Modeling of Adverse Weather Events in an Integrated Reliability and Power Quality Assessment of Distribution System Using Stochastic Diffusion Process
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Abstract
This paper presents the modeling of adverse weather events and their impact on the integrated reliability and power quality assessment of power distribution systems. With respect to previous works, stochastic modeling of adverse weather events is integrated into the Monte Carlo Simulation approach and its impacts on reliability and voltage sag indices are highlighted. In this paper, the aleatory and epistemic uncertainty are modeled into sampling of Time to Failure (TTF) using the Stochastic Diffusion Process. The Stochastic Diffusion Process, including the Jump diffusion is used to model the impact of adverse weather events on permanent and temporary failure rates. The proposed method is applied to the modified IEEE 34 node test feeder, and three case studies have been performed to investigate the impact of various uncertainties and adverse weather events. Numerical results for the IEEE 34 node test feeder are presented to quantify adverse weather impacts on both reliability and voltage sag indices.
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