用于系统可靠性评估的各阶故障独立重要抽样算法

Algorithm Evaluating Power Systems Reliability With Separate Importance Sampling to Each State Subspace of Different Contingencies Order

提出一种电力系统可靠性评估的高效算法,即各阶故障状态子空间独立重要抽样算法。该算法将系统状态空间分割为无故障状态子空间和各阶故障状态子空间,完全避免了对无故障状态子空间的抽样;且由于低阶故障状态子空间状态个数较少,采用解析法对其分析,而高阶故障状态子空间状态个数庞大,采用重要抽样法对其进行模拟分析;并对这些子空间的抽样次数进行最优分配。由于完全避免了对无故障状态子空间的抽样,并对各阶子空间进行独立分析,该算法在计算效率上具有很大的优势,适用于高可靠性系统。应用该算法对IEEE-RTS系统的发输电部分进行可靠性评估,并与自适应重要抽样方法和直接蒙特卡罗法进行比较,结果表明该算法在计算效率上具有明显优势。

An efficient algorithm of power systems reliability evaluation named separate importance sampling(SIS) taken from different contingencies order state subspaces was presented.With the SIS,the system state space is partitioned into one contingencies free state subspace(CFSS) and different contingencies order state subspaces(DCOSS).State enumeration method is used for handling lower order contingencies state subspaces,for the number of states in these subspaces is smaller.And SIS is applied to higher order contingencies state subspaces,for the number of states in them is larger.Because sampling the CFSS is avoided and DCOSS are considered separately,the SIS is efficient and can be applied to reliable power system.Compared to other methods,the results of the IEEE-RTS test system show that the proposed algorithm is correct and effective.