ELECTRICA
Original Article

Fault Diagnosis of Power Electronic Circuits Based on Improved Particle Swarm Optimization Algorithm Neural Network

1.

Department of Mechanical and Electronic, Jingdezhen University, Jingdezhen, China

2.

Department of Electrical, Electronics & Computer Engineering, Cape Peninsula University of Technology, Cape Town, South Africa

ELECTRICA 2022; 22: 365-372
DOI: 10.5152/electrica.2022.21180
Read: 208 Downloads: 81 Published: 04 July 2022

In the rapid development of high and new technology, the intelligence and integration of modern equipment are constantly improving. Power electronics technology is one of the indispensable key technologies in any high and new technology. In this paper, a power electronics circuit fault diagnosis based on improved particle swarm optimization neural network is proposed, the algorithm design of particle swarm optimization algorithm neural network is introduced, and the improved PS0 algorithm, standard PS0 algorithm, and BP algorithm optimized neural network are applied to the fault diagnosis classification system of rectifier circuits. The results show that the parameters of the basic (particle swarm optimization) algorithm are as follows: the parameter value of the basic PSO algorithm is the number of particles is 30, W decreases from 0.9 to 0.4 linearly with the increase of iterations, and the number of iterations is 300. The BP algorithm uses the traingdx training function. The transfer functions of the hidden layer and the output layer are hyperbolic tangent sigmoid and Purelin function, respectively. The target error e = 0.01. The superiority and effectiveness of the neural network diagnosis model of the improved PS0 algorithm are shown in this paper. This method can solve the fault diagnosis problem of the double-bridge parallel rectifier circuit.

Cite this article as: Z. Xiao, Z. Guo and V. Balyan, "Fault diagnosis of power electronic circuits based on improved particle swarm optimization algorithm neural network," Electrica., 22(3), 365-372, 2022.

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EISSN 2619-9831