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Estimation of Low Frequency Oscillation Parameters Using Singular Value Decomposition Combined Group Search Optimizer

机译:基于奇异值分解组合群搜索优化器的低频振荡参数估计

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摘要

This article presents a scheme using singular value decomposition (SVD) combined group search optimizer (GSO) algorithm to estimate the parameters of low frequency oscillation (LFO) in power grids. Firstly, Mathematical morphology (MM) is adopted as a preprocessing method. Secondly, SVD is applied to identify the number of modes in a LFO event. Finally, parameters of each mode are identified and determined by GSO. In order to demonstrate the accuracy and efficiency of the scheme proposed in this article, three simulation cases are implemented, which use predefined parameters, real-time digital system and wide-area measurement signal data collected from North American SynchroPhasor Initiative, respectively. The analysis results indicate that the proposed scheme can be applied in real-time monitoring environment on digital signal processor platform within short computation time, even in heavy noisy environment.
机译:本文提出了一种使用奇异值分解(SVD)组合组搜索优化器(GSO)算法来估计电网中低频振荡(LFO)参数的方案。首先,采用数学形态学(MM)作为预处理方法。其次,SVD用于识别LFO事件中的模式数量。最后,由GSO识别和确定每种模式的参数。为了证明本文提出的方案的准确性和效率,实现了三个仿真案例,分别使用预定义的参数,实时数字系统和从北美同步相量计划收集的广域测量信号数据。分析结果表明,所提出的方案即使在嘈杂的环境下,也可以在较短的计算时间内应用于数字信号处理器平台的实时监控环境。

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