Dynamic Load Reactive Power Optimization Based on Convolutional Neural Network VGG16 Model

Abstract

<p>In reactive power optimization of distribution network, in most cases, the processing of load data is often only for the specific data under a certain operation condition, so it is difficult to classify and process the data of multiple working conditions in a certain operation stage. To solve the above problems, an optimization method basedon convolutional neural network VGG16 (visual geometry group network) is proposed. Firstly, it is classified according to the capacity proportion of transformer in the station area, and three kinds of scenes of light, medium and high are obtained. Then, particle swarm optimization algorithm is used to optimize each kind of scene, and the corresponding optimization strategy is obtained. The effectiveness of this method to solve the problem of dynamic reactive power and voltage in the substation area is verified by simulation.</p>