Reliability Assessment of Power System Using Importance Sampling Technique Based on Layer Optimization Simulation

Abstract:

An improved importance sampling method with layer simulation optimization is presented in this paper. Through the solution sequence of the components’ optimum biased factors according to their importance degree to system reliability, the presented technique can further accelerate the convergence speed of the Monte-Carlo simulation. The idea is that the multivariate distribution’ optimization of components in power system is transferred to many steps’ optimization based on importance sampling method with different optimum biased factors. The practice is that the components are layered according to their importance degree to the system reliability before the Monte-Carlo simulation, the more forward, the more important, and the optimum biased factors of components in the latest layer is searched while the importance sampling is carried out until the demanded accuracy is reached. The validity of the presented is verified using the IEEE-RTS79 test system.

• Periodical: Applied Mechanics and Materials (Volumes 88-89)
• Pages: 554-558
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.88-89.554
• Online since: August 2011
• Authors: Bin Wang
• Keywords: Component’s Importance Degree, Convergence Speed, Layer Optimization, Monte-Carlo Simulation, Optimum Biased Factor, Simulation Optimization

References

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DOI: 10.1109/24.799896

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