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Transformer-Based Evaluation Method of Power Outage in Active Distribution Networks with Multiple Sources and Loads
JI Pengzhi, LI Guangxiao, WANG Lin, LIU Sixian, LIU Zongjie, MA Ziyao, WANG Zhaoqi, TANG Wei
Electric Power Construction ›› 2023, Vol. 44 ›› Issue (3) : 56-65.
PDF(3630 KB)
PDF(3630 KB)
Transformer-Based Evaluation Method of Power Outage in Active Distribution Networks with Multiple Sources and Loads
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.
active distribution network / multiple sources and loads / typhoon / power outage evaluation / deep learning
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