The Design And Simulation Of An Adaptive Excitation Controller Based On BP Network

This paper gives a thorough research on different excitation control methods such as linear or nonlinear optimal excitation control and direct feedback linear excitation control. A common ground was found: to linearize the matrix of A?B in the state equation in different ways and seek for the optional control laws through linear optimal control theory.This paper designed out a linear adaptive excitation controller. It doesn't linearize the matrix of A and B, but make the nonlinear elements change with the operating condition. As for a given condition, the matrix is constant. Although the control laws can be worked out from linear optimal control theory, it is difficult in the real time. This paper combines ANN with linear optimal control theory to solve this problem. Active power Pe, reactive power Qe and the end voltage of the generatorU are used as the inputs of ANN; the optimal feedback gains from the Riccati equation are used as teachers. After trained offline, the ANN can output the optimal gains according to the operating condition, the simulation result shows, this controller has better voltage regulation precision and dynamic characteristic than LOEC.Besides, this paper makes some researches on the structure? training algorithm and convergence of BP neural network and also gives a embedded discuss about how to simulate the power system with the software of MATLAB/SimPowerSystem.