HYBRID ARTIFICIAL NEURAL NETWORK AND HONEY BEE MATING OPTIMIZATION BASED ON OPTIMAL POWER SYSTEM STABILIZER IN MULTIMACHINE ENVIRONMENT

A Hybrid technique of Artificial Neural Network and Honey Bee Mating Optimization (H-ANN-HBMO) is presented in this paper to damp power system oscillation in multi machine environment. By considering this strategy the weights of ANN is optimized to find the optimum work point of controller. The proposed strategy consists of an ANN controller, which is used as a power system stabilizer in power system to damp the received signal from generator and the HBMO technique for tuning the ANN parameters. The proposed method has the features of a simple structure, adaptive and fast response. In proposed syndicate tuning technique, three performances indicate as ITAE and FD is computed for stability and performance at each of given set of operating conditions of the system. This newly proposed controller is more efficient because it cope with oscillations and different operating points. The effectiveness of proposed controller is tested in two case studies. The first one is single machine infinite bus system and second case study is 10-machine New England power system.