Design and real-time implementation of optimal power system wide area system-centric controller based on temporal difference learning

In this paper a new method for designing and implementing coordinated wide area controller architecture is presented and tested using real-time digital simulation on a benchmark two area power system model for improved power system dynamic stability. The algorithm is an optimal Wide Area System-Centric Controller and Observer (WASCCO) based on reinforcement and temporal difference learning which allows the system to learn from interaction and predict future states. The controller design uses a powerful technique of the adaptive critic design (ACD) family called dual heuristic programming (DHP). The DHP controllers training and testing are implemented on the Innovative Integration Picolo card consisting of the TMS320C28335 processor. The main advantage of this design is its ability to learn from the past using eligibility traces and predict the optimal trajectory through temporal difference learning in the format of Receding Horizon Control(RHC). Results on a two area system provides better response compared to conventional schemes.