Investigation of Jaya Optimized Artificial Neural Network Based PID for Single Area Load Frequency Control

This study investigates the application of Artificial Neural Network (ANN)-based controllers for single area Load Frequency Control (LFC) in power systems. Proportional-Integral-Derivative (PID) controllers are widely used but encounter limitations when dealing with dynamic and non-linear conditions, highlighting the need for adaptive control strategies. The research investigates the performance of conventional PID controllers with ANN-PID, Jaya Algorithm-optimized PID and ANN-JAYA-PID controllers. The different control strategies are implemented using MAT LAB Simulink. The ANN model is trained with data obtained from system simulations, ensuring that it accurately predicts controller behavior under varying load conditions. The Jaya Algorithm is employed to optimize the PID parameters, enhancing the controller's ability to maintain frequency stability. Results demonstrate that the ANN-Jaya-PID controller outperforms other configurations, achieving the fastest settling time and minimizing overshoot and undershoot during load disturbances. Additionally, the J aya optimization significantly improves the robustness of the LFC system, particularly in handling the non-linearities of modern power grids.