Frequency Response Mode Prediction of Power System After Large Disturbances Based on Deep Belief Neural Network

The power system frequency is an important indicator that reflects the power system’s operating status. Through real-time detection or prediction, it can effectively ensure stable power system operation. To provide a theoretical basis for the steady-state recovery of thepower system after major disturbances, a deep belief neural network model for predicting the power system’s frequency response mode after major disturbances has been proposed. This model combines deep belief networks with deep neural networks. The feature extraction and learning abilities of deep belief networks were utilized to complete model training and learning. Deep neural networks were utilized to complete data classification and prediction. The load sudden variable is larger, the system frequency fluctuation is greater, the inertia time constant increases less, and the system frequency fluctuation is greater. In simulation testing with a training sample size of 2100 and a testing sample size of 900, the deep belief neural network took 3320 seconds, while the deep neural network took 11523 seconds. The prediction results’ absolute error amplitude of this deep belief neural network is 0.025Hz. It meets the practical needs of frequency response mode prediction in power systems after major disturbances. The study analyzed the impact of load sudden changes and inertia time constants on system frequency, and successfully designed a frequency response mode prediction model for power systems after major disturbances.