基于MATLAB的电气设备故障诊断仿真设计论文-田林亚.doc

基于MATLAB的电气设备故 障诊断仿真设计 院 系： 机电与自动化学院 专 业 班： 电气及其自动化1002班 姓 名： 田林亚 学 号： 20102822077 指导教师： 郭磊 2013年5月  基于MATLAB的电气设备 故障诊断仿真设计 The Design of electrical equipment fault diagnosis Based on the MATLAB simulation  摘要 电力变压器作为电力系统中最为重要的设备之一，对电力系统安全、可靠、优质、经济的运行起着决定性作用，因而，必须尽量减少变压器故障的产生。电力变压器故障检测对电力系统的经济安全运行有着重要的意义。油中溶解气体法，是最有效的发现和检测变压器故障的方法之一。神经网络对外界具有很强的模式识别分类能力和联想记忆能力，因此神经网络可以用于变压器故障检测。基于神经网络的以变压器油中溶解气体为特征量的故障检测方法为变压器故障检测提供了新的途径。 本文将采用二种不同的神经网络（BP网络、RBF网络）应用于变压器故障检测中，分别介绍这几种网络的基本结构和原理，并进行模型设计和仿真。 关键词:变压器 故障检测 神经网络 BP算法 RBF算法  Abstract as the most important part of the power system equipment,the power transformer to the safety of the electricity system, reliable and high quality, and the operation of the economy plays a decisive role, therefore, we must try to reduce the of transformer faults. Power transformer of electric power system fault detection of the economic security has important significances. The dissolved gas method, is one the most effective and found that one of the ways to detect transformer faults. Neural network has a strong pattern recognition classification ability and associative memory ability to the outside world, so neural network can be used for the transformer fault detection. Based on neural network to gases dissolved in transformer oil for the characteristic features of fault detection method for transformer fault detection offers a new way. Therefore. This article will use two different neural network (BP network, RBF network ) used in transformer fault detection, are introduced the basic structure of the network and the principle and design and simulation model. key words : transformer ,fault detection ,neural network ,BP algorithm ,