• CSCD核心库收录期刊
  • 中文核心期刊
  • 中国科技核心期刊

ELECTRIC POWER CONSTRUCTION ›› 2023, Vol. 44 ›› Issue (3): 56-65.doi: 10.12204/j.issn.1000-7229.2023.03.006

• Research and Application of Key Technologies for Distribution Network Planning and Operation Optimization under New Energy Power Systems?Hosted by Professor WANG Shouxiang and Dr. ZHAO Qianyu? • Previous Articles     Next Articles

Transformer-Based Evaluation Method of Power Outage in Active Distribution Networks with Multiple Sources and Loads

JI Pengzhi1, LI Guangxiao1, WANG Lin1, LIU Sixian1, LIU Zongjie1, MA Ziyao2, WANG Zhaoqi2(), TANG Wei2()   

  1. 1. State Grid Jining Electric Power Supply Company, Jining 272100, Shandong Province, China
    2. College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China
  • Received:2022-03-10 Online:2023-03-01 Published:2023-03-02
  • Supported by:
    State Grid Shandong Electric Power Company Research Program(5206002000R2)


In recent years, the increasing strong typhoon weather has brought more and more serious losses to the distribution network in coastal and some inland areas, resulting in large-scale power loss of important load for a long time. Improving the recovery capacity of active distribution networks containing multiple sources and loads has become an urgent problem to be solved. To deal with the problem that the existing power outage evaluation methods of distribution networks failed to obtain the information of the nodes, this paper proposes a novel evaluation method of power outage in active distribution networks containing multiple sources and loads on the basis of deep learning method, such as the Transformer model. Considering the ground roughness and height, meteorological information correction model of the geographical environment of the active distribution network is constructed combined with the typhoon disaster meteorological data under the condition of weak communication. On this basis, considering the disaster mechanism of strong typhoons and the topology of active distribution networks, the Transformer model is used to construct the power outage model of active distribution networks to improve the accuracy of power outage evaluation in active distribution networks containing multiple sources and loads. Through the simulation tests of the improved IEEE 33-node active distribution network, it is verified that the proposed power outage evaluation method for active distribution networks can meet the accuracy requirement of power outage evaluation in typhoon-prone distribution networks.

Key words: active distribution network, multiple sources and loads, typhoon, power outage evaluation, deep learning

CLC Number: