Fuzzy model predictive control for alternating current excitation generators

Alternating current excitation generators (ACEG) can adjust the active power and inactive power flexibly and improve the stability of power system. The key to enhance the power system´s stability is to choose appropriate ACEG´s excitation control method. Conventional excitation controllers are unable to perform optimally over the full range of operation conditions and disturbances, due to the highly complex, non-linear nature of power systems. In this paper, fuzzy model predictive control is proposed to cope with the problem. T-S fuzzy model is employed to appropriate the nonlinear object. The fuzzy model is derived from input-output data by means of product-space fuzzy clustering, similarity driven rule base simplification is applied to detect and merges compatible fuzzy sets in the model and a new validity measure is adopted to determine the number of clusters. All these techniques make the fuzzy model transparent and accurate. The critical element in fuzzy model predictive control is the nonconvex optimization problem, iterative optimization techniques are mostly slow due to computational complexity, this hamper its application to fast system. In order to solve the problem, branch-and-bound optimization method is adopted. The fuzzy model predictive algorithm is used in internal model control scheme to compensate for process disturbances, measurement noise and modeling errors. Simulation test under large disturbance at various operating points is made. The results show the fuzzy model based predictive controller is effective and feasible. It performs well over a wide range of system disturbance and improves dynamic characteristic of ACEG system.