Electrical Faults Analysis and Detection in Photovoltaic Arrays Based on Machine Learning Classifiers
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- Pillai, Dhanup S. & Rajasekar, N., 2018. "A comprehensive review on protection challenges and fault diagnosis in PV systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 91(C), pages 18-40.
- Hong-Chan Chang & Shang-Chih Lin & Cheng-Chien Kuo & Hao-Ping Yu, 2014. "Cloud Monitoring for Solar Plants with Support Vector Machine Based Fault Detection System," Mathematical Problems in Engineering, Hindawi, vol. 2014, pages 1-10, July.
- Chine, W. & Mellit, A. & Lughi, V. & Malek, A. & Sulligoi, G. & Massi Pavan, A., 2016. "A novel fault diagnosis technique for photovoltaic systems based on artificial neural networks," Renewable Energy, Elsevier, vol. 90(C), pages 501-512.
- Chen, Zhicong & Wu, Lijun & Cheng, Shuying & Lin, Peijie & Wu, Yue & Lin, Wencheng, 2017. "Intelligent fault diagnosis of photovoltaic arrays based on optimized kernel extreme learning machine and I-V characteristics," Applied Energy, Elsevier, vol. 204(C), pages 912-931.
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Keywords
photovoltaic systems; PV string; I-V curve analysis; Support Vector Machine (SVM); Extreme Grading Boosting (XGBoost); Bees Algorithm (BA); Particle Swarm Optimization (PSO); fault classification;All these keywords.
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