摘要
风电并网引起的电能质量扰动信号多为复合的暂态振荡信号,针对传统的检测算法存在模态混叠、易受干扰、通用性差等缺点,提出采用聚类经验模态分解EEMD与基于Morlet复小波的谱峭度相结合的算法实现该类扰动检测。为采集风电并网引起的相关电能质量扰动信号,在实时数字仿真RTDS平台搭建了200台双馈感应风力发电机DFIG的风电场并网模型。仿真结果表明,该算法能有效实现对风电并网引起的电能质量复合扰动信号的检测。
The power quality disturbances caused by wind power grid are mostly multiple disturbances including transient oscillation. According to the disadvantages of traditional detection methods,such as modal aliasing,susceptibilityto disturbance and poor universality. An algorithm of ensemble empirical mode decomposition（EEMD）combined withMorlet complex wavelet based spectral kurtosis（SK）is proposed to detect these kinds of power quality disturbances inthis paper. The wind farm model of 200 doubly-fed induction generators（DFIGs）is built on the platform of real time digital simulator（RTDS）to collect power quality disturbance signals related to wind power grid. The simulation resultsshow that the method can effectively detect the multiple power quality disturbances caused by wind power grid.
出处
《电力系统及其自动化学报》
CSCD
北大核心
2016年第7期1-6,共6页
Proceedings of the CSU-EPSA
基金
国家自然科学基金资助项目(U1434203
U1134205
51377136)
关键词
风电并网
电能质量
复合扰动
EEMD
Morlet复小波
谱峭度
实时数字仿真
wind power grid
power quality
multiple disturbances
ensemble empirical mode decomposition（EEMD）
Morlet complex wavelet
spectral kurtosis
real time digital simulator
