Continuous-time random disturbances (also called stochastic excitations) due to increasing renewable generation have an increasing impact on power system dynamics; However, except from the Monte Carlo simulation, most existing methods for quantifying this impact are intrusive, meaning they are not based on commercial simulation software and hence are difficult to use for power utility companies. To fill this gap, this paper proposes an efficient and nonintrusive method for quantifying uncertainty in dynamic power systems subject to stochastic excitations. First, the Gaussian or non-Gaussian stochastic excitations are modeled with an It\^{o} process as stochastic differential equations. Then, the It\^{o} process is spectrally represented by independent Gaussian random parameters, which enables the polynomial chaos expansion (PCE) of the system dynamic response to be calculated via an adaptive sparse probabilistic collocation method. Finally, the probability distribution and the high-order moments of the system dynamic response and performance index are accurately and efficiently quantified. The proposed nonintrusive method is based on commercial simulation software such as PSS/E with carefully designed input signals, which ensures ease of use for power utility companies. The proposed method is validated via case studies of IEEE 39-bus and 118-bus test systems.

中文翻译：

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受随机激励影响的动态电力系统的非侵入式不确定性量化

由于可再生能源发电的增加，连续时间随机扰动（也称为随机激励）对电力系统动态的影响越来越大；然而，除了蒙特卡罗模拟之外，大多数现有的量化这种影响的方法都是侵入性的，这意味着它们不是基于商业模拟软件，因此难以用于电力公司。为了填补这一空白，本文提出了一种有效且非侵入性的方法，用于量化受随机激励影响的动态电力系统中的不确定性。首先，高斯或非高斯随机激励用 It\^{o} 过程建模为随机微分方程。然后，It\^{o} 过程由独立的高斯随机参数谱表示，这使得系统动态响应的多项式混沌展开 (PCE) 能够通过自适应稀疏概率搭配方法进行计算。最后，准确高效地量化了系统动态响应和性能指标的概率分布和高阶矩。所提出的非侵入式方法基于商业仿真软件，如 PSS/E，具有精心设计的输入信号，确保电力公司易于使用。所提出的方法通过 IEEE 39 总线和 118 总线测试系统的案例研究得到验证。所提出的非侵入式方法基于商业仿真软件，如 PSS/E，具有精心设计的输入信号，确保电力公司易于使用。所提出的方法通过 IEEE 39 总线和 118 总线测试系统的案例研究得到验证。所提出的非侵入式方法基于商业仿真软件，如 PSS/E，具有精心设计的输入信号，确保电力公司易于使用。所提出的方法通过 IEEE 39 总线和 118 总线测试系统的案例研究得到验证。