Abstract
With the rapid development of the process control theories in the electrical engineering, new control strategies which lead to better performances are urgently demanded for the excitation control of synchronous generators. For the sake of improving the convergence rate of the terminal voltage and covering the weakness in the adaptability of operational conditions of conventional controls in disturbances, a fuzzy predictive PID excitation control method is proposed in this paper. This control scheme can be divided into three steps in every sample interval: PID parameter adaptation, rolling state prediction and real-time control movement integration. Numerical simulations have been conducted under different operational conditions with the proposed method as well as the conventional ones, respectively. Experimental comparisons indicate the superiority in voltage regulation performance of the proposed method.
Highlights
PID control is still being applied in most excitation system of synchronous generators due to its robustness in control and simplicity in design, its deficiencies including dependence on control parameters, weakness in condition adaptation and hardship of parameter tuning make scholars worldwide find ways to improve its performance or design the new control methods with better performance
The fuzzy predictive PID (FP-PID) control scheme is designed on the basis of discrete-time affine nonlinear systems that can be described in Equation (1)
The fuzzy PID control theory have been utilized in rolling state prediction to replace the conventional rolling optimization process in every sampling instance due to its low calculated amount and control parameter adaptation characteristic
Summary
PID control is still being applied in most excitation system of synchronous generators due to its robustness in control and simplicity in design, its deficiencies including dependence on control parameters, weakness in condition adaptation and hardship of parameter tuning make scholars worldwide find ways to improve its performance or design the new control methods with better performance. For the sake of improving the condition adaptation characteristic of PID, parameter tuning tactics such as the swarm intelligence optimization [1], fuzzy logic [2], artificial neural network [3] and their comprehension [4] have been extensively studied over the decades These efforts do provide us with approaches to make PID parameters more closely match the working conditions in excitation control and to attenuate the deterioration in operating mode transitions. Zhang proposed a predictive control optimization based PID control for industrial surfactant reactors described by linear state-space equations [5] and Sato built a GPC-based PID controller for a weigh feeder [6], etc Most of these predictive integrated PID control methods are either designed for linear systems or possess heavy calculation burden for the inevitably online rolling optimization process [7], the application of these control schemes still have many limitations.
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