系统状态空间分割法在电力系统可靠性评估中的应用

Application of State-Space Partitioning Method in Power System Reliability Assessment

目前电力系统可靠性评估方法无法避免对无故障状态子空间的抽样,为此提出了系统状态空间分割法。该方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,并根据各阶故障状态子空间特点分别采用解析法和蒙特卡洛法进行分析。该方法不需要对无故障状态子空间抽样,并可对各故障状态子空间的抽样次数进行最优分配。同时提出了仅需要少量均匀随机数便能抽取特定阶数故障状态的系统随机状态抽样方法,该抽样方法应用于大规模系统中具有明显优势。算例结果验证了该方法的可行性。

It is inevitable for current methods of power system reliability assessment to perform sampling in unfaulty state subspace. To change this situation, a new sampling idea is proposed： the system state space is diveided into unfaulty state subspace and faulty state subspaces in different orders; according to their characteristics, these faulty state subspaces are analyzed by analytic method and Monte Carlo method respectively, thus the sampling in unfaulty state subspace is not needed and the number of sampling times in faulty state subspaces can be optimally allocated. On this basis a sampling method for system random state, in which only a small amount of uniform random number is needed to extract the fault state in specific order, is given and the given sampling method possesses evident advantage when it is applied to large-scale power grid. Simulation results of IEEE RTS system show that the given sampling method is feasible.