Phase Identification of Low Voltage Distribution Network Based on t-SNE Dimension Reduction and Affinity Propagation Clustering Algorithm

doi:10.11930/j.issn.1004-9649.202211004

Abstract

Abstract: The widespread popularity of smart meters and the establishment of advanced measurement infrastructure (AMI) provide a large amount of monitoring information and measurement data for the analysis of the operation of distribution networks, while the change of phase information of users in the station area brings difficulties to the accurate understand of the operation of the station area. Aiming at the problem of phase recognition of users in the station area, a phase recognition method is proposed based on t-distributed stochastic neighbor embedding (t-SNE) feature extraction and affinity propagation (AP) clustering algorithm of user voltage data. Firstly, the extracted user’s voltage data is processed by Z-score data standardization, and the data features are extracted by t-SNE dimensionality reduction. And then phase identification for the user is made with radial propagation clustering algorithm. Two districts in a city are selected for case study. The recognition effects of different recognition methods are compared using evaluation indicators, and the effects of different acquisition frequencies and different measurement errors on the recognition effects are analyzed. The accuracy of the proposed method is verified by the actual cases, which shows that the proposed method can effectively solve the problem of user phase identification in the station area.

Key words: low voltage distribution network, phase identification, machine learning, t-distributed stochastic neighbor embedding, affinity propagation clustering algorithm

LIU Shoucheng, WANG Chun, ZOU Zhihui, CHEN Jiahui, ZHOU Han, LIU Wei, ZHANG Xu. Phase Identification of Low Voltage Distribution Network Based on t-SNE Dimension Reduction and Affinity Propagation Clustering Algorithm[J]. Electric Power, 2023, 56(5): 108-117.

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URL: https://www.electricpower.com.cn/EN/10.11930/j.issn.1004-9649.202211004

https://www.electricpower.com.cn/EN/Y2023/V56/I5/108

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