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ew 23(1):

Research Article

Technology for Power Outage Research and Judgment-dependent Data Feature Noise Analysis

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  • @ARTICLE{10.4108/ew.3949,
        author={Xiang Li},
        title={Technology for Power Outage Research and Judgment-dependent Data Feature Noise Analysis},
        journal={EAI Endorsed Transactions on Energy Web},
        volume={10},
        number={1},
        publisher={EAI},
        journal_a={EW},
        year={2023},
        month={9},
        keywords={power outage research and judgment, data characteristics, noise analysis},
        doi={10.4108/ew.3949}
    }
    
  • Xiang Li
    Year: 2023
    Technology for Power Outage Research and Judgment-dependent Data Feature Noise Analysis
    EW
    EAI
    DOI: 10.4108/ew.3949
Xiang Li1,*
  • 1: Yunnan Power Grid Co.,Ltd
*Contact email: 18208891009@163.com

Abstract

INTRODUCTION: Power grid blackouts occur frequently, which significantly impacts social impact. Because these accidents are dynamic and random, predicting and evaluating them is challenging. OBJECTIVES: To explore the complexity of the power grid itself, analyzes the critical changes of the self-organizing model during power grid fault, extracts the data characteristics related to the steady-state maintenance of abnormal systems, and puts forward an effective outage prediction model. METHODS: Starting with cluster analysis, The authors can reduce data fluctuation and eliminate noise interference to optimize data. The evaluation indexes of initial fault occurrence possibility and fault propagation speed in the power grid are constructed. RESULTS: The validation of the outage forecasting model has produced promising results, achieving 96.4% forecasting accuracy and a meager error rate. In addition, the evaluation index developed in this study accurately reflects the possibility and spread speed of power outage accidents. CONCLUSION: The research proves the feasibility of establishing an outage prediction model based on the power grid system data characteristics. The model has high accuracy and reliability and is a valuable tool for power outage research and judgment.

Keywords
power outage research and judgment, data characteristics, noise analysis
Received
2023-01-15
Accepted
2023-09-16
Published
2023-09-22
Publisher
EAI
http://dx.doi.org/10.4108/ew.3949

Copyright © 2023 Li, licensed to EAI. This open-access article is distributed under the terms of the CC BY-NC-SA 4.0, which permits copying, redistributing, remixing, transforming, and building upon the material in any medium so long as the original work is properly cited.

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