Optimal load frequency control of series-compensated power system

This study proposes a nonlinear predictive optimal control framework, tracking model predictive static programming, for optimal load frequency control. It updates control actions by solving a low-dimensional static programming problem while satisfying state and input constraints. With recursive computation of “sensitivity matrices,” the algorithm is efficient. Tracking model predictive static programming was validated on a two-area power system with a thyristor-controlled phase shifter to damp tie-line power oscillations. Governor dead-band, generation rate, and thyristor-controlled-phase-shifter angular deviation constraints were considered. Tracking model predictive static programming demonstrated improved performance over integral and Model Predictive Controllers.