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

doi:10.12204/j.issn.1000-7229.2023.03.006

Abstract

Abstract:

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:

TM73

JI Pengzhi, LI Guangxiao, WANG Lin, LIU Sixian, LIU Zongjie, MA Ziyao, WANG Zhaoqi, TANG Wei. Transformer-Based Evaluation Method of Power Outage in Active Distribution Networks with Multiple Sources and Loads[J]. ELECTRIC POWER CONSTRUCTION, 2023, 44(3): 56-65.

Figures/Tables 11

Fig.1 Transformer-based prediction method of node power loss

Table 1 Reference values of terrain features

Table 2 Corresponding table of different terrain roughness

Fig.2 Structure of Transformer-based prediction model

Fig.3 Improved IEEE 33-node distribution network topology

Fig.4 Node prediction error considering different meteorological factors

Fig.5 Topological fault identification result before mechanism correction

Fig.6 Topological fault identification result after mechanism modification

Table 3 Comparison of adaptive example results

Fig.7 Comparison of prediction errors between Transformer-based and LSTM methods

Fig.8 Fault diagram of Jining 10 kV Jiayin line simulation example

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