基于相关性分析的电网非同步监测数据场景谐波责任划分

doi:10.11930/j.issn.1004-9649.202401107

中国电力 ›› 2025, Vol. 58 ›› Issue (1): 15-25.DOI: 10.11930/j.issn.1004-9649.202401107

• 电能质量及柔性输电技术 • 上一篇下一篇

基于相关性分析的电网非同步监测数据场景谐波责任划分

陈仕龙¹(), 吴涛¹, 郭成¹(), 毕贵红¹, 钱永亮²

1. 昆明理工大学电力工程学院，云南昆明　650500
2. 云南电网有限责任公司文山供电局，云南文山　663099

收稿日期:2024-01-25 接受日期:2024-07-25 出版日期:2025-01-28 发布日期:2025-01-23
作者简介:陈仕龙（1973—），男，博士，教授，从事电力系统新型继电保护、电能质量分析等研究，E-mail：chenshilong3@126.com
郭成（1978—），男，通信作者，博士，教授，从事电能质量分析与控制、电力系统性稳定分析等研究，E-mail：gc325@126.com
基金资助:
国家自然科学基金资助项目（特高压多端混合直流输电线路行波边界保护研究，52067009）。

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

Shilong CHEN¹(), Tao WU¹, Cheng GUO¹(), Guihong BI¹, Yongliang QIAN²

1. Faculty of Electric Power Engineering, Kunming University of Science and Technology, Kunming 650500, China
2. Wenshan Power Supply Bureau of Yunnan Power Grid Co., Ltd., Wenshan 663099, China

Received:2024-01-25 Accepted:2024-07-25 Online:2025-01-28 Published:2025-01-23
Supported by:
This work is supported by National Natural Science Foundation of China (Research on Travelling Wave Boundary Protection for Multi-terminal Hybrid UHVDC Transmission Line, No.52067009).

摘要/Abstract

摘要：

针对传统谐波责任划分方法需采用专门同步设备监测数据，且需基于等值电路模型划分谐波责任，工程应用较为复杂等不足，采用现有谐波监测装置非同步测量数据，提出一种综合考虑了数据非同步性、场景划分和数据相关性的谐波责任划分方法。首先，对原始非同步监测数据集采用分段聚合近似算法进行降噪预处理，利用形状动态时间规整算法（shape dynamic time warping，ShapeDTW）实现数据匹配对齐；然后，利用点排序识别聚类结构的聚类算法（ordering points to identify the clustering structure，OPTICS）划分场景以处理电力系统中因负荷投切和无功补偿装置切换等情况导致的谐波责任变化；最后，基于相关性分析构建场景谐波责任和总谐波责任指标，在指标构建的过程中引入了场景时长占比这一因素以得到更加科学合理的总谐波责任值。通过仿真验证和电网实例验证，该方法能基于现有非同步性监测数据实现各用户合理时间尺度动态谐波责任划分，可为工程上的快速谐波责任划分提供一定的新思路和新方法。

关键词: 电能质量, 谐波责任划分, 非同步监测数据, 场景划分, 相关性分析

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

陈仕龙, 吴涛, 郭成, 毕贵红, 钱永亮. 基于相关性分析的电网非同步监测数据场景谐波责任划分[J]. 中国电力, 2025, 58(1): 15-25.

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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链接本文: https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.202401107

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

图/表 17

图 1 多谐波源电力系统等效模型

Fig.1 Equivalent model of multi-harmonic source power system

图 2 3馈线7次谐波电流与母线7次谐波电压变化趋势

Fig.2 Variation of 7 th harmonic current of three feeders and 7 th harmonic voltage of bus

图 3 谐波源线性相关性散点图

Fig.3 Linear correlation scatter plot of harmonic sources

图 4 谐波监测数据采集示意

Fig.4 Harmonic monitoring data acquisition schematic

图 5 非同步采样下谐波监测数据散点图

Fig.5 Scatter plot of harmonic monitoring data by asynchronous sampling

图 6 谐波阻抗变化下谐波监测数据散点图

Fig.6 Scatter plot of harmonic monitoring data under harmonic impedance change

图 7 DTW距离对应与欧式距离对应对比

Fig.7 Comparison of DTW distance correspondence and Euclidean distance correspondence

图 8 合理匹配与不合理匹配示意

Fig.8 Schematic of reasonable and unreasonable matching

图 9 多谐波源等效电路

Fig.9 Equivalent circuit of multi-harmonic sources

表 1 仿真参数

Table 1 Simulation parameters

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

图 10 数据预处理效果

Fig.10 Data preprocessing effect

图 11 部分数据的匹配对齐

Fig.11 Matching alignment of partial data

图 12 OPTICS聚类的有序队列

Fig.12 Ordered queue of OPTICS clustering

表 2 各场景下馈线的谐波责任

Table 2 Harmonic responsibility of the feeder under each scenario

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

表 3 考虑场景时长占比后的谐波责任

Table 3 Harmonic responsibility with consideration of the scenario duration percentage

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

表 4 总谐波责任对比

Table 4 Total harmonic responsibility comparison

谐波源	总谐波责任对比/%
谐波源	本文方法	未考虑数据非同步性	未考虑谐波阻抗变化	未考虑各场景时间范畴
馈线1	31.38	59.24	25.38	40.94
馈线2	36.67	27.89	35.30	27.91
馈线3	31.95	12.87	39.32	31.16

表 5 实例分析的谐波责任划分

Table 5 Harmonic responsibility division in case study

场景	时长占比/%	谐波责任划分/%
场景	时长占比/%	馈线1	馈线2	馈线3
1	30.13	75.43	–40.54	53.21
2	21.92	93.70	–27.06	63.01
3	47.95	81.82	–35.11	69.56
全时段总谐波		67.59	–19.37	51.78

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基于相关性分析的电网非同步监测数据场景谐波责任划分

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

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基于相关性分析的电网非同步监测数据场景谐波责任划分

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

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