Optimization Setting of Generator Excitation Adjustment Coefficient Based on power grid Operation Data Set

Abstract

This paper proposes an optimal setting method of GESCC (generator excitation system correlation coefficient) based on power grid operation data set. By means of self-organizing feature mapping neural network, the typical operation modes and the occurrence probability of the grid are obtained based on power grid operation data set. On this basis, the influence of different values of GESCC on the power grid is analyzed, and the GESCC of the generator can be optimized. Simulation results of Jilin regional power grid show that using the proposed method the network loss is reduced, the power grid voltage level is improved, and the voltage fluctuation decreases at the same time, while the operating characteristics of both the load and the renewable energy power generation are fully considered.