Modeling and simulation of stochastic transients in power systems based on frequency shifting theory

Abstract

The increasing uncertainties in power systems have brought non-negligible influences on the dynamic behaviors. An accurate and efficient simulation method considering the effects of stochastic disturbances is of critical importance for the analysis of the dynamic performance of power systems. In this paper, a methodology for the simulation of stochastic transients in power systems is developed based on frequency shifting theory. The stochastic differential equations describing the stochastic process of parameter migration are represented through analytic signals. The frequency shifting operation is introduced. The Fourier spectra of analytic signals are shifted to reduce their maximum frequency contents, thereby permitting a larger time-step in time-domain simulation in accordance with Shannon’s sampling theory. Through the trapezoidal Milstein scheme, the stochastic differential equations with shifted analytic signals are discretized. Branch companion models that process analytic signals for the simulation of stochastic transients are formulated. The numerical properties of branch models are further examined. The analysis of test cases demonstrates that the proposed method can be used to represent stochastic transients caused by parameter migrations accurately and efficiently.