A neural-network-based controller for the cost-effective operation of a hybrid compensator for nonactive power

The need to eliminate distortion from power networks has led to the develop ment of various compensator topologies. The increasing cost of electrical e nergy requires the choice of the most cost-effective compensator operation, An investigation of a neural-network-based controller that chooses the mos t cost-effective compensator mode of operation on the basis of a continuous analysis of load conditions and the operational losses of the elements in the compensator structure are reported. The modeling of operational losses of each subtopology and the required control strategy are discussed. The re sults show that the operational loss savings due to the neural-network-cont rolled hybrid compensator were 30%-70% as compared to the conventionally co ntrolled hybrid compensator, while also conforming to other control strateg y requirements.