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The reliability investigation considering data uncertainty;an application of Fuzzy Transformation Method in substation protection

机译:考虑数据不确定性的可靠性研究;模糊变换法在变电站保护中的应用

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This paper sheds new lights on data uncertainty in fuzzy reliability computations to achieve the best possible accuracy using minimum data. To the best of our knowledge, there are major drawbacks to implement Conventional Fuzzy Arithmetic (CFA): catastrophic overestimation, non-applicability to real-world systems and complex computations based on impractical optimizations. A novel approach namely Fuzzy Transformation Method (FTM) has recently been put forward that eliminates the core problems of CFA. The present paper aims to validate the applicability of FTM to the reliability issues. Few researchers have addressed the issues of both symmetric and asymmetric uncertainty distributions in Fuzzy Failure Rates and how a fuzzy result can profoundly come under the influence of them. Hence, a comprehensive investigation is launched to evaluate the uncertainties and some beneficial observations are provided as well as sensitivity analysis and component importance measure in fuzzy frameworks. An interesting correlation between the component importance and input-output uncertainties is observed. Additionally, pictorial representations, results and remarks are numerically discussed. We opted for All-Digital Protection Systems (ADPSs) due to novelty of them and their impacts on substation reliability. Until now, there has not been provided a fuzzy perspective concerning reliability of ADPSs.
机译:本文为模糊可靠性计算中的数据不确定性提供了新的思路,以使用最少的数据获得最佳的精度。据我们所知,实现常规模糊算法(CFA)存在主要缺点:灾难性的高估,不适用于现实世界的系统以及基于不切实际的优化的复杂计算。最近提出了一种新颖的方法,即模糊变换方法(FTM),它消除了CFA的核心问题。本文旨在验证FTM在可靠性问题上的适用性。很少有研究人员讨论过模糊故障率中对称性和非对称性不确定性分布的问题,以及如何将模糊结果深刻地影响到它们。因此,开展了全面调查以评估不确定性,并提供了一些有益的观察,以及模糊框架中的敏感性分析和组件重要性度量。观察到组件重要性与投入产出不确定性之间的有趣关系。另外,数字表示图形表示,结果和说明。由于它们的新颖性及其对变电站可靠性的影响,我们选择了全数字保护系统(ADPS)。到目前为止,还没有提供关于ADPS可靠性的模糊观点。

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