Abstract
Extreme outliers of wind power fluctuation are a source of severe damage to power systems. In our previous work, we proposed a modelling framework, verified its usefulness via real data, and develo...
Highlights
In many engineering problems, it is important to evaluate the effect of probabilistic uncertainty
In recent years, it has been pointed out that the fluctuation of wind power generation is usually small, but it takes extremely large values due to the occurrence of wind gusts and turbulence at a non-negligible frequency. These outliers bring about large frequency fluctuation to power systems interconnected to wind energy [2]
A stochastic linearization method [6,7] is extended for the systems driven by stable processes based on their mathematical similarity to Gaussian distributions [3]
Summary
It is important to evaluate the effect of probabilistic uncertainty. A huge sample size is often required to capture the effect of extreme outliers [5] To resolve these issues, a stochastic linearization method [6,7] is extended for the systems driven by stable processes based on their mathematical similarity to Gaussian distributions [3]. Contribution: The goal of this paper is to overcome this weakness to develop methods for the assessment of power system fluctuation subject to extreme outliers To this end, in this paper, we exploit the fact that the proposed error analysis can be seen as robust performance analysis of a Lur’e system having diagonal nonlinearity. The present paper shows that this method is effective for the error analysis of stochastic linearization This is not trivial due to the heavy-tailed stochasticity in the dynamics.
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More From: SICE Journal of Control, Measurement, and System Integration
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