Genetic Algorithms in Power System Small SignalStability

Genetic Algorithms in Power System Small Signal Stability Analysis Zhao Yang Dong Yuri V. Makarov David J. Hill Department of Electrical Engineering The University of Sydney NSW 2006, Australia Summary Power system small signal stability analysis aims to explore di erent small signal stability conditions and controls, namely, 1) exploring the power system security domains and boundaries in the space of power system parameters of interest, including load ow feasibility, saddle node and Hopf bifurcation ones, 2) nding the maximum and minimum damping conditions, and 3) determining control actions to provide and increase small signal stability. These problems are presented in the paper as di erent modi cations of a general optimization problem, and each of them has multiple minima and maxima. The usual optimization procedures converge to a minimum/maximum depending on the initial guesses of variables and numerical methods used. In the considered problems, all the extreme points are of interest. Additionally, there are di culties with nding the derivatives of the objective functions with respect to parameters. Numerical computations of derivatives in traditional optimization procedures are time consuming. In the paper, we propose a new black box genetic technique for comprehensive small signal stability analysis, which can e ectively cope with highly nonlinear objective functions with multiple minima and maxima and derivatives which can not be expressed analytically. Genetic Algorithms(GAs) [2] are capable of locating the global optimum of a tness function in a bounded search domain, provided a su cient population size is given. The GA sharing function method is able to locate the multiple local maxima as well. In the paper, a black box system model is used, Fig. 1. The black box has control parameters as