Forecasting the fault and danger in power system and limiting their incidence have been the emphasis in modern power system study. The abundant real-time data gathering in power system contain the complexity and uncertainty of the system, so mining and fusing a series of universal applicable quantities from these data to detect system fault and its stability is essential. Combining wavelet analysis, the modern time-frequency analysis technique, with entropy theory by exploiting their virtue will resolve the problem. In this paper, the feasibility of the wavelet entropy application in power system fault detection and distinguish is analyzed, the wavelet entropy concept based on wavelet analysis is defined, the computation methods of the two kinds of wavelet entropies are established, and the application of the wavelet entropy in transmission line fault detection is simulated and analyzed.

暂态信号分析是电力系统故障诊断和暂态保护的基础和依据，小波变换为暂态信号分析提供了强有力的数学工具。本文讨论了电力系统中常见的暂态信号分析应用，如电力暂态信号检测、去噪与数据压缩以及暂态信号定位等，对这些应用中小波基的选择原则进行了系统的分析研究。仿真分析结果进一步说明了文中提出的选择原则正确性，为小波分析在电力系统中的应用提供了有意义的参考。

本文在介绍多小波基本理论的基础上，探讨了基于多小波的信号去噪方法，针对目前多小波去噪的不足，提出一种基于自适应阈值的多小波去噪方法，应用于电力系统输电线故障暂态信号的去噪，并与基于传统阈值多小波和小波的去噪效果进行了比较，仿真结果表明，该方法可以根据实际信号自适应改变阈值大小，在去噪效果上优于传统多小波去噪方法。

在论述基于多分辨分析的小波基构造方法和小波分解理论的基础上，建立了基于离散小波变换的电力暂态信号数据压缩方法。针对电力故障信号基波伴随短时暂态成分的特点，研究了基于信号离散小波逼近品质，即离散逼近时域二范数最大的小波基优化方法。对一实际500kv输电线路故障暂态的分析计算表明，基于优化小波基的离散小波变换对电力故障暂态数据具有较高的压缩比和较小的重构误差。