A Novel Opposition-Based Border Collie Optimization Approach for Fault Detection in Solar Photovoltaic Array
DOI:
https://doi.org/10.13052/dgaej2156-3306.38313Keywords:
Fault detection, solar photovoltaic system, line-line fault, multiclass SVM, one vs all classifier, machine learning, opposition-based learning, border collie optimizationAbstract
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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