Power system frequency prediction after disturbance based on deep learning

In order to ensure the safe and stable operation of power system, enrich the means of power grid analysis and control and expand the application of deep intelligent learning methods in power grid systems, the application of deep learning intelligent machine learning method in frequency prediction of large power grid is explored. Fini, on the basis of deep learning, the frequency response mode of large power grid is analyzed and the key characteristic quantities tbat affect the frequency response mode are extracted. Second, the deep belief neural network (DBN.DNN) frequency prediction model is constructed. Abo, ihe training and testing of the model are introduced. Finally, the input and output based on the DBN.CNN prediction model and the network structure design of the model are analyzed. The prediction performance of the model is evaluated. The results show tbat when the number of neurons in tbe hidden layer is 50. the model achieves the optimal prediction effect, increasing the number of training Samples helps to improve the modeling ability and prediction accuracy of the model. For frequency prediction problems, the number of training samples should be set to ≥400, and the number of hidden layers corresponding to the model should be 5. When tbe number of hidden layer neurons is 10, the prediction accuracy of the DBN/DNN network is poor. When the number of hidden layer neurons is 10, the model can achieve the best prediction effect. Overall, tbe DBN.DNN prediction model bas good prediction performance. The RAISE of the forecast data is 0073 Hz can basically meet the actual application requirements. Therefore, the frequency prediction method based on deep belief neural network bas certain advantages in accuracy and efficiency. © 2020, North Atlantic University Union NAUN. All rights reserved.