Time-Delay Correlation Analysis of Wide-Area PMU Control Signals in Complex Communication Scenarios

Jianbo Yi, Zhendong Liu, Zhenyuan Zhang, Jian Li, Qi Huang
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Abstract

Power system wide-area measurement and control signals based on phasor measurement unit (PMU) may exist in complex communication scenarios. This makes the problems of time delay, packet loss, noise and control signal distortion caused by chaotic time series. This paper presents a novel prediction compensation structure, which combines the signal hierarchical filtering method with gray prediction algorithm. First, utilize complete ensemble empirical decomposition with adaptive noise (CEEMDAN) method to decompose the PMU measurement signal into components with different frequency scales and use WOLF method to determine the chaotic characteristics of each frequency band component. Second, based on the analysis results of singular spectrum analysis (SSA), after removing the weak correlation information and noise corresponding to small singular values, reconstruct the known chaotic components. Third, add the components of each frequency scale predicted by gray Verhulst prediction method to get the final control input signal without time delay interference. Finally, the paper takes 5 communication scenarios to analyze that the proposed compensation scheme has a good compensation effect under different delays and packet loss.
复杂通信场景下广域PMU控制信号的时延相关分析
在复杂的通信场景中,可能存在基于相量测量单元(PMU)的电力系统广域测控信号。这就产生了混沌时间序列引起的时间延迟、丢包、噪声和控制信号失真等问题。提出了一种将信号层次滤波方法与灰色预测算法相结合的新型预测补偿结构。首先,利用CEEMDAN (complete ensemble empirical decomposition with adaptive noise)方法将PMU测量信号分解成不同频率尺度的分量,并利用WOLF方法确定各频段分量的混沌特性。其次,基于奇异谱分析(SSA)的分析结果,去除小奇异值对应的弱相关信息和噪声后,重构已知的混沌分量。第三,将灰色Verhulst预测法预测的各频率尺度分量相加,得到无时延干扰的最终控制输入信号。最后,本文以5种通信场景分析了所提出的补偿方案在不同时延和丢包情况下具有良好的补偿效果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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