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Abstract
Identifying faults in the photovoltaic (PV) arrays is very much essential in improving the PV system’s safety and reliability. Solar PV arrays operate with non-linear characteristics, installed with maximum power point trackers (MPPT’s), and blocking diodes cause mismatch levels. Line-to-line and line-to-ground faults are identified, and the faulted circuits are isolated by means of over current protection devices (OCPD) and ground fault protection devices (GFPD). In order to improve the accuracy of fault detection, artificial intelligence (AI)-based techniques like Fuzzy inference, wavelet, support vector machine, and k-nearest neighbors are used. The drawback of AI-based techniques are (1) requirement of large dataset for effective fault identification and also show incompatibility if there is low irradiation and (2) require a larger number of voltage and current sensors. An experimental setup of 160 W, 4 × 4 solar PV array having PV modules (SPB) is subjected to different fault conditions (CS), and the faults are identified using the minimum number of sensors. The faults that are not detected by the conventional methods are detected using this proposed method, and the power gain due to the fault identification is around 152% which is 97 W in the PV array.
DISCLOSURE STATEMENT
No potential conflict of interest was reported by the author(s).
COMPETING INTERESTS
The authors have no relevant financial or non-financial interests to disclose.
DATA AVAILABILITY STATEMENT
Not applicable.
Additional information
Funding
Notes on contributors
Sakthivel Ganesan
Sakthivel Ganesan completed his Bachelor’s degree in Electrical and Electronics Engineering from Anna University, Chennai, India, in 2009 and his Master’s degree in Power Electronics and Drives from Anna University, in 2012. He received a doctoral degree from Anna University, in 2022. Presently, he is working as an Assistant Professor at Kamaraj College of Engineering and Technology, Madurai, India. His current research interests include electric motor drives, power converters, power quality, and renewable energy systems.
Prince Winston David
Prince Winston David received the bachelor’s degree in electrical and electronics engineering, the master’s degree in power electronics and drives, and the doctoral degree from Anna University, Chennai, India, in 2006, 2008, and 2013, respectively. He is currently working as a Professor and Head with the Department of Electrical and Electronics Engineering, Kamaraj College of Engineering and Technology, Virudhunagar, India. Also, he is acting as the Dean of R&D in Kamaraj College of Engineering and Technology. He has published many articles in many reputed journals. His current research interests include electric motor drives, power converters, power quality, and renewable energy systems.
Pravin Murugesan
Pravin Murugesan received his B.E. degree in Electrical and Electronics Engineering from Anna University in 2021. Currently, he is pursuing an M.E. degree in Power Systems Engineering. He has published 10 research articles in several reputed international journals. His current research interests include Solar PV, Solar Still.
Praveen Kumar Balachandran
Praveen Kumar Balachandran (Senior Member, IEEE) received B.E. degree in Electrical and Electronic Engineering, M.E. and Ph.D. degrees in Power Systems Engineering from Anna University, Chennai, India, in 2014, 2016, and 2019, respectively. He is currently working as an Associate Professor with the Department of Electrical and Electronic Engineering, Vardhaman College of Engineering, Hyderabad, India. He has published 60+ scientific articles in reputed journals and also has 10 patents to his name. His current research interests include solar photovoltaics, solar stills, EV, IoT and renewable energy systems.