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.