基于人工智能及信息融合的电力系统故障诊断方法

Power System Fault Diagnosis Based on Artificial Intelligence and Information Fusion

故障诊断软件作为调度中心综合智能告警模块的核心软件,其综合诊断性能的提高一直是电力工程界及学术界的热点问题之一。提出了基于信息融合思想的人工智能故障诊断新方法。该方法首先建立基于神经网络的信息融合故障诊断新模型,即利用神经网络对于信息所蕴含的特征提取能力,实现3种人工智能故障诊断方法权重的自动确定,以期获得更高的诊断精度;然后用测试样本对其进行测试,若精度满足要求则说明历史样本数量充足,则把训练好的神经网络直接用于对于新来的信息进行相应设备故障与否的诊断;否则,说明现有样本数量不足,使得基于神经网络实现信息融合的方法在该场景下不再适用,此时文中采用对于历史样本数量要求较少的基于自适应确定权重的信息融合策略,即基于故障诊断评价指标体系及客观权重法中的拉开档次法,自适应地确定3种人工智能故障诊断方法的权重,从而使得融合之后的故障诊断具有更高的诊断精度。不同样本下的算例均证明文中所提出的故障诊断新方法的综合故障诊断能力高于其他诊断方法。

It has been one of the hot topics for years in the fields of electrical engineering and academia how to improve the comprehensive diagnostic performance of fault diagnosis software as much as possible,which is the core software of integrated intelligent alarm module in the dispatch center.This paper proposes a new method of artificial intelligent(AI)fault diagnosis based on the idea of information fusion.First,a fault diagnosis fusion model based on the neural network(NN)is established,which can determine the weights of three AI basic fault diagnosis models automatically by using NN’s capability to extract features,so that the fault diagnosis model can achieve higher accuracy.Then,the model is checked with test samples:If the accuracy meets the requirements,which means that the amount of the historical samples is sufficient,and the trained NN is satisfactory and can be directly used for the fault diagnosis of the new information of the corresponding equipment.Otherwise,it indicates that the amount of current samples is not sufficient,which indicates the method of information fusion based on NN is no longer applicable in this situation.In this case,an information fusion method for fault diagnosis is adopted to determine adaptively the weight of the three AI basic fault diagnosis models through fault diagnosis evaluation system and scatter degree method to achieve the more satisfactory performance of the fault diagnosis,which more fits the scene with fewer historical samples.Examples of different samples demonstrate that the new fusion method proposed in this paper has better comprehensive performance of fault diagnosis in any case,compared with the other fault diagnosis methods.