摘要
同调机群识别在电力系统的动态等值、主动解列控制中具有重要意义。提出一种基于Prony分析特征提取的同调机组分群方法。首先针对Prony分析受噪声干扰严重的缺点,利用集成经验模式分解(EnsembleEmpirical Mode Decomposition,EEMD)方法对含噪声的信号降噪。然后对降噪后的功角信号进行Prony分析,提取功角信号的幅值、频率和阻尼特征值,形成每台机组的特征向量。最后将系统中所有机组特征向量组成的特征矩阵输入到自组织神经网络进行聚类,从而实现同调机组分群。EPRI-36节点系统和华北电网系统算例表明,所提方法可以很好地降低噪声影响,充分提取功角曲线特征,准确识别同调机组。
Coherent cluster identification plays an important role in the dynamic equivalent and active splitting control of power systems.This paper presents a coherency clustering method based on Prony analysis feature extraction.First,given the disadvantage of Prony analysis which is seriously disturbed by noise,the method of Ensemble Empirical Mode Decomposition(EEMD)is used to reduce the noise of the signal.Then,Prony analysis is performed on the angle signal to extract feature values such as amplitude,frequency,and damping to form the feature vector of each generator.Then the feature matrix formed by all the generator feature vectors in the system are input to a self-organizing neural network to realize coherency clustering.Taking the EPRI-36 bus system and North China grid as case studies,it is verified that this method can effectively eliminate the impact of noise on Prony analysis,fully extract the feature of the power angle signal,and accurately identify the coherency generator group.
作者
李高望
张智
李达
陈艳波
LI Gaowang;ZHANG Zhi;LI Da;CHEN Yanbo(State Grid Economic and Technological Research Institute Co.,Ltd.,Beijing 102209,China;School of Electrical&Electronic Engineering,North China Electric Power University,Beijing 102206,China)
出处
《电力系统保护与控制》
EI
CSCD
北大核心
2020年第22期91-99,共9页
Power System Protection and Control
基金
国家自然科学基金项目资助(51777067)“基于键合图理论的综合能源系统动态状态估计研究”
国网经济技术研究院有限公司自主投入科技项目资助(524414190002)“基于机电暂态-电磁暂态混合仿真技术的高压直流输电系统动态性能研究”。
关键词
同调机群
PRONY分析
集成经验模式分解
自组织神经网络
coherency clustering
Prony analysis
ensemble empirical mode decomposition
self-organizing neural network
