-2095-1.
• 14. Fengxin Cui, Yanzhao Tu, Wei Gao, A Photovoltaic system fault identification method based on improved deep residual shrinkage networks. Energies 2022;15(11):3961. https://doi.org/10.3390/en15113961.
• 15. Fethallah Tati, Hicham Talhaoui, Oualid Aissa, Abdeldjalil Dahbi, Intelligent shading fault detection in a PV system with MPPT control using neural network technique. International Journal of Energy and Environmental Engineering 2022;13:1147-1161. https://doi.org/10.1007/s40095-022-00486-5.
• 16. Ghadeer Badran, Mahmoud Dhimish. Field study on the severity of photovoltaic potential induced degradation. Scientific Reports 2022;12:22094. https://doi.org/10.1038/s41598-022-26310-y.
• 17. Kaid I, Hafaifa A, Guemana M, Hadroug N, Kouzou A, Mazouz L. Photovoltaic system failure diagnosis based on adaptive neuro fuzzy inference approach: South Algeria solar power plant. Journal of Cleaner Production 2018;204:169-182. https://doi.org/10.1016/j.jclepro.2018.09.023.
• 18. Jenitha P, Immanuel Selvakumar A. Fault detection in PV systems. Applied Solar Energy 2017;53:229-237. https://doi.org/10.3103/S0003701X17030069.
• 19. Jianbo Yu, Yue Zhang. Challenges and opportunities of deep learning-based process fault detection and diagnosis: a review. Neural Computing and Applications 2023;35:211-252. https://doi.org/10.1007/s00521-022-08017-3.
• 20. José Miguel Sanz-Alcaine, Eduardo Sebastián, Iván Sanz-Gorrachategui, Carlos Bernal-Ruiz, Antonio Bono-Nuez, Milutin Pajovic, Philip V. Orlik. Online voltage prediction using gaussian process regression for fault-tolerant photovoltaic standalone applications. Neural Computing and Applications 2021;33:16577-16590. https://doi.org/10.1007/s00521-021-06254-6.
• 21. Joshuva Arockia Dhanraj, Ali Mostafaeipour, Karthikeyan Velmurugan, Kuaanan Techato, Prem Kumar Chaurasiya, Jenoris Muthiya Solomon, Anitha Gopalan, Khamphe Phoungthong, An Effective evaluation on fault detection in solar panels. Energies 2021;14(22):7770. https://doi.org/10.3390/en14227770.
• 22. Kurukuru V S Bharath, Ahteshamul Haque, Arun Kumar Tripathy, Mohammed Ali Khan, Machine learning framework for photovoltaic module defect detection with infrared images. International Journal of System Assurance Engineering and Management 2022;13: 1771-1787. https://doi.org/10.1007/s13198- 021-01544-7.
• 23. Lipsa Priyadarshini, Dash PK, Mrutyunjaya Sahani, Diagnosis of voltage dips using a novel morphological filter and a smart Deep Learning LSTM-Based Minimum Variance RVFLN Classifier. Iranian Journal of Science and Technology, Transactions of Electrical Engineering 2023;47: 79- 101. https://doi.org/10.1007/s40998-022-00550-0.
• 24. Mohamed Ben Rahmoune, Ahmed Hafaifa, Abdellah Kouzou, XiaoQi Chen, Ahmed Chaibet, Gas turbine monitoring using neural network dynamic nonlinear autoregressive with external exogenous input modelling. Mathematics and Computers in Simulation 2021;179:23-47. https://doi.org/10.1016/j.matcom.2020.07.017.
• 25. Mohammed Amine Deriche, Ahmed Hafaifa and Mohammedi Kamal, Performance evaluation of A-Si and CdTe solar photovoltaic using energy and exergy analysis. International Journal of Exergy 2020;32(4): 373-387. https://doi.org/10.1504/IJEX.2020.108947.
• 26. Mohammed Amine Deriche, Ahmed Hafaifa, Ali Tahri, Kamal Mohammedi, Fatima Tahri, Energy and environmental performance analysis of gridconnected photovoltaic systems under similar outdoor conditions in the Saharan environment. Diagnostyka 2020;21(2):13-23. https://doi.org/10.29354/diag/122035.
• 27. Noamane Ncir, Nabil El Akchioui, An Intelligent improvement based on a novel configuration of artificial neural network model to track the maximum power point of a photovoltaic panel. Journal of Control, Automation and Electrical Systems 2023;34:363-375. https://doi.org/10.1007/s40313-022-00972-5.
• 28. Ruby Beniwal, Gupta HO, Tiwari GN. A generalized ANN model for reliability analysis of a semitransparent photovoltaic solar module with cost modeling. Journal of Computational Electronics 2018;17:1167-1175. https://doi.org/10.1007/s10825-018-1200-2.
• 29. Sally Abdulaziz, Galal Atlam, Gomaa Zaki, Essam Nabi., Optimal control strategies-based maximum power point tracking for photovoltaic systems under variable environmental conditions. International Journal of Modelling, Identification and Control 2023;42(1):64-82. https://doi.org/10.1504/IJMIC.2023.128773.
• 30. Salomé Ndjakomo Essiane, Patrick Juvet Gnetchejo, Pierre Ele, Zhicong Chen. Faults detection and identification in PV array using kernel principal components analysis. International Journal of Energy and Environmental Engineering 2022;13:153-178. https://doi.org/10.1007/s40095-021-00416-x.
• 31. Suryanarayana Gangolu, Saumendra Sarangi. FuzzyBased fault detection and classification in gridconnected floating PV System. Journal of Control, Automation and Electrical Systems 2023;34:324-332. https://doi.org/10.1007/s40313-022-00969-0.
• 32. Van Gompel Jonas, Domenico Spina, Chris Develder. Cost-effective fault diagnosis of nearby photovoltaic systems using graph neural networks. Energy, 2023;266:126444. https://doi.org/10.1016/j.energy.2022.126444.
• 33. Vincenzo Carletti, Antonio Greco, Alessia Saggese, Mario Vento, An intelligent flying system for automatic detection of faults in photovoltaic plants. Journal of Ambient Intelligence and Humanized Computing 2020;11:2027-2040. https://doi.org/10.1007/s12652-019-01212-6.
• 34. Zhou Li, Tao Peng, Peng-Fei Zhang, Hua Han, Jian Yang. Fault diagnosis and fault-tolerant control of photovoltaic micro-inverter. Journal of Central South University 2016;23:2284-2295. https://doi.org/10.1007/s11771-016-3286-7.

Uwagi

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).