风电接入的电力系统电能质量扰动的检测与定位

风能的波动性、间歇性和随机性等特性使接入风电的电力系统运行特性和电能质量受到复杂的影响。针对风电接入的电力系统电能质量扰动问题，重点研究电能质量扰动的小波检测方法，提出了基于Euclidean分解算法的db4复小波的提升方案。通过Euclidean分解算法得到复小波提升方案，求取了db4复小波自适应提升因子并构建了分解与重构模型，对扰动信号和基波分量进行提升变换后得到幅值和相位信息分别作差。利用幅值差和相位差来确定扰动的幅度和时间，并根据扰动段的幅值差和相位差值实现了扰动起止时刻定位。基于Matlab的仿真结果表明，与复小波相比，该方法能进一步提高风电接入电力系统电能质量扰动信号定位的速度和精度。

Detection and localization of power quality disturbance for power system connected with wind energy generation

The operation characteristics and power quality of power system are affected by the properties of wind power generation such as fluctuation, intermittence, randomicity, and so on. Aiming at the problems of power quality disturbance for wind energy integrated power system, this paper mainly researches the wavelet detection method of power quality disturbance, and proposes the lifting scheme of db4 complex wavelet based on the Euclidean decomposition principle. The Euclidean decomposition principle is used to obtain complex wavelet lifting scheme, the db4 adaptive lifting factor of complex wavelet is calculated, and the decomposition model and the reconstruction model are built. By lifting and transforming disturbance signal and fundamental component, the differences of the amplitude and phase are obtained which can be used to determine the disturbance amplitude and time and then realize the location of disturbance start-stop moment. Compared with complex wavelet, the simulation results based on Matlab show that the proposed algorithm further enhances the speed and accuracy of power quality disturbance location for wind energy integrated power system. This work is supported by Hunan Province Natural Science Foundation of Zhuzhou United Foundation (No. 2016JJ5033).