Research on inverter power control strategies based on long short-term memory network

The traditional PI control strategy is not only complex and cumbersome in the process of parameter tuning, but also shows poor adaptability in the face of dynamic changes of power grid. Therefore, this study proposes a new control framework, which can adjust the control parameters more accurately and enhance the response ability and stability of the system to the change of power grid state. In this work, an inverter power control strategy based on Long Short-Term Memory Network (LSTM) is proposed. This strategy uses the long-term memory ability of LSTM to model and predict the dynamic relationship between the grid and the inverter. By inputting historical power data, the LSTM model is able to learn and identify patterns and trends in power demand, and then optimize the output response of the inverter to adapt to changes in demand and state of the grid. Experimental analysis shows that the LSTM model significantly improves the adaptability and response speed of the inverter to complex power grid conditions, thereby effectively enhancing the stability and efficiency of the system.