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.