粒子群优化
人工神经网络
断层(地质)
计算机科学
反向传播
电子线路
电子工程
模拟退火
工程类
控制理论(社会学)
算法
人工智能
电气工程
控制(管理)
地震学
地质学
作者
Deye Jiang,Yiguang Wang
出处
期刊:Computers, materials & continua
日期:2023-01-01
卷期号:76 (1): 295-309
被引量:2
标识
DOI:10.32604/cmc.2023.039244
摘要
In the field of energy conversion, the increasing attention on power electronic equipment is fault detection and diagnosis. A power electronic circuit is an essential part of a power electronic system. The state of its internal components affects the performance of the system. The stability and reliability of an energy system can be improved by studying the fault diagnosis of power electronic circuits. Therefore, an algorithm based on adaptive simulated annealing particle swarm optimization (ASAPSO) was used in the present study to optimize a backpropagation (BP) neural network employed for the online fault diagnosis of a power electronic circuit. We built a circuit simulation model in MATLAB to obtain its DC output voltage. Using Fourier analysis, we extracted fault features. These were normalized as training samples and input to an unoptimized BP neural network and BP neural networks optimized by particle swarm optimization (PSO) and the ASAPSO algorithm. The accuracy of fault diagnosis was compared for the three networks. The simulation results demonstrate that a BP neural network optimized with the ASAPSO algorithm has higher fault diagnosis accuracy, better reliability, and adaptability and can more effectively diagnose and locate faults in power electronic circuits.
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