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To further improve the accuracy, this study develops a saddlepoint approximation reliability method that does not require additional transformations and approximations on the quadratic function.
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An approximation method to estimate reliability of single-step results has been developed [ 23].
In this present study, ICC, which is considered to be a direct approximation of reliability, has been used for reliability assessment.
However, these methods can only provide a rough approximation of reliability indices in the operational phase because of high fluctuations of wind power generation.
The approximations are combined with efficient simulation methods to generate explicit approximations of reliability measures in terms of the design variables.
With the first and second order approximations, the reliability interval composed of the belief measure and the plausibility measure is efficiently obtained for FARM and SARM, respectively.
A few iterations yield a good approximation of the reliability index for the original problem.
The validity of the reliability approximation is checked by comparing the results to those derived through direct Monte Carlo simulation of the nonlinear model.
Also practical guidelines are provided for improvement of the computational efficiency for using the analytical reliability approximation within the algorithm that searches for the optimal system design configuration.
In particular, three technical components are proposed including: i) an accuracy metric for time dependent model responses under uncertainty, ii) effective approaches for time dependent model bias calibration and approximation, and iii) reliability analysis considering the time dependent model bias.
The positive side rejects an absolute Skeptical doubt, however, suggesting that empirical knowledge is possible because we can identify a range of strengths that are normally associated with many beliefs about appearances their reliability, approximation to the truth, and likelihood by degrees.
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