Deep Learning-Driven Digital Twin for Remote Control of Power Systems

At present, digital twin technology has been developed in many ields and plays a very important role.In this study, digital twin technology is applied to remote control of power system to build a set of remote control system of power system, which contains perception layer, data layer, operation layer, function layer and application layer.In order to make the power system remote control system more reliable and effective, a power system fault diagnosis method based on MRPSODE-ELM is proposed using deep learning technology.The method combines PSO algorithm and DE algorithm to construct a multiple stochastic variation particle swarm differential evolution algorithm, and it is used for the optimization seeking ability of the number of neurons in the hidden layer of the limit learning machine.The experimental results show that the MRPSODE-ELM model performs superiorly in detecting different fault types in terms of accuracy, recall and F1 score, with the results of each index above 95%, and the fault diagnosis accuracy is improved by 4.77% and 3.36% over SVM algorithm and DNN algorithm, respectively, and possesses a smaller training time consumption.The fault detection method proposed in the study can be applied to the remote control of power systems based on digital twins.