Research on Comprehensive State Assessment of Current Transformer Based on Improved Neural Network
For the accuracy degradation and fault caused by long-term operation of current transformers under harsh operating conditions,an electronic current transformer online condition comprehensive evaluation system is proposed to achieve online error prediction and fault diagnosis of transformers based on neural networks.With regard to the slow convergence and low accuracy of existing neural network-based methods,a neural network based on improved whale optimization is put forward for error prediction and fault diagnosis.The convergence speed of whale optimization is accelerated by nonlinear convergence factor,and meanwhile,adaptive inertia weights and simulated annealing mechanism are introduced to improve the accuracy of the whale optimization algorithm and avoid falling into local optimal.Benchmark function test and case analysis are conducted to verify the validity and reliability of the method.The experiment proves that designed the online state comprehensive evaluation system of current transformer designed can effectively perform error prediction and fault diagnosis of current transformer.
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