Nonlinear predictive control scheme with immune optimization for voltage security control of power system

Abstract

An immune algorithm based nonlinear predictive control scheme is proposed to solve power system voltage security control problems. A nonlinear differential-algebraic model is used to predict system behavior. A gradational targeting method is developed to decompose global horizon control targets into sub-objectives in receding prediction intervals via Pareto-type weighting functions. A novel immune algorithm is presented, using a multiple gene chain structure of antibodies to represent the solution candidates of the complicated optimization problem. A pattern recognition technique is employed to extract gene patterns of better antibodies. Similar antigen patterns are identified via learning, and memorized to create a better initial guess of solutions in order to accelerate the convergence of the optimal searching procedure. System performance comparative results are reported based on the emergency voltage control of a six-bus example power system. The results indicate the promising application potential of the method.