The maximum power generation in the solar photovoltaic (PV) array is reduced due to the abnormal conditions such as module mismatch, string faults and damage of the PV modules, which reduces the efficiency and reliability of the system. Conventional protection devices fail to detect the faults, which leads to protection issues and fire threats in the PV plants. This paper proposes a new fault detection algorithm to identify the faults in the PV array and the PV string. A simple analysis is developed for fault detection under different fault conditions, such as line-line (L-L) fault, line-ground (L-G) fault and short-circuit fault with multiple strings, and the values of their current indicator and threshold are predetermined. Based on these values, the proposed fault detection algorithm identifies the fault in the PV array and the PV string, with a reduced number of current sensing devices. The effectiveness of the proposed algorithm is tested and verified through MATLAB simulation and experimentation under various operating conditions of the solar PV plant. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
Solar photovoltaic systems installed in outdoor environments are susceptible to faults and partial shading, which leads to reduction in the production of maximum power. The conventional protection units are unable to detect the types of faults due to non-linear characteristics and they result in fire hazards and reduced system efficiency. In this paper, a fault detection method based on Multiclass Support Vector Machine (MSVM) is proposed to detect different faults like line-ground (L-G), line-line (L-L), and partial shading. The array voltage, array current and irradiance are used to detect the line-line and partial shading under different irradiation conditions. The novel Opposition-based Border Collie Optimization (OBCO) algorithm is used to improve the accuracy of fault classification by optimizing the hyper-parameters of MSVM. A 1.6 kW, 4 × 4 solar photovoltaic array is developed, and the fault conditions are experimentally tested to validate the proposed algorithm. The experimental results show that the proposed MSVM-OBCO fault detection algorithm has higher accuracy compared to that of the existing classification algorithms such as k-nearest neighbor, Naïve Bayes, Decision Tree and Random Forest.
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