An approach for load frequency control enhancement in two-area hydro-wind power systems using LSTM + GA-PID controller with augmented lagrangian methods.

This paper proposes an advanced Load Frequency Control (LFC) strategy for two-area hydro-wind power systems, using a hybrid Long Short-Term Memory (LSTM) neural network combined with a Genetic Algorithm-optimized PID (GA-PID) controller. Traditional PID controllers, while extensively used, often face limitations in handling the nonlinearities and uncertainties inherent in interconnected power systems, leading to slower settling time and higher overshoot during load disturbances. The LSTM + GA-PID controller mitigates these issues by utilizing LSTM's capacity to learn from historical data by using gradient descent to forecast the future disturbances, while the GA optimizes the PID parameters in real time, ensuring dynamic adaptability and improved control precision. The proposed controller's performance is rigorously tested against both classical PID and GA-PID controllers through simulations conducted in MATLAB/Simulink. The results reveal that the LSTM + GA-PID controller achieves a 2.33-fold reduction in settling time compared to the GA-PID controller and a 4.07-fold reduction compared to the classical PID controller. Additionally, the controller exhibits a 3.27% reduction in overshoot and mitigates mechanical power output perturbations by 3.43% during transient load changes. Hardware validation has been carried out to show the robustness of the model.