随机扰动引起的频率违规：一种MCMC方法
Frequency violations from random disturbances: an MCMC approach
AI总结：
本文提出一种MCMC方法，用于高效采样罕见的功率扰动，研究频率变化率违规的成因及概率。
摘要：
电力系统频率稳定性正面临各种扰动的挑战，尤其是可再生能源渗透率增加导致发电波动性和系统惯性下降。本文提出一种名为ghost sampling的Metropolis-Hastings MCMC方法变体，用于高效采样导致节点频率违规的罕见功率扰动。生成代表性随机样本回答了诸如
英文摘要：
The frequency stability of power systems is increasingly challenged by various types of disturbances. In particular, the increasing penetration of renewable energy sources is increasing the variability of power generation and at the same time reducing system inertia against disturbances. In this paper we are particularly interested in understanding how rate of change of frequency (RoCoF) violations could arise from unusually large power disturbances. We devise a novel specialization, named ghost sampling, of the Metropolis-Hastings Markov Chain Monte Carlo method that is tailored to efficiently sample rare power disturbances leading to nodal frequency violations. Generating a representative random sample addresses important statistical questions such as "which generator is most likely to be disconnected due to a RoCoF violation?" or "what is the probability of having simultaneous RoCoF violations, given that a violation occurs?" Our method can perform conditional sampling from any joint distribution of power disturbances including, for instance, correlated and non-Gaussian disturbances, features which have both been recently shown to be significant in security analyses.
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