电力系统负荷非侵入式监测方法研究

doi:10.19595/j.cnki.1000-6753.tces.200622

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Abstract In order to monitor the power system load efficiently, this paper proposes a non-invasive monitoring method for its transient and steady-state working conditions. In order to accurately obtain the load working state at any steady-state time, an automatic screening optimized genetic algorithm (AOGA) is proposed, steady-state monitoring model, which transforms the power parameter model into active component model and reactive power component model, establishes double objective function, and solves the problem of monitoring error caused by small influence of high harmonic current and less solving parameters. The AOGA algorithm constructs a self screening program, which filters the results with the same fitness first, and then uses Euclidean distance to judge the power, which solves the defect of misjudgment caused by the same fitness in traditional genetic algorithm (GA). When the load is switched on and off, in order to obtain the switching type accurately, this paper establishes a power-time(P-T) based on power time, In the Matlab/Simulink model of transient monitoring, the discrete Fourier decomposition method is used to extract the power change before and after the transient occurrence, and the action load is identified by comparing the power matching degree; on the basis of power monitoring, the harmonic characteristics are identified by taking the harmonic content of the load as the load characteristics, which further improves the accuracy of the transient load monitoring.

Key words： Non-invasive automatic screening optimized genetic algorithm double objective function P-T mode harmonic characteristics

Received: 09 June 2020

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TM762

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	Articles by authors
	Lei Yiqin
	Sun Zhaolong
	Ye Zhihao
	Wu Xiaokang

Cite this article:

Lei Yiqin,Sun Zhaolong,Ye Zhihao等. Research on Non-Invasive Load Monitoring Method in Power System[J]. Transactions of China Electrotechnical Society, 2021, 36(11): 2288-2297.

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https://dgjsxb.ces-transaction.com/EN/10.19595/j.cnki.1000-6753.tces.200622 OR https://dgjsxb.ces-transaction.com/EN/Y2021/V36/I11/2288

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