基于概率神经网络的高压断路器故障诊断

High voltage circuit breaker fault diagnosis of probabilistic neural network

高压断路器是最重要的电力设备之一,在电力系统中起控制和保护作用。为了提高高压断路器故障诊断的准确率,提出了一种基于概率神经网络(PNN)的高压断路器故障诊断方法。该方法在分析高压断路器的故障特性来确定特征信号的基础上建立了PNN故障诊断模型,该模型将采集的特征数据作为网络的输入,通过Parzen窗估计法得到类条件概率密度,进而按Bayes决策规则对特征数据进行分类。经仿真表明,概率神经网络故障诊断模型具有收敛速度快、故障诊断准确率高、容易训练等特点。因此,该方法是一种有效的故障诊断方法,具有良好的应用前景。

The high voltage circuit breaker is one of the most important electrical equipments, which controls and protects the power system. In order to improve the accuracy of fault diagnosis of high voltage circuit breakers, a fault diagnosis method of high voltage circuit breakers is proposed based on probabilistic neural network （PNN）. This paper establishes PNN fault diagnosis model on the basis of analyzing the failure characteristics of high voltage circuit breaker to determine the characteristics of the signal. The model takes the collected feature data as the input of the network to get the class conditional probabilistic density function by Parzen window estimation method, then classifies characteristic data according to the Bayes decision rules. The simulation verifies that the probabilistic neural network fault diagnosis model has fast convergence, high fault diagnosis accuracy, easy to train and so on. Therefore, this method is an effective method of fault diagnosing and has good prospects.