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Stochastic Modeling and Stability Analysis of Wind Power System Based on Markov Theory

机译:基于马尔可夫理论的风电系统随机建模与稳定性分析

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With the scale of wind power integration expanding continuously, the random characteristic of power system is more and more prominent due to the volatility and intermittency of wind energy. Because of the fluctuations brought by wind power integration, the traditional deterministic analysis methods and the conventional stochastic differential equations are difficult to accurately analyze wind power system. In this paper, Markov theory is utilized to establish a stochastic Markov dynamic model for wind power systems by considering various uncertainties. Then, based on the developed model, Lyapunov energy function and M matrix are combined together to present the analytical method of stochastic mean stability and stochastic mean square stability of wind power system. Compared with the conventional stochastic differential equation analysis method, the proposed method can overcome the shortcomings that the stochastic stability cannot be analyzed when the system operating conditions change. Finally, simulation results are provided to verify the validity and correctness of the proposed method.
机译:随着风电集成规模的不断扩大,由于风能的波动性和间歇性,电力系统的随机特性越来越突出。由于风电集成带来的波动,传统的确定性分析方法和常规的随机微分方程很难准确地分析风电系统。本文利用马尔可夫理论,通过考虑各种不确定性,建立了风力发电系统的随机马尔可夫动力学模型。然后,在建立的模型的基础上,将李雅普诺夫能量函数和M矩阵结合起来,提出了风电系统随机均值稳定性和随机均方根稳定性的分析方法。与传统的随机微分方程分析方法相比,该方法克服了系统运行条件变化时无法分析随机稳定性的缺点。最后,通过仿真结果验证了该方法的有效性和正确性。

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