Power system stabilizer based on fuzzy neural network with improved learning algorithm

The power system stabilizer based on FNN(Fuzzy Neural Network) adapts well to the nonlinearity of power system and does not rely on the precise mathematical model. A FNN design method based on maximum entropy principle is proposed to optimize the center parameters of its membership functions,which uses an optimized objective function to deduce the learning algorithms of center vector and width,improving its regression and generalization ability. For the lower - frequency oscillation of power system,a power system stabilizer design based on entropy optimization FNN is proposed, which, independent of the precise system model,fully uses the learning ability of neural network to generate on - line training samples and implement the real-time control of power system. Simulation results show that the designed power system stabilizer enhances significantly the stability of controlled generator and the dynamic performance of power system.