Abstract
INTRODUCTION: With the deepening of the application of big data technology, the power sector attaches great importance to power outage judgment. However, many factors affect the judgment result of power outage, and the analysis process is very complicated, which can not achieve the corresponding accuracy.
OBJECTIVES: Aiming at the problem that it is impossible to accurately judge the result in judging power failure, a deep mining model of big data is proposed.
METHODS: Firstly, the research data set is established using power outage big data technology to ensure the results meet the requirements. Then, the power failure judgment data are classified using big data theory, and different judgment methods are selected. Using big data theory, the accuracy of power failure judgment is verified.
RESULTS: The deep mining model of big data can improve the accuracy of power failure judgment and shorten the judgment time of power failure under big data, and the overall result is better than the statistical method of power failure.
CONCLUSION: The deep mining model based on power outage big data proposed can accurately judge the power outage fault and shorten the analysis time.
Publisher
European Alliance for Innovation n.o.
Subject
Marketing,Energy Engineering and Power Technology,Renewable Energy, Sustainability and the Environment
Reference21 articles.
1. R. Atat, M. Ismail, and E. Serpedin, "Limiting the Failure Impact of Interdependent Power-Communication Networks via Optimal Partitioning," Ieee Transactions on Smart Grid, vol. 14, no. 1, pp. 732-745, Jan 2023.
2. M. Atrigna et al., "A Machine Learning Approach to Fault Prediction of Power Distribution Grids Under Heatwaves," Ieee Transactions on Industry Applications, vol. 59, no. 4, pp. 4835-4845, Jul-Aug 2023.
3. S. Azizi, M. R. Jegarluei, J. S. Cortes, and V. Terzija, "State of the art, challenges and prospects of wide-area event identification on transmission systems," International Journal of Electrical Power & Energy Systems, vol. 148, Jun 2023, Art. no. 108937.
4. T. G. Bolandi, V. Talavat, and J. Morsali, "Online vulnerability assessment of Zone-3 distance relays against static load Encroachment: A novel approach based on fault chain theory," International Journal of Electrical Power & Energy Systems, vol. 151, Sep 2023, Art. no. 109183.
5. X. Y. Cao, Z. J. Wu, X. F. Xie, X. J. Quan, Q. R. Hu, and M. F. Li, "Maloperation prevention for overcurrent protection in photovoltaic integration system under weather intermittency," Electric Power Systems Research, vol. 223, Oct 2023, Art. no. 109566.
Cited by
1 articles.
订阅此论文施引文献
订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献