Abstract
Effective fault diagnosis in a PV system requires understanding the behavior of the current/voltage (I/V) parameters in different environmental conditions. Especially during the winter season, I/V characters of certain faulty states in a PV system closely resemble that of a normal state. Therefore, a normal fault detection model can falsely predict a well-operating PV system as a faulty state and vice versa. In this paper, an intelligent fault diagnosis model is proposed for the fault detection and classification in PV systems. For the experimental verification, various fault state and normal state datasets are collected during the winter season under wide environmental conditions. The collected datasets are normalized and preprocessed using several data-mining techniques and then fed into a probabilistic neural network (PNN). The PNN model will be trained with the historical data to predict and classify faults when new data is fetched in it. The trained model showed better performance in prediction accuracy when compared with other classification methods in machine learning.
Highlights
Fault detection and timely troubleshooting are essential for the optimum performance in any power generation system, including photovoltaic (PV) systems
Since PV systems are subject to various faults and failures, early detection of such faults and failures is very crucial for achieving the goal [1,2,3]
We propose an intelligent fault diagnosis model for detecting faulty modules and further classifying the fault type that is applicable in all environmental conditions
Summary
Fault detection and timely troubleshooting are essential for the optimum performance in any power generation system, including photovoltaic (PV) systems. The goal for any commercial power-producing house is maximizing power production, minimizing energy loss and maintenance cost, and the safe operation of the facility. Since PV systems are subject to various faults and failures, early detection of such faults and failures is very crucial for achieving the goal [1,2,3]. The US National Electric Code requires the installation of OCPD (Overcurrent Protection Device) and GFDI (Ground Fault Detection Interrupters) in PV installations for protection against certain faults. The Bakersfield Fire case, 2009, and Mount Holly, 2011, show the inability of these devices to detect the fault in those particular scenarios [4]. Faults in a PV system can arise from either physical, environmental, or electrical conditions [5, 6]
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