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Research on Power System Evaluation Based on Hybrid Cluster Analysis

Published: 31 July 2024 Publication History

Abstract

In order to quickly and accurately achieve power system loss assessment, it will be an important research point. A novel network loss assessment method based on hybrid clustering analysis is proposed using data mining and typical scenario simulation ideas. This method first determines clustering attributes for network loss assessment; Secondly, based on the numerical types of each clustering attribute, the original clustering problem is decomposed into two sub clustering problems. After fully considering the characteristics of power data, partition clustering algorithm and hierarchical clustering algorithm are selected for clustering analysis, and the clustering results of each sub problem are integrated; Finally, a typical operating mode set of the power grid is generated based on the mixed clustering results for network loss assessment. Taking a provincial power grid as an example to verify the effectiveness of the proposed method in network loss assessment, the results show that the network loss assessment based on the proposed method has high accuracy, good computational efficiency, and strong practicality in engineering practice.

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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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