改进PSO-BPNN的电力变压器故障诊断与模式识别  

Fault diagnosis and pattern recognition of power transformer based on improved PSO-BPNN

作　　者：夏琳琳[1] 文磊[1] 刘惠敏[2] 初妍[3] 台金娟 

机构地区：[1]东北电力大学自动化工程学院,吉林吉林132012 [2]青岛农业大学机电工程学院,山东青岛266109 [3]哈尔滨工程大学计算机科学与技术学院,哈尔滨150001

出　　处：《沈阳工业大学学报》2016年第6期606-611,共6页Journal of Shenyang University of Technology

基　　金：国家自然科学基金青年基金资助项目(61503073);吉林市杰出青年基金资助项目(20166005)

摘　　要：为了优化反向传播网络相关学习参数,提出一种粒子群优化辅助BP神经网络(BPNN)的新方法.以变压器油中气体体积分数百分比构造故障特征,将BP网络的初始权值和阈值进行实数编码,以对应PSO中的粒子,实现BP网络的离线训练与在线分析,对变压器故障模式做出判断.结果表明,该算法更合理地更新了粒子的位置和速度,最优地设置了全局极值,有效克服了粒子的早熟收敛,获得的故障诊断准确率高达91%,并大大提升了BP网络的收敛速度.该算法为此类设计提供了有效的模型参考.In order to optimize the relative learning parameters for back propagation neural network （BPNN）, a novel particle swarm optimization （PSO） aided BPNN method was proposed. The fault features were established with the gas volume fraction in the oil of power transformer. In addition, the initial weight and threshold values of BPNN were coded in real number form, and were related to the particles in the PSO. The off-line training and on-line analysis of BPNN were realized, and the fault patterns of transformer were judged. The results show that the algorithm updates the position and velocity of particles more reasonably, and the global extremum is optimally set. The premature convergence of particles is effectively overcome, and the accuracy rate of fault diagnosis approaches as high as 91%. Besides, the convergence rate of BPNN gets dramatically enhanced, and the mentioned algorithm provides an effective model reference for this sort of designs.

关 键 词：粒子群优化算法 混沌初始化 惯性权重 高斯扩张变异 BPNN方法 电力变压器 故障诊断 

分 类 号：TM407[电气工程—电器]