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The significant imbalance between power generation and load caused by severedisturbance may make the power system unable to maintain a steady frequency. Ifthe post-disturbance dynamic frequency features can be predicted and emergencycontrols are appropriately taken, the risk of frequency instability will begreatly reduced. In this paper, a predictive algorithm for post-disturbancedynamic frequency features is proposed based on convolutional neural network(CNN) . The operation data before and immediately after disturbance is used toconstruct the input tensor data of CNN, with the dynamic frequency features ofthe power system after the disturbance as the output. The operation data of thepower system such as generators unbalanced power has spatial distributioncharacteristics. The electrical distance is presented to describe the spatialcorrelation of power system nodes, and the t-SNE dimensionality reductionalgorithm is used to map the high-dimensional distance information of nodes tothe 2-D plane, thereby constructing the CNN input tensor to reflect spatialdistribution of nodes operation data on 2-D plane. The CNN with deep networkstructure and local connectivity characteristics is adopted and the networkparameters are trained by utilizing the backpropagation-gradient descentalgorithm. The case study results on an improved IEEE 39-node system and anactual power grid in USA shows that the proposed method can predict the lowestfrequency of power system after the disturbance accurately and quickly.

Post-Disturbance Dynamic Frequency Features Prediction Based on Convolutional Neural Network