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Composite System Adequacy Assessment Using Monte Carlo Simulation and Logistic Regression Classifier

机译:蒙特卡罗仿真和逻辑回归分类器的复合系统充足评估

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This paper presents a new method that combines Logistic Regression Classifier (LRC) and Monte Carlo Simulation (MCS) to evaluate the adequacy of a composite power system. LRC is used to pre-classify the system states as failure or success based on training data set provided by conventional MCS itself, but with a relaxed error tolerance level. The proposed method is applied to the IEEE Reliability test system (IEEE-RTS-79) to calculate the annualized and annual indices.The results thus obtained are compared with that of conventional MCS. In different cases, the simulation results provide a significant improvement in computational burden and indices calculation time while maintaining resonable accuracy.
机译:本文介绍了一种新方法,将逻辑回归分类器(LRC)和蒙特卡罗模拟(MCS)组合以评估复合电力系统的充分性。 LRC用于将系统状态预先分类为基于传统MCS本身提供的培训数据集的故障或成功,但具有放松的误差容差级别。 该提出的方法应用于IEEE可靠性测试系统(IEEE-RTS-79)以计算年化和年度指数。由此获得的结果与传统MCS的结果进行比较。 在不同的情况下,仿真结果在保持可谐振精度的同时提供计算负担和指数计算时间的显着改善。

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