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Transferred Bosting Learning for Transient Stability Analysis of Power Systems

Published: 31 July 2024 Publication History

Abstract

The power system holds a crucial role in the energy supply system, being strategically important for national economic development and social activities. Transient stability analysis and preventive control are pivotal in ensuring the secure and stable operation of power systems. In the face of the intricate and ever-changing power system operation modes, the robust adaptive capabilities of online transient stability analysis and preventive control are essential cornerstones for ensuring the system's safety and stability. The rapid advancements in power big data and artificial intelligence technology present new opportunities for online transient stability analysis. This paper focuses on data-driven analysis, particularly in scenarios involving changes in power system topology. We present an online transient stability analysis method using transfer boosting learning, incorporating the concept of data transfer learning into the transient stability analysis modeling process. By effectively leveraging information from existing transient stability analysis data, we facilitate the construction and updating of transient stability analysis models under new fault and topology scenarios. This method achieves the model's easy scalability and updateability. Experimental validation on power data confirms the method's effectiveness.

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  1. Transferred Bosting Learning for Transient Stability Analysis of Power Systems

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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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