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A transmission line fault identification method based on long short-term memory network and random matrix principle

Published: 14 March 2023 Publication History

Abstract

In the past decade, driven by the policy of maximizing the consumption of renewable energy, renewable energy is being integrated into the power grid in the form of centralized power generation or decentralized power generation. The volatility and randomness of renewable energy generation lead to great uncertainty in the power flow of transmission lines, which leads to the increasing diversity of the types and characteristics of transmission line faults. This paper presents an intelligent fault identification method for transmission lines based on long short-term memory network and stochastic matrix principle. Firstly, a method to determine the fault time of transmission lines in stochastic matrix theory is proposed. Secondly, on this basis, a learning and training method of large sample fault random matrix is given. Furthermore, the fault types of transmission lines are further identified based on long short-term memory network. Finally, an actual transmission line is taken as an example to demonstrate the effectiveness of the proposed method.

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        cover image ACM Other conferences
        ACAI '22: Proceedings of the 2022 5th International Conference on Algorithms, Computing and Artificial Intelligence
        December 2022
        770 pages
        ISBN:9781450398336
        DOI:10.1145/3579654
        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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        New York, NY, United States

        Publication History

        Published: 14 March 2023

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        Author Tags

        1. Fault
        2. Random matrix
        3. Transmission line
        4. long short-term memory network

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