An integrated lookahead control-based adaptive supervisory framework for autonomic power system applications

Management of the power system infrastructure is a challenge due to its complex dynamics, size, deregulated operation, quality of service demands, and stability concerns. Autonomic computing has recently gained interest on power systems arena for self-healing, self-configuration, self-optimization, and self-protection schemes. Robustness and reliability of the power system can be enhanced with appropriate autonomic control action following the disturbances. Predictive optimal tuning of real time control parameters from the finite set of control actions is considered in this paper to prevent from impending system deterioration. We present the formulation of a generic, higher layer model-based Limited Lookahead Control (LLC) approach that can be applied to a variety of power system applications. The system model, including the lower level controller, load dynamics, and a network assists the controller action. Discrete time control decisions are made based on the optimization of the predicted response to a limited horizon from the developed model. Heuristics based A* algorithm is integrated into the framework to reduce the control overhead for real-time operation. Finally, we present a case studies on Matlab, and RTDS® to demonstrate the applicability of the proposed framework. A nine bus multi-machine power system benchmark is considered for voltage control application with the finite set of capacitor tuning.