AI-based Load Frequency Control with ANN and PID

This project explores the use of artificial intelligence for load frequency control (LFC) in a single-area power system. Keeping system frequency stable is vital for reliable electricity supply, as sudden load changes can cause deviations that threaten grid stability. Traditional controllers like PID (Proportional-Integral-Derivative) can correct frequency deviations, but they often struggle with nonlinearities and complex system dynamics. Here, a feedforward Artificial Neural Network (ANN) is trained to predict the next frequency deviation using recent historical data of frequency and load. The ANN controller adjusts the mechanical power input in real time to minimize these deviations. Training data is generated from numerous random load profiles to simulate realistic disturbances. The performance of the ANN controller is compared with both open-loop (no control) and classical PID control. Results show that the ANN achieves better frequency regulation, with lower root mean square (RMS) deviations and smoother responses. Graphs of frequency, mechanical power, and load clearly demonstrate the ANN’s ability to handle disturbances more effectively than PID. This work highlights the potential of machine learning in modern power system control, offering an adaptive and robust approach to frequency regulation. Future work could extend this to multi-area systems, use recurrent neural networks for better temporal modeling, and explore reinforcement learning for optimal control strategies.