Noise-induced chaos in single-machine infinite-bus power systems

Abstract

We numerically investigate the influences of Gaussian white noise on the dynamical behaviors of a power system. The studied model is described by the classical single-machine infinite-bus (SMIB) system with the values of parameters at which the system is stable. It is found that with increasing noise intensity σ, the power system becomes unstable and falls into chaos as σ is further increased. These phenomena imply that random noise can induce and enhance chaos in the power system. Furthermore, the possible mechanism behind the action of noise is addressed basing on the stochastic Melnikov method. Our results may provide a useful tip for understanding power system's security operations.

Understanding Early Indicators of Critical Transitions in Power Systems From Autocorrelation Functions

“…This causes the linearized state matrix to be independent of the bifurcation parameter. From (38), one can show that the normalized autocorrelation of ∆δ depends only on A and the time lag. Since A is constant in this system, the autocorrelation of ∆δ will be constant for a specific ∆t.…”

“…A few of these papers use stochastic SMIB models. In [38], it is suggested that increasing noise in the stochastic SMIB system can make the system unstable and induce chaotic behavior. Reference [26] (mentioned in Sec.…”

Control of Chaos in a Single Machine Infinite Bus Power System Using the Discrete Sliding Mode Control Technique

“…The increasing demand for electric power forces the power system to operate nearly close to its stability boundary. In this operating environment, a sudden disturbance can lead to a chaotic behavior [3][4][5][6]. Chaos is related to many power system instability phenomena such as voltage collapse which occurs when the power system is heavily loaded.…”

Stability region and radius in electric power systems under sustained random perturbations

“…However, no numerical methods are presented for implementation in real systems. In [14] numerical methods are reported to evaluate stability in mechanical systems by means of Lyapunov exponents, but the results shown cannot be extrapolated to large systems such as electric power systems.…”

“…However, no numerical methods are presented for implementation in real systems. In [14] numerical methods are reported to evaluate stability in mechanical systems by means of Lyapunov exponents, but the results shown cannot be extrapolated to large systems such as electric power systems.In the context of the model of random variations sustained in time, white noise or Brownian motion, see [15], has been used to represent the stochastic dynamics of electric systems. However, this process is adequate for applications at the microscopic level, and it is not a correct approximation to represent the macroscopic phenomena existing in electric networks.…”