Reactive power voltage optimization technology for large-scale new energy grid connection
In order to improve the power quality and voltage stability of the system,a reactive power and voltage optimization method combining deep neural network and Particle Swarm Optimization algorithm is proposed.This method identifies the most effective control node for reactive power and voltage optimization by feature extraction of power system operation state using graph Convolutional neural network.After compressing the control node,the Particle swarm optimization method is used to solve the reactive power and voltage optimization model,which avoids the problem of traditional algorithms requiring high initial value and long calculation time.The experimental verification was conducted using a certain power grid data as an example.The distribution of reactive power and node voltage obtained by different methods showed that the proposed method has a certain improvement compared to other methods in optimizing system reactive power and improving node voltage.The average load rate of the system line is only 0.67% .
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