基于融合极限学习机的局部放电模式识别

局部放电(PD)是配电设备绝缘故障早期的主要表现形式,放电类型的模式识别对于设备绝缘性能的判定具有重要意义。考虑到极限学习机(ELM)法结构简单、训练速度快,但初始参数选取随机性大,算法稳定性不够的特点,提出一种基于融合ELM算法的PD模式识别方法,综合考虑不同特征判断准确率的差异,采用自适应权值分配对子分类器输出结果实现决策级融合。文中设计了4种放电物理模型来模拟典型的设备绝缘缺陷,采用高频电流法对PD信号波形和相位-幅值谱图(PRPD)进行采集,获得足够样本的实验数据,提取时频域及统计特征值进行分类。结果表明融合ELM算法在保证训练速度的同时,在识别正确率和稳定性上均优于传统ELM算法和反向传播(BP)神经网络。

Pattern recognition of partial discharge based on fusion extreme learning machine

Partial discharge is the main form of early insulation failure of electrical equipment. Pattern recognition of discharge type is of great significance for the estimation of equipment insulation performance. Considering that the extreme learning machine (ELM) method has the advantages of simple structure and fast training speed, yet the initial parameter selection is random and the algorithm is unstable. A pattern recognition method based on fusion ELM algorithm for partial discharge is proposed. Considering the different judgement precisions based on variable features, the adaptive weight assignment is used to achieve the decision-level fusion of the output. In this paper, four physic discharge models are designed to simulate typical partial discharge defects. Discharge signal waveform and phase-amplitude spectrum is collected by high-frequency current transformer method, sufficient samples of experiment data are obtained to extract time-frequency domain and statistical features for classification. The result shows that the fusion ELM algorithm is superior to the traditional ELM algorithm and BP neural network in the recognition accuracy and stability without sacrificing training speed.