Unified Fuzzy Logic Based Approach for Detection and Classification of PV Faults Using I-V Trend Line

Imran Hussain, Ihsan Ullah Khalil, Aqsa Islam, Mati Ullah Ahsan, Taosif Iqbal, Md. Shahariar Chowdhury, Kuaanan Techato and Nasim Ullah
Additional contact information
Imran Hussain: Department of Electrical Engineering, University of Engineering and Technology, Taxila 47050, Pakistan
Ihsan Ullah Khalil: College of Electrical and Mechanical Engineering, National University of Sciences and Technology, Islamabad 44000, Pakistan
Aqsa Islam: Department of Physics, University of Agriculture, Faisalabad 38000, Pakistan
Mati Ullah Ahsan: Department of Electrical Engineering, Universiti Tun Hussein Onn Malaysia (UTHM), Parit Raja, Batu Pahat 86400, Malaysia
Taosif Iqbal: College of Electrical and Mechanical Engineering, National University of Sciences and Technology, Islamabad 44000, Pakistan
Md. Shahariar Chowdhury: Faculties Environmental Management, Prince of Songkla University, Songkhla 90110, Thailand
Kuaanan Techato: Faculties Environmental Management, Prince of Songkla University, Songkhla 90110, Thailand
Nasim Ullah: Department of Electrical Engineering, College of Engineering, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia

Energies, 2022, vol. 15, issue 14, 1-14

Abstract: Solar photovoltaic PV plants worldwide are continuously monitored and carefully protected to ensure safe and reliable operation through detecting and isolating faults. Faults are very common in modern solar PV systems which interrupt normal system operation adversely affecting the performance of the PV systems. When undetected, faults not only cause significant reduction in the efficiency and life span of the PV system, but also result in damage and fire hazards compromising their reliability. Therefore, early fault detection and diagnosis of photovoltaic plants is a necessity for safe and reliable operation required for growing solar PV systems. Unfortunately, several recent fire incidents have been reported recently caused by undetected faults in solar PV systems. Motivated by this challenge, this paper, utilizing a proposed fuzzy logic algorithm, presents a novel technique for detecting and classifying faults in solar PV systems. Furthermore, the proposed method introduces fault indexing as a performance indicator that measures the degree of deviation from the normal operating conditions of the photovoltaic system. Various signatures of each fault scenario are identified in the shape of corresponding current-voltage trajectories and their extracted parameters. The effectiveness of the proposed technique is evaluated both in simulation and experimentally using a 5 kW grid connected solar array. It is demonstrated that the proposed technique is capable of diagnosing the occurrence of different faults with more than 98% accuracy.

Keywords: fuzzy logic controller (FLC); fault detection and diagnosis (FDD); machine learning (ML); photovoltaic (PV) systems (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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