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首页> 外文期刊>International journal of electrical and power engineering >Three Phase Fault Diagnosis Based on RBF Neural Network Optimized By PSO Algorithm
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Three Phase Fault Diagnosis Based on RBF Neural Network Optimized By PSO Algorithm

机译：PSO算法优化的基于RBF神经网络的三相故障诊断

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摘要

The present study proposes a fault diagnosis methodology of three phase inverter circuit base on Radial Basis Function (RBF) artificial neural network trained by Particle Swarm Optimization (PSO) algorithm. Using the appropriate stimulus signal, fault features are extracted from efficient points in frequency response of the circuit directly and then a fault dictionary is created by collecting signatures of different fault conditions. Trained by the examples contained in the fault dictionary, the RBF neural network optimized by PSO has been demonstrated to provide robust diagnosis to the difficult problem of soft faults in three phase inverter circuits. The experimental result shows that the proposed technique is succeeded in diagnosing and locating faults effectively.
机译：本文提出了一种基于粒子群优化算法训练的径向基函数神经网络的三相逆变器电路故障诊断方法。使用适当的激励信号，直接从电路频率响应的有效点中提取故障特征，然后通过收集不同故障条件的特征来创建故障字典。通过故障字典中包含的示例进行训练，已经证明了通过PSO优化的RBF神经网络可以为三相逆变器电路中的软故障难题提供可靠的诊断。实验结果表明，所提出的技术能够成功地对故障进行诊断和定位。

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