Minimum Frequency Prediction Model of Power Systems Based on Physical-data Driven

The physical equation is less integrated into the data-driven model in existing frequency prediction methods. A minimum frequency prediction model based on physical-data driven is proposed in this paper. Firstly, the physical information embedded layer is constructed. The minimum frequency expression of the system frequency response (SFR) model is transformed into the input layer of the long short-term memory (LSTM) network. The physical knowledge in the SFR model can be explored through neural network training. And the physical knowledge is used to improve the prediction accuracy of the LSTM network. Then, the steady-state frequency constraint is added to the loss function. The search space of the embedded layer parameters is directly constrained. With the constrain, the parameter drift problem can be effectively addressed. Finally, the simulation verification is performed in the IEEE 39-node system. The results show that the proposed model has higher prediction accuracy than common fusion models. When the data contains noise, the proposed model shows good noise immunity.