基于蒙特卡罗模拟和逻辑回归分类器的复合系统充分性评估

Sangit Poudel, Nava Raj Karki
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引用次数: 0

摘要

提出了一种将逻辑回归分类器(LRC)与蒙特卡罗仿真(MCS)相结合的复合电力系统充分性评估方法。LRC基于传统MCS本身提供的训练数据集,对系统状态进行失败或成功的预分类,但具有宽松的容错级别。将该方法应用于IEEE可靠性测试系统(IEEE- rts -79),计算了年化指标和年化指标。所得结果与常规MCS进行了比较。在不同情况下,仿真结果在保持合理精度的同时,显著改善了计算量和指标计算时间。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Composite System Adequacy Assessment Using Monte Carlo Simulation and Logistic Regression Classifier
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.
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