Open Access

ARTICLE

Risk Analysis Using Multi-Source Data for Distribution Networks Facing Extreme Natural Disasters

by Jun Yang^*, Nannan Wang, Jiang Wang, Yashuai Luo

School of Information Engineering, Beijing Institute of Petrochemical Technology, Beijing, 102617, China

* Corresponding Author: Jun Yang. Email:

Energy Engineering 2023, 120(9), 2079-2096. https://doi.org/10.32604/ee.2023.028653

Received 30 December 2022; Accepted 15 March 2023; Issue published 03 August 2023

Abstract

Distribution networks denote important public infrastructure necessary for people’s livelihoods. However, extreme natural disasters, such as earthquakes, typhoons, and mudslides, severely threaten the safe and stable operation of distribution networks and power supplies needed for daily life. Therefore, considering the requirements for distribution network disaster prevention and mitigation, there is an urgent need for in-depth research on risk assessment methods of distribution networks under extreme natural disaster conditions. This paper accesses multi-source data, presents the data quality improvement methods of distribution networks, and conducts data-driven active fault diagnosis and disaster damage analysis and evaluation using data-driven theory. Furthermore, the paper realizes real-time, accurate access to distribution network disaster information. The proposed approach performs an accurate and rapid assessment of cross-sectional risk through case study. The minimal average annual outage time can be reduced to 3 h/a in the ring network through case study. The approach proposed in this paper can provide technical support to the further improvement of the ability of distribution networks to cope with extreme natural disasters.

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Keywords

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