Load-Frequency Control: a GA based Bayesian Networks Multi-agent System

Bayesian Networks (BN) provides a robust probabilistic method of reasoning under uncertainty. They have been successfully applied in a variety of real-world tasks but they have received little attention in the area of load-frequency control (LFC). In practice, LFC systems use proportional-integral controllers. However since these controllers are designed using a linear model, the nonlinearities of the system are not accounted for and they are incapable to gain good dynamical performance for a wide range of operating conditions in a multi-area power system. A strategy for solving this problem due to the distributed nature of a multi-area power system, is presented by using a BN multi-agent system. This method admits considerable flexibility in defining the control objective. Also BN provides a flexible means of representing and reasoning with probabilistic information. Efficient probabilistic inference algorithms in BN permit answering various probabilistic queries about the system. Moreover using multi-agent structure in the proposed model, realized parallel computation and leading to a high degree of scalability. To demonstrate the capability of the proposed control structure, we construct a BN on the basis of optimized data using genetic algorithm (GA) for LFC of a three-area power system with two scenarios.