Harmonic Responsibility Division of Grid Asynchronous Monitoring Data Scenarios Based on Correlation Analysis

doi:10.11930/j.issn.1004-9649.202401107

Abstract

Abstract:

In view of the shortcomings of the traditional harmonic responsibility division methods, which require the use of dedicated synchronous equipment monitoring data and the division of harmonic responsibility based on equivalent circuit model, and are complex in engineering application, based on the asynchronous measurement data of the existing harmonic monitoring devices, a harmonic responsibility division method is proposed, which takes into account the asynchronous nature of the data, the division of the scenarios, and the correlation of the data. Firstly, the original asynchronous monitoring data set is preprocessed using piecewise aggregation approximation (PAA) algorithm for noise reduction, and then the ShapeDTW algorithm is used to realize the data matching and alignment. Secondly, the OPTICS algorithm is used to divide the scenarios for dealing with the harmonic responsibility changes in power system caused by switching of the power load and the reactive power compensation devices. Finally, the harmonic responsibility of the scenarios and the total harmonic responsibility indexes are constructed based on correlation analysis, and the factor of scenario duration percentage is introduced in the process of indexes construction to .get a more scientific and reasonable total harmonic responsibility value. The simulation verification and grid example verification show that the proposed method can realize the dynamic harmonic responsibility division of each user in a reasonable time scale based on the existing asynchronous monitoring data, which can provide new ideas and methods for fast harmonic responsibility division in engineering.

Key words: power quality, responsibility division, asynchronous monitoring data, scenario division, correlation analysis

Shilong CHEN, Tao WU, Cheng GUO, Guihong BI, Yongliang QIAN. Harmonic Responsibility Division of Grid Asynchronous Monitoring Data Scenarios Based on Correlation Analysis[J]. Electric Power, 2025, 58(1): 15-25.

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URL: https://www.electricpower.com.cn/EN/10.11930/j.issn.1004-9649.202401107

https://www.electricpower.com.cn/EN/Y2025/V58/I1/15

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References 26

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场景（样本点数）	非同步采样时间差/s	系统侧/馈线侧阻抗	Z_x/Ω	Z_y/Ω
场景1 (1—440)	0	Z_S	20.15	7.89
		Z_c1	12.35	11.27
		Z_c2	22.75	21.36
		Z_c3	43.85	43.12
场景2 （441—1160）	–2	Z_S	12.15	6.57
		Z_c1	8.19	11.34
		Z_c2	17.34	2.77
		Z_c3	38.53	45.27
场景3 （1161—2600）	+3	Z_S	25.36	8.73
		Z_c1	16.31	9.87
		Z_c2	20.75	16.15
		Z_c3	25.39	11.68
场景4 （2601—4320）	–1	Z_S	8.91	3.26
		Z_c1	20.39	12.62
		Z_c2	18.56	16.21
		Z_c3	30.21	16.45

场景（样本点数）	非同步采样时间差/s	系统侧/馈线侧阻抗	Z_x/Ω	Z_y/Ω
场景1 (1—440)	0	Z_S	20.15	7.89
		Z_c1	12.35	11.27
		Z_c2	22.75	21.36
		Z_c3	43.85	43.12
场景2 （441—1160）	–2	Z_S	12.15	6.57
		Z_c1	8.19	11.34
		Z_c2	17.34	2.77
		Z_c3	38.53	45.27
场景3 （1161—2600）	+3	Z_S	25.36	8.73
		Z_c1	16.31	9.87
		Z_c2	20.75	16.15
		Z_c3	25.39	11.68
场景4 （2601—4320）	–1	Z_S	8.91	3.26
		Z_c1	20.39	12.62
		Z_c2	18.56	16.21
		Z_c3	30.21	16.45

场景	谐波责任占比/%
场景	馈线1	馈线2	馈线3
1	61.31	–2.77	41.46
2	57.78	30.79	11.43
3	35.27	22.56	42.17
4	9.40	61.04	29.56

场景	谐波责任占比/%
场景	馈线1	馈线2	馈线3
1	61.31	–2.77	41.46
2	57.78	30.79	11.43
3	35.27	22.56	42.17
4	9.40	61.04	29.56

场景	时长占比/%	谐波责任占比/%
场景	时长占比/%	馈线1	馈线2	馈线3
1	10.19	6.25	–0.28	4.22
2	16.67	9.63	5.13	1.91
3	33.33	11.76	7.52	14.06
4	39.81	3.74	24.30	11.77