Detecting fault in photovoltaic system with a hybrid PDACNN-IDMOA methodology - Rashmi, G.; Manjusha, M.; Anish John Paul, M.; Juliet Rose, D. B. (Electrical engineering, 2025)
PV system fault detection is essential since unidentified problems lead to energy loss, safety hazards and financial losses due to reduced power output and possible component damage. Early detection contributes to a sustainable and effective solar solution by ensuring optimal performance, minimizing risks and maximizing financial benefits. Hence research on the detection of fault in photovoltaic (PV) systems is quite important. This paper proposes a hybrid technique for detecting the fault in PV systems. The proposed hybrid method is the combined performance of both the pyramidal dilation attention convolutional neural network (PDACNN) and improved dwarf mongoose optimization algorithm (IDMOA). Commonly it is referred as PDACNN-IDMOA technique. The major goal of the proposed method is to improve the detection of fault in PV system. In this manuscript, the hybrid PDACNN-IDMOA approach is suggested. The PDACNN approach is used for the fault detection in the PV system, and the IDMOA is used to optimize the weight parameter of the PDACNN. Moreover, the result of the system critic behaviour is researched, where the critic is a function of control fault. By then, the proposed framework has been integrated into the MATLAB/Simulink workspace, and existing methods are being used to compute its execution. The proposed method shows better outcomes than all existing methods like genetic algorithm–support vector machine (GA-SVM), backpropagation neural network–particle swarm optimization (BPNN-PSO) and salp swarm optimization–supervised machine learning (SSA-SML). The PV system breakdown can be found using the proposed method. The outcome indicates that, in comparison with existing methods, the proposed method's based RMSE value is 3.521* 10 - 4 is smaller than the existing approaches.