ADVANCED FUZZY GAIN-SCHEDULED PID CONTROL FOR DYNAMIC PERFORMANCE ENHANCEMENT IN POWER PLANT SYSTEMS

Abstract

Stable and precise control of thermal power plant variables such as steam temperature, drum level, and turbine speed is critical for safe and efficient operation. Conventional Proportional–Integral–Derivative (PID) controllers are widely deployed in power plants; however, fixed-gain tuning methods often fail to maintain optimal performance under nonlinearities, load disturbances, and parameter uncertainties. This paper proposes an adaptive fuzzy logic–based self-tuning PID controller for power plant control applications. The proposed controller dynamically adjusts PID gains based on real-time error and change in error using a rule-based fuzzy inference mechanism. A nonlinear boiler–turbine model is considered to evaluate performance under load variations and disturbances. Simulation results demonstrate significant improvements in overshoot reduction, settling time, disturbance rejection, and robustness compared to classical PID control. The proposed approach enhances plant stability and operational efficiency, making it suitable for modern intelligent power generation systems.