A Fiducial Approach To Comparing The Electric Power Objects Of The Same Type

  • E. M. Farhadzadeh Azerbaijan Scientific-Research and Design-Prospecting Power Engineering Institute, Baku, Republic of Azerbaijan
  • A. Z. Muradaliyev Azerbaijan Scientific-Research and Design-Prospecting Power Engineering Institute, Baku, Republic of Azerbaijan
  • Y. Z. Farzaliyev Azerbaijan Scientific-Research and Design-Prospecting Power Engineering Institute, Baku, Republic of Azerbaijan
  • T. K. Rafiyeva Azerbaijan Scientific-Research and Design-Prospecting Power Engineering Institute, Baku, Republic of Azerbaijan
  • S. A. Abdullayeva Azerbaijan Scientific-Research and Design-Prospecting Power Engineering Institute, Baku, Republic of Azerbaijan
Keywords: reliability indices, varieties of attributes, classification, expediency, risk of the erroneous decision

Abstract

An increase in service life of equipment and plants (objects) in electric power systems makes it more appropriate to relate the organization of a system of maintenance service and restoration of wear and tear to their technical condition. This, in turn generates the need to quantitatively estimate the indices of their individual reliability. There can be no data on failures and defects of concrete objects, therefore, in practice we often calculate generalized reliability indices. An intuitive understanding of the varied significance of varieties of attributes is reflected by classifying statistical data for some varieties of attributes. For example, they can be classified according to voltage class, design, service life, etc. At the same time, the question on the appropriateness of the statistical data classification is not considered. Initial assumptions of known methods and criteria of checking if it is expedient to classify the statistical data on failures of the electric power system objects in most cases are unacceptable, since they are not relevant to this data set. We have developed a new method and an algorithm to assess the appropriateness of the statistical data classification. Their novelty lies in the application of a fiducial approach to estimation of critical values of a sample from a set of multivariate statistical data.

Published
2019-02-28