PSO改进RBPNN在变压器故障诊断中的应用

施恂山; 马宏忠; 张 琳; 李 凯; 许洪华; 陈冰冰

引用本文:	施恂山,马宏忠,张琳,等.PSO改进RBPNN在变压器故障诊断中的应用[J].电力系统保护与控制,2016,44(17):39-44.
	SHI Xunshan,MA Hongzhong,ZHANG Lin,et al.Application of RBPNN improved by PSO in fault diagnosis of transformers[J].Power System Protection and Control,2016,44(17):39-44

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PSO改进RBPNN在变压器故障诊断中的应用
施恂山¹,马宏忠¹,张琳¹,李凯²,许洪华²,陈冰冰²
(1.河海大学能源与电气学院，江苏南京211100;2.江苏省电力公司南京供电公司，江苏南京 210008)

摘要:

针对概率神经网络(PNN)及遗传算法(GA)在变压器内部故障诊断中存在的不足，提出了一种基于粒子群算法(PSO)改进径向基概率神经网络(RBPNN)的故障诊断方法。首先，引入RBPNN，选取反向传播作为学习算法以及油中溶解气体含量比值作为故障特征量。然后，由于该模型受网络结构和初值影响较大，故拟用GA、PSO和改进的PSO对网络优化并测试。通过对比分析，得出改进的PSO在确定拓扑结构、降低误差精度、加快收敛速度和提高预测准确度上更占优势的结论，同时证明了所提方法在故障诊断中的正确性和可行性。

关键词: 粒子群算法径向基概率神经网络反向传播变压器故障诊断

DOI：10.7667/PSPC160081

分类号:

基金项目:国家自然科学基金项目(51577050);江苏省电力公司科技项目(J2014055)

Application of RBPNN improved by PSO in fault diagnosis of transformers

SHI Xunshan¹,MA Hongzhong¹,ZHANG Lin¹,LI Kai²,XU Honghua²,CHEN Bingbing²

(1. College of Energy and Electrical Engineering, Hohai University, Nanjing 211100, China;
;2. Nanjing Power Supply Company, Jiangsu Electric Power Company, Nanjing 210008, China)

Abstract:

Aiming at the existing deficiencies of probabilistic neural network (PNN) and genetic algorithm (GA) in internal faults of transformers, a fault diagnosis method based on radial basis probabilistic neural network (RBPNN) improved by particle swarm optimization (PSO) is proposed. Firstly, this paper introduces RBPNN and selects back-propagation as the learning algorithm as well as the content ratio of dissolved gases in oil as the characteristic quantity of fault. Then, since the network structure and the initial value have a great impact on RBPNN, this model is optimized and tested with GA, PSO and improved PSO. The comparison results show that improved PSO has more advantages in determining topology, decreasing error accuracy, accelerating the convergence speed and improving prediction accuracy, which also verify the correctness and feasibility of the proposed method in fault diagnosis. This work is supported by National Natural Science Foundation of China (No. 51577050) and Science and Technology Project of Jiangsu Province Electric Power Company (No. J2014055).

Key words: PSO RBPNN back propagation power transformer fault diagnosis

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