电力系统可靠性评估的重要抽样影响增量方法

Importance Sampling-based Incremental Impact Method for Power System Reliability Evaluation

针对大规模输电系统可靠性评估效率较低的问题,提出了一种基于重要抽样的影响增量方法。首先,利用迭代方法构造新的事故概率分布函数,进而得到重要抽样权函数;其次,根据所构造的事故概率分布函数对失效空间进行偏倚抽样,得到大量对评估结果有效的样本;最后利用基于影响增量的计算公式对系统可靠性指标进行计算。该方法可以在分析较少的事故状态下,达到大量抽样的蒙特卡洛方法的评估精度,影响增量公式将高阶事故权重向低阶事故转移,从而提升可靠性评估的准确性和效率,同时重要抽样方法减小了方差并加快收敛速度,进一步提升了可靠性评估的计算效率。算例分析部分选取IEEE-RTS-24节点测试系统,测试结果证实了所提方法的高精度和高效率特性。

Aimed at the lower efficiency of the reliability evaluation method for a large-scale power transmission system,an importance sampling-based incremental impact method is proposed. First,a novel contingency probability distribution function is constructed using the iterative method,thus obtaining an importance sampling weight function. Second,the failure space is bias sampled by utilizing the novel contingency probability distribution function,thereby obtaining a large number of samples that are effective for the evaluation results. Finally,the incremental impact-based calculation formula is used to calculate the system reliability indices. Under the circumstance of fewer continency states,the proposed method can realize an evaluation accuracy as high as the Monte Carlo method with a large number of samples. The incremental impact-based formula can transfer the weight of a high-order contingency to a low-order one,thus improving the accuracy and efficiency of reliability evaluation. Meanwhile,the importance sampling method can reduce the variance and accelerate the convergence speed,which further improves the calculation efficiency of reliability evaluation. A case study is carried out on an IEEE-RTS 24-bus system,and results verify that the proposed method has characteristics of high accuracy and high efficiency.