Imran Hussain
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Imran Hussain has a total of 7 co-authors. Here are the top co-authors by number of shared publications:
- Aqsa Islam
Department of Physics, University of Agriculture, Faisalabad 38000, Pakistan
1 shared publication - Ihsan Ullah Khalil
Department of Electrical Engineering, Abasyn University, Peshawar, Pakistan
1 shared publication - Kuaanan Techato
Faculty of Environmental Management, Prince of Songkla University, Songkhla, Thailand
1 shared publication - Matiullah Ahsan
University Tun Hussein Onn Malaysia
1 shared publication - Nasim Ullah
Department of Electrical Engineering, College of Engineering, Taif University, Taif, Saudi Arabia
1 shared publication - Shahariar Chowdhury
Health and Environmental Research Center, Faculty of Environmental Management, Prince of Songkla University, Hat Yai, Thailand
1 shared publication
Latest Publications
- Open Access
Unified Fuzzy Logic Based Approach for Detection and Classification of PV Faults Using I-V Trend Line
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.
Open AccessUnified Fuzzy Logic Based Approach for Detection and Classification of PV Faults Using I-V Trend Line
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.