Research on real time online automatic early warning method of power failure in distribution network

Zhe Chen

doi:10.1117/12.2620660

29 November 2021 Research on real time online automatic early warning method of power failure in distribution network

Zhe Chen

Proceedings Volume 12080, 4th International Symposium on Power Electronics and Control Engineering (ISPECE 2021); 120800X (2021) https://doi.org/10.1117/12.2620660
Event: 4th International Symposium on Power Electronics and Control Engineering (ISPECE 2021), 2021, Nanchang, China

Abstract

To solve the problem of high alarm error rate caused by unbalanced load distribution in power failure state of distribution network, a real-time online automatic warning method for power failure status of distribution network is proposed. The topology structure of distribution network is established, and the connection relationship of power equipment is represented by binary form, the shortest distance of nodes is calculated and the power supply path is searched in real time. By determining the objective function and constraint conditions, the power flow calculation model of distribution network is constructed to ensure the economic performance of power supply path. The category of feature vector of fault factors is determined, the outage time length trained according to decision rules is obtained, and the power failure state warning model is established according to the correlation matrix. The experimental results show that the false alarm rate of this method is lower than that based on LightGBM model and Gray Markov model in the outage failure of weather, operation and equipment, which can improve the level of automatic operation and maintenance of distribution network.

Citation Download Citation

Zhe Chen "Research on real time online automatic early warning method of power failure in distribution network", Proc. SPIE 12080, 4th International Symposium on Power Electronics and Control Engineering (ISPECE 2021), 120800X (29 November 2021); https://doi.org/10.1117/12.2620660

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