基于时频相似度的油纸绝缘多局部放电源脉冲群分离与识别策略

Pulses Separation and Recognition Strategy for Multiple Partial Discharge Sources of Oil-Paper Insulation Based on Time-Frequency Similarity

由于变压器内部液-固绝缘结构和运行环境的复杂性,导致其内部往往是多个局部放电源同时存在,引起局部放电PRPD图谱交叉和重叠而无法识别。为解决多局部放电源的识别问题,本文提出了基于脉冲时频分析(TFA)结合近邻传播聚类(APC)的变压器油纸绝缘多局部放电源脉冲群分离与识别策略。首先,将基于S变换(ST)时频分布的脉冲相似度(时频相似度)矩阵输入近邻传播聚类(APC)分离多局部放电源脉冲群。然后,采用局部放电相位分布(PRPD)统计特征与粒子群优化支持向量机(PSO-SVM)识别ST+APC分离的子脉冲群,并验证分离的有效性。对实验室人工缺陷模型的局部放电数据进行分析,结果表明ST+APC算法可以有效去除脉冲型干扰(PSN)和分离油纸绝缘多局部放电源脉冲群。

Multiple partial discharge(PD) sources are often generated within power transformers due to the complexity of liquid-solid insulation system and operation condition, which would give rise to crossover and overlap of the registered PRPD patterns and incorrect diagnosis. To solve the problem of multiple PD sources recognition, a new method based on time-frequency analysis(TFA) of PD pulses and affinity propagation clustering(APC) is proposed for pulses separation and recognition of multiple PD sources of oil-paper insulation in transformers. The multiple PD pulses are firstly separated by input the S transform(ST) based time-frequency similarity matrix into affinity propagation clustering(APC) algorithm. Then, a support vector machine with particle swarm optimization(PSO-SVM) classifier based on PRPD statistical features is employed to obtain the recognition results of PRPD sub-patterns relevant to each PD source, and thereby examine the separation effectiveness. The PD data of artificial defect models acquired in laboratory are adopted for algorithms testing. It is shown that ST combined with APC can effectively eliminate pulse-shaped noises(PSN) and separate pulses of multiple PD sources.