Enhancing Hydro Turbine Frequency Regulation with Neural Network-Aided PID Governor Design

Abstract

This paper presents a novel approach to hydro turbine speed control by integrating neural networks into the tuning process of digital governors, with the objective of meeting the stringent performance requirements set by the National Power System Control Center of Vietnam Electriciy (EVN). The proposed control strategy employs a closed-loop configuration using turbine rotational speed as feedback to regulate water flow and maintain power balance under varying load conditions. A key innovation of the study is the use of an adaptive neural network - propotional intergral derivative (NN-PID) Controller, which continuously updates control parameters in real-time through the Brandt-Lin learning algorithm. This allows the controller to respond effectively to nonlinearities and disturbances in the system. Simulation and hardware-in-the-loop experiments validate the effectiveness of the proposed method, demonstrating enhanced performance compared to traditional propotional intergral derivative (PID) controllers including faster settling times, zero steady-state error, and suppression of oscillations during sudden load changes. The results suggest that the NN-PID controller offers a promising alternative for next-generation digital governor design in large hydropower plants.