Fault Identification and Classification of Asynchronous Motor Drive Using Optimization Approach with Improved Reliability

This article aims to provide a technique for identifying and categorizing interturn insulation problems in variable-speed motor drives by combining Salp Swarm Optimization (SSO) with Recurrent Neural Network (RNN). The goal of the proposed is detect classify Asynchronous Motor faults at their early stages, under both normal abnormal operating conditions. uses recurrent neural network two phases identify label concerns, first phase being utilised establish whether or not motors are healthy. In second step, it discovers categorises potentially dangerous errors. SSO approach used learning procedure, function minimizing error mind. CSSRN simplifies system detecting issue, resulting increased precision. addition, model implemented MATLAB/Simulink, where metrics such as accuracy, precision, recall, specificity may be analysed. Similarly, existing methods Adaptive Neuro-Fuzzy Inference System (ANFIS), (RNN), Algorithm Artificial (SSAANN) evaluate Root mean squared (RMSE), Mean bias (MBE), absolute percentage (MAPE), consumption, execution time comparative analysis.