Adaptive Neural Trajectory Tracking Control for Synchronous Generators in Interconnected Power Systems

The synchronous generator is one of the most important active components in current electric power systems. New control methods should be designed to guarantee an efficient dynamic performance of the synchronous generator in strongly interconnected nonlinear power systems over a wide range of variable operating conditions. In this context, active suppression capability for different uncertainties and external disturbances represents a current trend in the development of new control design methodologies. In this paper, a new adaptive neural control scheme based on differential flatness with a modified structure including B-spline Neural Networks for transient stabilization and tracking of power-angle reference profiles for synchronous generators in interconnected electric power systems is introduced. These features are attained due to the advantages extracted of these two approaches: (a) a control design stage based on a power system model by differential flatness and (b) an adaptive performance using a correct design of B-spline Neural Networks, minimizing parameter dependency. The effectiveness of the proposed algorithm is demonstrated by simulation results in two test systems: single machine infinite bus and an interconnected power system. Transient stability and robust power-angle reference profile tracking are both verified.