基于 CNN-LSTM 网络的电网电压稳定紧急控制策略

The complexity and variability of operation mode, uncertainty of disturbance fault and weak immunity of power electronic equipment bring great challenges to the safe and stable operation of hybrid AC/DC power grids. To ensure the power grid voltage stability after the large-disturbance fault, a response-driven emergency control strategy is proposed based on convolutional neural network (CNN) and long short-term memory (LSTM) network. First, the mapping relationship between voltage time sequence value of key bus nodes and the voltage stability level is analyzed, and the large-disturbance voltage stability evaluation model based on CNN-LSTM network is established offline. Secondly, the evaluation model is used to predict the enhancement of power grid voltage stability level after the operation of alternative generator tripping and load shedding spot control measures and to determine the sensitivity of response-driven emergency control measures. Finally, considering the sensitivity of emergency control measures, an emergency control optimization problem is established with the actual operation constraints of the power grid, and the optimal emergency control strategy is obtained. For the actual scenario of hybrid AC/DC power grid with the voltage instability problem, the simulation results verify the accuracy of the proposed response-driven control sensitivity prediction method. The optimal coordination strategy of emergency control measures for voltage stability can ensure the safe and stable operation of power grid after large-disturbance faults.