小波熵理论及其在电力系统故障检测中的应用研究

A STUDY OF WAVELET ENTROPY THEORYAND ITS APPLICATION IN ELECTRIC POWER SYSTEM FAULT DETECTION

电力系统暂态信号经小波变换后数据众多,且对故障的判别缺乏定量的手段,所以,挖掘和融合出一个或系列普适量来有效地检测电力系统故障或判别其稳定性至关重要。该文利用小波分析具有时频局部化特性和熵能对系统状态进行表征的特点,将小波分析和熵结合起来,定义了 3 种小波熵(小波能谱熵、小波时间熵、小波奇异熵),并给出其算法,揭示了这 3 种小波熵对系统故障表征的机理,对两种理论信号和基于 PSCAD/EMTDC 仿真的输电线路故障信号种小波熵能反映系统变化,且不受噪声干扰,能够有效地检测出电力系统故障。

The wavelet transform result data of electrical transient signals were abundant, and there was no quantitative method, so to mine and fuse one or a series of universal applicable quantities to detect system fault and stability was essential. Combining wavelet analysis with entropy theory by exploiting the time-frequency localization ability of wavelet analysis and the ability of entropy to token system state, three wavelet entropy concepts, i.e. wavelet energy entropy(WEE), wavelet time entropy(WTE), wavelet singularity entropy(WSE) were defined, and corresponding algorithms were put forward. The mechanism that these three wavelet entropies can token system fault were disclosed. Simulation results of two theoretic signals and transmission line PSCAD/EMTDC simulation signals indicate that wavelet entropy can reflect the system change, and they can eliminate the disturbance of noise and be applied to power system fault detection.