Abstract
In order to improve the accuracy and adaptability of estimating or predicting the closed-loop current of medium voltage feeders in traditional distribution networks, this paper proposes a method of predicting the closed-loop current of MV feeders in distribution networks based on Convolutional Neural Network (CNN) and Gated Recurrent Unit (GRU). Firstly, the data acquisition and monitoring control system is used to obtain and pre-process the historical load data, grid structure parameters and operation modes. Secondly, the pre-processed massive data are constructed as continuous feature matrices according to the time-sliding window as input. Finally, the CNN-GRU hybrid model is used to establish the mapping relationship between the input features and the loop current to generate the CNN-GRU based medium voltage feeder loop current prediction model and then realize its regression prediction. With the help of DIgSILENT and MATLAB 2020a software, the case study is carried out in a city distribution network in Guizhou Province. The simulation of the envisioned scenario and the results of three sets of six feeder loop closing tests initially show the effectiveness and adaptability of the proposed method, and the related conclusions and discussions are of reference value for the development of intelligent technologies for distribution networks.
Published Version
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