基于改进LMD方法的电压骤降检测与分析

随着新能源并网技术的普及和发展,大量非线性装置接入电网对其电能质量产生了一定影响,因此有必要对电能质量进行检测和分析。针对现有检测识别方法存在抗噪性和精确性不足的问题,提出一种改进的LMD方法。该方法首先对自适应分解方法筛选过程的机理进行研究,分析了极值点拟合分布程度容易受到高频间断信号干扰,提出对原始信号先加入受控高斯白噪声再进行LMD分解。其次针对特征参数提取部分存在端点能量泄漏问题,提出采用经验调制分解方法对瞬时参数进行检测的方式。通过仿真实验表明所提方法可以有效抑制模态混叠和端点效应。最后搭建了电压骤降实验平台,运用实测数据验证了所提方法能够准确提取电压骤降的各个扰动参数,从而为电能质量扰动分析提供了一种新思路。

Voltage sag detection and analysis based on a modified LMD method

With the popularization and development of new energy grid-connected technology, the large number of non-linear devices connected to the power grid have an impact on its power quality. Thus it is necessary to detect and analyze that impact. To help overcome the shortcomings of existing detection and recognition methods in anti-noise and accuracy, a modified LMD method is proposed in this paper. This approach first studies the mechanism of the selection process for the adaptive decomposition method, and then analyzes the degree of extreme point fitting distribution that is susceptible to high frequency and intermittent signal interference. It uses the noise-assisted decomposition method to add controlled Gaussian white noise to the original signal and then perform LMD decomposition. Then, taking into account end-point energy leakage in the feature parameter extraction, an empirical modulation decomposition method is proposed to detect instantaneous parameters. Simulation results show that the proposed method is able to effectively suppress mode mixing and endpoint effects. Finally, experimental data from the built power quality disturbance platform demonstrates that the proposed method is capable of accurately extracting all disturbance parameters of voltage sag. This also provides a novel method for power quality disturbance analysis. This work is supported by National Key Basic Research Program of China (No. 2016YFB0101800).