Journal of Modern Power Systems and Clean Energy

ISSN 2196-5625 CN 32-1884/TK

Non-intrusive Load Monitoring Based on Graph Total Variation for Residential Appliances

1.College of Electrical Engineering, Sichuan University, Chengdu 610065, China;2.State Grid Chongqing Electric Power Company, Chongqing 400014, China;3.State Grid Sichuan Electric Power Company, Chengdu 610095, China;4.State Grid Hubei Electric Power Company Limited Research Institute, Wuhan 430077, China

Fund Project:

This work was supported by National Natural Science Foundation of China (No. 52107117).

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    Non-intrusive load monitoring is a technique for monitoring the operating conditions of electrical appliances by collecting the aggregated electrical information at the household power inlet. Despite several studies on the mining of unique load characteristics, few studies have extensively considered the high computational burden and sample training. Based on low-frequency sampling data, a non-intrusive load monitoring algorithm utilizing the graph total variation (GTV) is proposed in this study. The algorithm can effectively depict the load state without the need for prior training. First, the combined K-means clustering algorithm and graph signals are used to build concise and accurate graph structures as load models. The GTV representing the internal structure of the graph signal is introduced as the optimization model and solved using the augmented Lagrangian iterative algorithm. The introduction of the difference operator decreases the computing cost and addresses the inaccurate reconstruction of the graph signal. With low-frequency sampling data, the algorithm only requires a little prior data and no training, thereby reducing the computing cost. Experiments conducted using the reference energy disaggregation dataset and almanac of minutely power dataset demonstrated the stable superiority of the algorithm and its low computational burden.

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  • Received:September 09,2022
  • Revised:March 16,2023
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  • Online: May 20,2024
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