Abstract
The ever-increasing penetration of wind power generation and plug-in electric vehicles introduces stochastic continuous disturbances to the power system. This paper proposes an analytical approach to analyze the influence of stochastic continuous disturbances on power system small signal stability. The noise-to-state stability (NSS) and NSS Lyapunov function (NSS-LF) are adopted for stability analysis with respect to the magnitude of uncertainties in a power system. The power system is modeled as a set of stochastic differential equations (SDEs). The supremum of the norm of the covariance is employed to characterize the influence of magnitudes of uncertainties on the power system. Then the relationship between the magnitudes of stochastic variations and probabilistic stability is explicitly identified by NSS. The proposed method can assess the stochastic stability of the power system by checking some algebraic expressions. Hence, it has high computation efficiency compared with the well-established Monte Carlo based method. Besides, since the magnitudes of the stochastic variations are integrated into the definition of the stochastic stability, the proposed method provides theoretical explanations for the impacts of uncertainties.
Highlights
The environmental pollution resulting from fossil fuels is posing a great challenge for human society
Given the above stated background, this paper proposes an analytical method for stochastic small signal stability analysis of a power system with respect to the magnitudes of uncertainties
This work focuses on power system small signal stability with respect to various degrees of uncertainty magnitudes
Summary
The environmental pollution resulting from fossil fuels is posing a great challenge for human society. Given the above stated background, this paper proposes an analytical method for stochastic small signal stability analysis of a power system with respect to the magnitudes of uncertainties. The probability of the state deviations falling within an interval is provided in an analytical way, so it is easier than the existing methods by solving a partial differential equation; Secondly, only some algebraic conditions need to be examined for identifying stochastic stability and this can be carried out efficiently compared with the Monte Carlo based approaches for practical applications; Thirdly, since the proposed system stability index explicitly takes the uncertainty magnitudes into consideration, the dynamic process of the system states with uncertainties can be theoretically explained.
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