论文标题
使用灵敏度分析和PCA的概率关键时间评估的参数减少
Parameter Reduction in Probabilistic Critical Time Evaluation Using Sensitivity Analysis and PCA
论文作者
论文摘要
在本文中，我们讨论了一种方法，以找到概率瞬态稳定性评估问题的最具影响力的电力系统参数---找到关键清除时间的概率分布。我们通过采用灵敏度分析与主成分分析相结合来执行参数选择。首先，我们确定机器角度相对于所有系统参数的灵敏度。其次，我们采用主成分分析算法来识别瞬态稳定性问题中最具影响力的参数。通过识别此类参数，我们可以将不确定参数的数量减少到瞬态稳定性概率评估中的有影响力的参数，从而在大型功率系统的概率分析中提供了显着的加速。在IEEE 14总线系统中测试了所提出的算法，获得的结果表明，我们的方法可以有效地找到最具影响力的参数。
In this paper, we discuss a method to find the most influential power system parameters to the probabilistic transient stability assessment problem---finding the probability distribution of the critical clearing time. We perform the parameter selection by employing a sensitivity analysis combined with a principal component analysis. First, we determine the sensitivity of the machine angles with respect to all system parameters. Second, we employ the principal component analysis algorithm to identify the most influential parameters in the transient stability problem. By identifying such parameters, we can reduce the number of uncertain parameters to only the influential ones in the probabilistic assessment of transient stability, providing a significant speed-up in the probabilistic analysis of large power systems. The proposed algorithm was tested in the IEEE 14 bus systems and the results obtained show that our method can effectively find the most influential parameters.
