Design and Real-Time Implementation of Optimal Power System Wide-Area System-Centric Controller Based on Temporal Difference Learning

Abstract

In this paper, a novel framework for designing and implementing a coordinated wide-area controller architecture for improved power system dynamic stability is presented and tested. The algorithm is an optimal wide-area system-centric controller and observer based on a hybrid reinforcement learning and temporal difference framework. It allows the system to deal with major concerns of wide-area monitoring problem: delays in signal transmission, the uncertainty of the communication network, and data traffic. The main advantage of this design is its ability to learn from the past using eligibility traces and predict the optimal trajectory of cost function through temporal difference method. The control algorithm is evolved from adaptive critic design (ACD) and performed online at a finite horizon through backward and forward view. The ACD controller's training and testing are implemented on the Innovative Integration Picolo card integrated to TMS320C28335 processor. Results on a real experimental test bed using a real power system feeder shows that this architecture provides better stability compared with conventional schemes.