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Recent advances in failure diagnosis techniques based on performance data analysis for grid-connected photovoltaic systems

Citations

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  1. Li, B. & Delpha, C. & Diallo, D. & Migan-Dubois, A., 2021. "Application of Artificial Neural Networks to photovoltaic fault detection and diagnosis: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 138(C).
  2. Duberney Murillo-Yarce & José Alarcón-Alarcón & Marco Rivera & Carlos Restrepo & Javier Muñoz & Carlos Baier & Patrick Wheeler, 2020. "A Review of Control Techniques in Photovoltaic Systems," Sustainability, MDPI, vol. 12(24), pages 1-21, December.
  3. Antonio Pio Catalano & Ciro Scognamillo & Pierluigi Guerriero & Santolo Daliento & Vincenzo d’Alessandro, 2021. "Using EMPHASIS for the Thermography-Based Fault Detection in Photovoltaic Plants," Energies, MDPI, vol. 14(6), pages 1-19, March.
  4. Nien-Che Yang & Harun Ismail, 2022. "Voting-Based Ensemble Learning Algorithm for Fault Detection in Photovoltaic Systems under Different Weather Conditions," Mathematics, MDPI, vol. 10(2), pages 1-18, January.
  5. Van Gompel, Jonas & Spina, Domenico & Develder, Chris, 2023. "Cost-effective fault diagnosis of nearby photovoltaic systems using graph neural networks," Energy, Elsevier, vol. 266(C).
  6. Liu, Tianhao & Shan, Linke & Jiang, Meihui & Li, Fangning & Kong, Fannie & Du, Pengcheng & Zhu, Hongyu & Goh, Hui Hwang & Kurniawan, Tonni Agustiono & Huang, Chao & Zhang, Dongdong, 2025. "Multi-dimensional data processing and intelligent forecasting technologies for renewable energy generation," Applied Energy, Elsevier, vol. 398(C).
  7. Belqasem Aljafari & Siva Rama Krishna Madeti & Priya Ranjan Satpathy & Sudhakar Babu Thanikanti & Bamidele Victor Ayodele, 2022. "Automatic Monitoring System for Online Module-Level Fault Detection in Grid-Tied Photovoltaic Plants," Energies, MDPI, vol. 15(20), pages 1-28, October.
  8. Bilal Taghezouit & Fouzi Harrou & Cherif Larbes & Ying Sun & Smail Semaoui & Amar Hadj Arab & Salim Bouchakour, 2022. "Intelligent Monitoring of Photovoltaic Systems via Simplicial Empirical Models and Performance Loss Rate Evaluation under LabVIEW: A Case Study," Energies, MDPI, vol. 15(21), pages 1-30, October.
  9. Ding, Kun & Chen, Xiang & Jiang, Meng & Yang, Hang & Chen, Xihui & Zhang, Jingwei & Gao, Ruiguang & Cui, Liu, 2024. "Feature extraction and fault diagnosis of photovoltaic array based on current–voltage conversion," Applied Energy, Elsevier, vol. 353(PB).
  10. Liu, Yinyan & Duran, Earl & Bruce, Anna & Yildiz, Baran & Mendonca Severiano, Bernardo & Anwar Ibrahim, Ibrahim & Rispler, Jonathan & Martell, Chris & Rougieux, Fiacre, 2025. "A methodological review of cost-effective data-driven fault detection and diagnosis in distributed photovoltaic systems," Applied Energy, Elsevier, vol. 401(PA).
  11. Li, Chenxi & Yang, Yongheng & Spataru, Sergiu & Zhang, Kanjian & Wei, Haikun, 2021. "A robust parametrization method of photovoltaic modules for enhancing one-diode model accuracy under varying operating conditions," Renewable Energy, Elsevier, vol. 168(C), pages 764-778.
  12. Qu, Jiaqi & Sun, Qiang & Qian, Zheng & Wei, Lu & Zareipour, Hamidreza, 2024. "Fault diagnosis for PV arrays considering dust impact based on transformed graphical features of characteristic curves and convolutional neural network with CBAM modules," Applied Energy, Elsevier, vol. 355(C).
  13. Tomáš Finsterle & Ladislava Černá & Pavel Hrzina & David Rokusek & Vítězslav Benda, 2021. "Diagnostics of PID Early Stage in PV Systems," Energies, MDPI, vol. 14(8), pages 1-15, April.
  14. Mahmudul Islam & Masud Rana Rashel & Md Tofael Ahmed & A. K. M. Kamrul Islam & Mouhaydine Tlemçani, 2023. "Artificial Intelligence in Photovoltaic Fault Identification and Diagnosis: A Systematic Review," Energies, MDPI, vol. 16(21), pages 1-18, November.
  15. Michael W. Hopwood & Joshua S. Stein & Jennifer L. Braid & Hubert P. Seigneur, 2022. "Physics-Based Method for Generating Fully Synthetic IV Curve Training Datasets for Machine Learning Classification of PV Failures," Energies, MDPI, vol. 15(14), pages 1-16, July.
  16. Francisco José Gimeno-Sales & Salvador Orts-Grau & Alejandro Escribá-Aparisi & Pablo González-Altozano & Ibán Balbastre-Peralta & Camilo Itzame Martínez-Márquez & María Gasque & Salvador Seguí-Chilet, 2020. "PV Monitoring System for a Water Pumping Scheme with a Lithium-Ion Battery Using Free Open-Source Software and IoT Technologies," Sustainability, MDPI, vol. 12(24), pages 1-28, December.
  17. Chen, Qi & Li, Xinyuan & Zhang, Zhengjia & Zhou, Chao & Guo, Zhiling & Liu, Zhengguang & Zhang, Haoran, 2023. "Remote sensing of photovoltaic scenarios: Techniques, applications and future directions," Applied Energy, Elsevier, vol. 333(C).
  18. Clavijo-Blanco, J.A. & Álvarez-Tey, G. & Saborido-Barba, N. & Barberá-González, J.L. & García-López, C. & Jiménez-Castañeda, R., 2021. "Laboratory tests for the evaluation of the degradation of a photovoltaic plant of 2.85 MWp with different classes of PV modules," Renewable Energy, Elsevier, vol. 174(C), pages 262-277.
  19. Sascha Lindig & Atse Louwen & David Moser & Marko Topic, 2020. "Outdoor PV System Monitoring—Input Data Quality, Data Imputation and Filtering Approaches," Energies, MDPI, vol. 13(19), pages 1-18, September.
  20. Rehman, Tauseef-ur & Qaisrani, Mumtaz A. & Shafiq, M. Basit & Baba, Yousra Filali & Aslfattahi, Navid & Shahsavar, Amin & Cheema, Taqi Ahmad & Park, Cheol Woo, 2025. "Global perspectives on advancing photovoltaic system performance—A state-of-the-art review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 207(C).
  21. Bouaichi, Abdellatif & Alami Merrouni, Ahmed & Hajjaj, Charaf & Messaoudi, Choukri & Ghennioui, Abdellatif & Benlarabi, Ahmed & Ikken, Badr & El Amrani, Aumeur & Zitouni, Houssin, 2019. "In-situ evaluation of the early PV module degradation of various technologies under harsh climatic conditions: The case of Morocco," Renewable Energy, Elsevier, vol. 143(C), pages 1500-1518.
  22. Bakdi, Azzeddine & Bounoua, Wahiba & Mekhilef, Saad & Halabi, Laith M., 2019. "Nonparametric Kullback-divergence-PCA for intelligent mismatch detection and power quality monitoring in grid-connected rooftop PV," Energy, Elsevier, vol. 189(C).
  23. Sahil Shah & Aravind Reddy Boozula & Radhika Girish Lampuse & Jigar Janakbhai Thakkar, 2025. "A Systematic Framework for Reconciling Solar Energy Production Models with Operational Data: A Literature Synthesis and Methodology Development," European Journal of Energy Research, European Open Science, vol. 5(4), pages 7-23, July.
  24. Fouzi Harrou & Bilal Taghezouit & Sofiane Khadraoui & Abdelkader Dairi & Ying Sun & Amar Hadj Arab, 2022. "Ensemble Learning Techniques-Based Monitoring Charts for Fault Detection in Photovoltaic Systems," Energies, MDPI, vol. 15(18), pages 1-28, September.
  25. Romero-Fiances, Irene & Livera, Andreas & Theristis, Marios & Makrides, George & Stein, Joshua S. & Nofuentes, Gustavo & de la Casa, Juan & Georghiou, George E., 2022. "Impact of duration and missing data on the long-term photovoltaic degradation rate estimation," Renewable Energy, Elsevier, vol. 181(C), pages 738-748.
  26. Qu, Jiaqi & Qian, Zheng & Pei, Yan & Wei, Lu & Zareipour, Hamidreza & Sun, Qiang, 2022. "An unsupervised hourly weather status pattern recognition and blending fitting model for PV system fault detection," Applied Energy, Elsevier, vol. 319(C).
  27. Wang, Shinong & Wang, Zheng & Ge, Yuan & Amer, Ragab Ahmed, 2025. "Performance estimator of photovoltaic modules by integrating deep learning network with physical model," Energy, Elsevier, vol. 325(C).
  28. Hetita, Ibrahim & Zalhaf, Amr S. & Mansour, Diaa-Eldin A. & Han, Yang & Yang, Ping & Wang, Congling, 2022. "Modeling and protection of photovoltaic systems during lightning strikes: A review," Renewable Energy, Elsevier, vol. 184(C), pages 134-148.
  29. Van Gompel, Jonas & Spina, Domenico & Develder, Chris, 2022. "Satellite based fault diagnosis of photovoltaic systems using recurrent neural networks," Applied Energy, Elsevier, vol. 305(C).
  30. Wang, Han & Zhang, Ning & Du, Ershun & Yan, Jie & Han, Shuang & Li, Nan & Li, Hongxia & Liu, Yongqian, 2023. "An adaptive identification method of abnormal data in wind and solar power stations," Renewable Energy, Elsevier, vol. 208(C), pages 76-93.
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