Nonlinear model predictive control with immune optimization for voltage security control

A nonlinear model predictive control scheme with immune algorithm is proposed to solve power system voltage security control problems. A nonlinear differential-algebraic-inequality 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; employing pattern recognition techniques to extract gene patterns of better antibodies, and identifying similar antigen patterns via learning and memorizing to create a better initial guess of solutions in order to accelerate the convergence of the optima searching procedure. System performance comparative results based on the emergency voltage control of a six-bus example power system are reported. The results indicate the promising application potential of the method proposed in this paper.