基于近邻传播聚类和遗传优化的非侵入式负荷分解方法

doi:10.19595/j.cnki.1000-6753.tces.170894

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Abstract Research on load monitoring and decomposition has broad prospects. Electricity information can be refined to internal load composition details, which has high application value for the two-way interaction and demand-side management in the smart grid, achieving benefits to electric power companies and power consumers. However, the existing non-intrusive load monitoring and disaggregation (NILMD) methods lack the concern about electricity consumption characteristics and solution to multi-state electrical appliances. Thus, this paper considers the difference between load patterns, and then divides the loads of weekdays and weekends separately into certain clusters based on the affinity propagation (AP) method. After that, combining with the conditional judgment to working state rationality, this paper realizes the decomposition of different electrical appliances and the corresponding state-changes from the total load signal according to multi-feature genetic iteration. The case study shows that the proposed method can effectively identify various electrical appliances. The proposed method uses steady state power signatures, reduces hardware cost to some extent, and is friendly to the general sampling equipment.

Key words： Load decomposition non-intrusive load monitoring genetic algorithm affinity propagation clustering analysis

Received: 23 June 2017 Published: 03 September 2018

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TM714

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	Xu Qingshan
	Lou Oudie
	Zheng Aixia
	Liu Yujun

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Xu Qingshan,Lou Oudie,Zheng Aixia等. A Non-Intrusive Load Decomposition Method Based on Affinity Propagation and Genetic Algorithm Optimization[J]. Transactions of China Electrotechnical Society, 2018, 33(16): 3868-3878.

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https://dgjsxb.ces-transaction.com/EN/10.19595/j.cnki.1000-6753.tces.170894 OR https://dgjsxb.ces-transaction.com/EN/Y2018/V33/I16/3868

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