Архив статей журнала
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.
The paper places an emphasis on the fact that many publications on complex reliability assessment of electric power and fuel systems do not always substantiate the application of various methods. We address the “system”, “nodal”, and “estimation” approaches to assess the reliability of electric power systems, given reliable fuel supply to power plants. These approaches are accompanied by an analysis of their correspondence to the objectives and goals of the study, as well as an analysis of the validity of their application in terms of the obtained result accuracy, research time, complexity of search for and preparation of the input data and forms of their representation in a model. All the approaches were tested in case studies. The nodal and system approaches were tested on a conventional power system, while the estimation approach was tested on design diagrams of the gas and electric power systems of the Northwestern Federal District of the Russian Federation