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Research on line loss anomaly identification method of distribution network considering distributed photovoltaic access

Published: 31 July 2024 Publication History

Abstract

For a long time, the identification of 10kV distribution network line loss anomalies has been a very difficult task, with the large number of distributed photovoltaics (DPV) access, the distribution network power flow from one-way flow to two-way flow, which greatly affects the line power flow, voltage and network loss, and increases the difficulty of identifying abnormal distribution network line loss. Therefore, a method for identifying line loss anomalies in distribution network considering distributed photovoltaic access is proposed. Firstly, the characteristic index of distribution network line loss after distributed photovoltaic access is extracted, and the correlation between the line loss index and line loss of the distribution network is calculated by using the gray correlation analysis method, and the correlation degree is ranked. Then, according to the order of gray correlation degree, appropriate indicators are selected for fuzzy C-means clustering, and outliers in each category are detected based on the K-nearest neighbor algorithm to determine whether there is an abnormal line loss in the distribution network. Finally, the verification analysis is carried out through the example, and the results show that the proposed method can effectively identify whether abnormal line loss occurs in the distribution network containing distributed photovoltaics.

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  • (2024)Distribution Network Anomaly Detection Based on Graph Contrastive LearningJournal of Signal Processing Systems10.1007/s11265-024-01940-9Online publication date: 11-Dec-2024

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    PEAI '24: Proceedings of the 2024 International Conference on Power Electronics and Artificial Intelligence
    January 2024
    969 pages
    ISBN:9798400716638
    DOI:10.1145/3674225
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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    Published: 31 July 2024

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    • (2024)Distribution Network Anomaly Detection Based on Graph Contrastive LearningJournal of Signal Processing Systems10.1007/s11265-024-01940-9Online publication date: 11-Dec-2024

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