基于加权最小绝对值状态估计的有源配电网拓扑辨识方法

doi:10.11930/j.issn.1004-9649.202307049

摘要/Abstract

摘要：

在配电系统状态估计中，一方面众多网络设备难以完全监控导致量测数据不足，另一方面，频繁的拓扑切换亟须开发高效的算法。因此，提出了一种配电网拓扑辨识的加权最小绝对值状态估计方法。首先，对传统基于线性规划的加权最小绝对值状态估计方法进行改进，以提高计算效率；其次，在重新制定的加权最小绝对值状态估计方法中加入补充变量和约束，以适应拓扑辨识问题，形成了一个基于混合整数线性规划的加权最小绝对值状态估计方法；最后，4个算例系统的仿真结果验证了所提方法在不同数量的实时测量、高伪测量误差、被坏数据破坏的测量以及未知支路状态场景下的优异性能。

关键词: 有源配电系统, 状态估计, 拓扑辨识, 加权最小绝对值, 混合整数线性规划

Abstract:

In the state estimation of distribution systems, it is difficult to fully monitor numerous network devices, resulting in insufficient measurement data. Besides, frequent topology switching urgently requires efficient algorithms. Therefore, this paper proposed an effective weighted minimum absolute value state estimation method for distribution network topology identification. Firstly, the traditional weighted minimum absolute value state estimation method based on linear programming was improved to improve the calculation efficiency. Secondly, a weighted minimum absolute value state estimation method based on mixed integer linear programming was formed by adding supplementary variables and constraints to the reformulated weighted minimum absolute value state estimation method to adapt to the topology identification problem. The simulation results on four example systems validate the excellent performance of the proposed topology identification method in different amounts of real-time measurements, high pseudo measurement errors, measurements corrupted by bad data, and unknown branch state scenarios.

Key words: distribution system, state estimation, topology identification, weighted minimum absolute value, mixed integer linear programming

马光, 朱文, 顾慧杰, 赵化时, 何锡祺, 陈世杰. 基于加权最小绝对值状态估计的有源配电网拓扑辨识方法[J]. 中国电力, 2024, 57(1): 167-174.

Guang MA, Wen ZHU, Huijie GU, Huashi ZHAO, Xiqi HE, Shijie CHEN. Topology Identification Method for Active Distribution Network Based on Weighted Minimum Absolute Value State Estimation[J]. Electric Power, 2024, 57(1): 167-174.

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链接本文: https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.202307049

https://www.electricpower.com.cn/CN/Y2024/V57/I1/167

图/表 6

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系统	状态估计方法	平均迭代次数	平均求解时间/s	E_MAP/%
33节点	本文	3.10	17.56	0.51
33节点	传统	3.10	29.87	0.49
69节点	本文	3.20	22.27	0.42
69节点	传统	3.10	42.27	0.40
123节点	本文	3.16	25.27	0.49
123节点	传统	3.01	71.19	0.48
417节点	本文	3.10	174.64	0.46
417节点	传统	3.10	1956.12	0.46

系统	状态估计方法	平均迭代次数	平均求解时间/s	E_MAP/%
33节点	本文	3.10	17.56	0.51
33节点	传统	3.10	29.87	0.49
69节点	本文	3.20	22.27	0.42
69节点	传统	3.10	42.27	0.40
123节点	本文	3.16	25.27	0.49
123节点	传统	3.01	71.19	0.48
417节点	本文	3.10	174.64	0.46
417节点	传统	3.10	1956.12	0.46

测量类型		位置（节点/支路）	误差/%
实时测量	电压幅值	任意	1
实时测量	潮流	任意	3
伪测量	注入功率	所有负荷节点	50
虚拟测量	注入功率	28, 39, 57, 92, 104	–
虚拟测量	参考角	1	–

测量类型		位置（节点/支路）	误差/%
实时测量	电压幅值	任意	1
实时测量	潮流	任意	3
伪测量	注入功率	所有负荷节点	50
虚拟测量	注入功率	28, 39, 57, 92, 104	–
虚拟测量	参考角	1	–

α_rt /%	方法	A/%	E_MAP/%	平均求解时间/s
80	本文	100	0.04	9.20
80	文献[14]	45	0.06	55.84
70	本文	94	0.05	13.77
70	文献[14]	37	0.20	55.48
60	本文	98	0.05	14.14
60	文献[14]	55	0.10	61.34
50	本文	100	0.05	16.26
50	文献[14]	33	0.12	90.21
40	本文	93	0.07	78.21
40	文献[14]	24	0.10	104.56