基于排列熵算法的电力系统故障信号分析

电网故障的快速准确诊断对加快事故处理和系统恢复进程、保证电力系统的安全运行具有至关重要的作用。分析了目前应用于电力系统故障诊断的数据来源,提出采用广域量测的电气量信息进行电力系统的快速诊断方法。首先将广域量测系统采集的数据进行小波变换,通过相关系数法滤噪处理,以此来重构信号,采用排列熵算法对重构的信号进行排列熵分析。由于排列熵反映了一维时间序列的复杂度,对信号变化具有较高的敏感性,可以应用于电力系统故障信号分析方面。相比传统的小波分析方法,该方法不存在选取小波基问题,且算法简单,编程易实现,能够满足在线故障诊断条件。通过对IEEE10机39节点标准测试系统的故障仿真分析,结果表明了该方法的有效性和实用性。

Power System Fault Signal Analysis Based on Permutation Entropy Algorithm

Rapid and accurate diagnosis of fault has a crucial role for speeding up the recovery process and ensuring the safe operation of the power system. This paper analyzes the data sources currently used in power system fault diagnosis, proposes the use of wide-area information for rapid diagnosis of power systems. First, wavelet transform is used to process the wide area measurement data, through correlation coefficient method to eliminate the noise and reconstruct the signal, then permutation entropy calculation use the of the reconstructed signal. Since the complexity of the arrangement of entropy reflects a one-dimensional time series, the signal changes with high sensitivity, therefore, it can be applied to power system fault diagnosis. Compared with the traditional wavelet analysis method, this method avoids the problem of selecting wavelet base and meets the conditions for online fault diagnosis. In addition, it is simple and easy to program. The approach is applied in IEEE10 machine 39-bus system fault diagnosis. The diagnostic results show the applicability and effectiveness of the method.