Non-Intrusive Load Monitoring Method for Resident Users Based on Alternating Optimization in Graph Signal

被引：0

作者：

Feng R. ^{[1
]}

Yuan W. ^{[1
]}

Ge L. ^{[1
]}

机构：

[1] School of Electrical and Information Engineering, Tianjin University, Nankai District, Tianjin

来源：

Zhongguo Dianji Gongcheng Xuebao/Proceedings of the Chinese Society of Electrical Engineering | 2022年 / 42卷 / 04期

基金：

中国国家自然科学基金;

关键词：

Alternating optimization; Graph signal; Non-intrusive load monitoring; Power constraint;

D O I：

10.13334/j.0258-8013.pcsee.210299

中图分类号：

学科分类号：

摘要：

Non-intrusive load monitoring (NILM) is a common method to research load information of resident users. However, it has some problems, such as low disaggregation accuracy and lacks of generalization and etc. Therefore, an NILM-alternating optimization (NILM-AO) method based on graph signal processing (GSP) theory was proposed. A graph signal model was constructed based on the total load data, and a power loss constraint can be obtained by the graph signal model to solve the lack of load data correlation research in traditional methods. Compared with the traditional method that requires altering model parameters, NILM-AO finds an optimal model parameter automatically, which improves the capability of real-time monitoring and decreases the operation cost of power grid. Simulation results show that NILM-AO improves the accuracy for 15%, and decreases the calculation time for 10% under 1-min sampling rate, which indicates the superiority of NILM-AO. © 2022 Chin. Soc. for Elec. Eng.

引用

页码：1355 / 1364

页数：9

共 22 条

[1]

WANG Ying, YANG Wei, XIAO Xianyong, Et al., Non-intrusive residential load monitoring method based on fine identification of U-I trajectory curve, Power System Technology, 45, 10, pp. 4104-4113, (2021)

[2]

LEI Yiqin, SUN Zhaolong, YE Zhihao, Et al., Research on non-intrusive monitoring method of power system load, Transactions of China Electrotechnical Society, 36, 11, pp. 2288-2297, (2021)

[3]

WANG Ke, ZHONG Haiwang, YU Nanpeng, Et al., Nonintrusive load monitoring based on sequence-to-sequence model with Attention mechanism, Proceedings of the CSEE, 39, 1, pp. 75-83, (2019)

[4]

YAN Xufeng, ZHAI Shaopeng, WANG Zhihua, Et al., Application of deep neural network in non-intrusive load disaggregation, Automation of Electric Power Systems, 43, 1, pp. 126-132, (2019)

[5]

BONFIGLI R, PRINCIPI E, FAGIANI M, Et al., Non-intrusive load monitoring by using active and reactive power in additive factorial hidden Markov models, Applied Energy, 208, pp. 1590-1607, (2017)

[6]

MAKONIN S, POPOWICH F, BAJIC I V, Et al., Exploiting HMM sparsity to perform online real-time nonintrusive load monitoring, IEEE Transactions on Smart Grid, 7, 6, pp. 2575-2585, (2016)

[7]

TANAKA Y., Spectral domain sampling of graph signals, IEEE Transactions on Signal Processing, 66, 14, pp. 3752-3767, (2018)

[8]

ARAGHI H, SABBAQI M, BABAIE-ZADEH M, Et al., K-Graphs: an algorithm for graph signal clustering and multiple graph learning, IEEE Signal Processing Letters, 26, 10, pp. 1486-1490, (2019)

[9]

LIU Daowei, ZHANG Rui, LI Baiqing, Et al., Research on reverse recognition method of power system topological structure based on state measurement, Proceedings of the CSEE, 39, 24, pp. 7107-7117, (2019)

[10]

QI Bing, LIU Liya, WU Xin, Et al., Power signal disaggregation for smart meter based on graph signal processing, Automation of Electric Power Systems, 43, 4, pp. 79-85, (2019)

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