AI-Based Solution For Efficient Disturbance Rejection in Mini Hydropower Plant

This paper presents a systematic comparison of four advanced control strategies for disturbance rejection in a mini hydropower plant. The control strategies that are proposed include a conventional PID controller, a FOPID controller, an Internal Model Control with PD controller, and an adaptive PID controller implemented using discretization and artificial intelligence techniques. The mini hydropower plant is modelled using a fractional differentiation order process, capturing the complex dynamics and nonlinearities inherent in such systems. The performance of each controller is evaluated in terms of disturbance rejection, set-point tracking, and robustness to parameter variations. Simulation results demonstrate the strengths and limitations of each control strategy, providing valuable insights for the selection and design of controllers in mini hydropower applications. These findings highlight the effectiveness of the IMC-PD approach in handling disturbances and maintaining system stability, while the adaptive PID controller shows promise for applications requiring adaptability under varying conditions. The study provides valuable insights for the selection and design of controllers in mini hydropower systems, contributing to the advancement of efficient and reliable control strategies for renewable energy applications.