Identification of distribution network topology parameters based on multidimensional operation data
The connection relationship of distribution network topology is of great significance for the maintenance and fault diagnosis of distribution network, and scheduled power outage optimization. At present, the verification of topological documents mainly relies on on-site inspection, which consumes a...
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doaj-b4e8bdc816ce4c78825257a7a3a506892021-04-14T04:16:14ZengElsevierEnergy Reports2352-48472021-04-017304311Identification of distribution network topology parameters based on multidimensional operation dataJiaqiao Li0Di Wu1Weichao Jin2Zhenyue Chu3Shengyuan Liu4Jien Ma5Zhenzhi Lin6Li Yang7School of Electrical Engineering, Zhejiang University, Hangzhou 310027, ChinaSchool of Electrical Engineering, Zhejiang University, Hangzhou 310027, ChinaSchool of Electrical Engineering, Zhejiang University, Hangzhou 310027, ChinaSchool of Electrical Engineering, Zhejiang University, Hangzhou 310027, ChinaSchool of Electrical Engineering, Zhejiang University, Hangzhou 310027, ChinaSchool of Electrical Engineering, Zhejiang University, Hangzhou 310027, China; Corresponding author.School of Electrical Engineering, Zhejiang University, Hangzhou 310027, China; School of Electrical Engineering, Shandong University, Jinan 250061, ChinaSchool of Electrical Engineering, Zhejiang University, Hangzhou 310027, ChinaThe connection relationship of distribution network topology is of great significance for the maintenance and fault diagnosis of distribution network, and scheduled power outage optimization. At present, the verification of topological documents mainly relies on on-site inspection, which consumes a lot of manpower and material resources and is inefficient. Therefore, an efficient method for topology verification of low-voltage substation areas is required. Given this background, a model for error correction and user access phase identification of low-voltage stations based on multi-dimensional voltage data collected by smart meters is presented in this paper, which can provide a certain reference for topology identification and line troubleshooting of low-voltage substations. First, the t-Distributed Stochastic Neighbor Embedding (t-SNE) algorithm and the Principal Component Analysis (PCA) performs dimensionality reduction on the original load data to solve the problem of redundancy caused by the high dimension of the original voltage data set. Second, the Local Outlier Factor (LOF) algorithm is used to identify abnormal samples in the voltage data set. Then, the spectral clustering method is used to cluster the dimensionality-reduced load data to realize the phase identification of single-phase users in the low-voltage station area. Finally, the real data of a certain area in Haining, Zhejiang Province of China are used as simulation cases for demonstrating. The results of the case studies show that the model proposed in this paper is feasible and effective.http://www.sciencedirect.com/science/article/pii/S2352484721000664Station–user relationshipPhase identificationt-Distributed Stochastic Neighbor EmbeddingPrincipal Component AnalysisLocal Outlier FactorSpectral clustering |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Jiaqiao Li Di Wu Weichao Jin Zhenyue Chu Shengyuan Liu Jien Ma Zhenzhi Lin Li Yang |
spellingShingle |
Jiaqiao Li Di Wu Weichao Jin Zhenyue Chu Shengyuan Liu Jien Ma Zhenzhi Lin Li Yang Identification of distribution network topology parameters based on multidimensional operation data Energy Reports Station–user relationship Phase identification t-Distributed Stochastic Neighbor Embedding Principal Component Analysis Local Outlier Factor Spectral clustering |
author_facet |
Jiaqiao Li Di Wu Weichao Jin Zhenyue Chu Shengyuan Liu Jien Ma Zhenzhi Lin Li Yang |
author_sort |
Jiaqiao Li |
title |
Identification of distribution network topology parameters based on multidimensional operation data |
title_short |
Identification of distribution network topology parameters based on multidimensional operation data |
title_full |
Identification of distribution network topology parameters based on multidimensional operation data |
title_fullStr |
Identification of distribution network topology parameters based on multidimensional operation data |
title_full_unstemmed |
Identification of distribution network topology parameters based on multidimensional operation data |
title_sort |
identification of distribution network topology parameters based on multidimensional operation data |
publisher |
Elsevier |
series |
Energy Reports |
issn |
2352-4847 |
publishDate |
2021-04-01 |
description |
The connection relationship of distribution network topology is of great significance for the maintenance and fault diagnosis of distribution network, and scheduled power outage optimization. At present, the verification of topological documents mainly relies on on-site inspection, which consumes a lot of manpower and material resources and is inefficient. Therefore, an efficient method for topology verification of low-voltage substation areas is required. Given this background, a model for error correction and user access phase identification of low-voltage stations based on multi-dimensional voltage data collected by smart meters is presented in this paper, which can provide a certain reference for topology identification and line troubleshooting of low-voltage substations. First, the t-Distributed Stochastic Neighbor Embedding (t-SNE) algorithm and the Principal Component Analysis (PCA) performs dimensionality reduction on the original load data to solve the problem of redundancy caused by the high dimension of the original voltage data set. Second, the Local Outlier Factor (LOF) algorithm is used to identify abnormal samples in the voltage data set. Then, the spectral clustering method is used to cluster the dimensionality-reduced load data to realize the phase identification of single-phase users in the low-voltage station area. Finally, the real data of a certain area in Haining, Zhejiang Province of China are used as simulation cases for demonstrating. The results of the case studies show that the model proposed in this paper is feasible and effective. |
topic |
Station–user relationship Phase identification t-Distributed Stochastic Neighbor Embedding Principal Component Analysis Local Outlier Factor Spectral clustering |
url |
http://www.sciencedirect.com/science/article/pii/S2352484721000664 |
work_keys_str_mv |
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