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Research on data-driven assessment of power outage losses for power users

Published: 31 July 2024 Publication History

Abstract

This paper starts by systematically analyzing the factors that affect user power outage losses based on the electricity consumption characteristics, outage characteristics, and distribution network operation characteristics. It constructs a factor system for the influence of user power outage losses based on eight indicators from both the distribution network side and the user side. Additionally, it delves into the composition components of power outage losses for different types of users and explains the source and processing of assessment data.The paper proposes a power outage loss assessment method based on the Extreme Learning Machine (ELM) algorithm and validates this method using comprehensive data from user samples for the years 2020-2023. The results show that the proposed method in this paper exhibits significant effectiveness compared to existing assessment methods in the literature, with a relatively small deviation from actual loss data.

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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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    Association for Computing Machinery

    New York, NY, United States

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    Published: 31 July 2024

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