Estimating the failure probability of an instrument transformer cell in the high voltage distributing device using an expert-statistical method

Abstract

The approach has been developed to determining the numerical value of a failure probability and to forecasting the resource of an instrument transformer cell at the time of observation. Underlying a given approach is the control over the main parameters that affect the technical condition (TC) of an instrument transformer cell in the distributing device of high voltage (DDHV). To determine a TC of the devices, a mathematical method of fuzzy modeling has been applied, which makes it possible to integrate the diagnostic parameters that are different in their nature. Building a fuzzy model involved the experience of experts in the relevant industry. The relevance of the development of a given approach is predetermined by the functional importance of a current transformer. Its performance affects the accuracy of triggering the relay protection devices, as well as the accounting of electrical energy. Precise accounting of electric energy implies minimizing its losses and shows the path to energy savings. A special feature of this approach is that it takes into consideration the influence of TC of each piece of cell equipment on the probability of its failure in general. To account for the factors of random disturbances, an expert fuzzy model is refined by the probabilistic-statistical method. An example of the DDHV instrument transformer cell in an electric-energy system has been used to substantiate the advantage of a given approach over the existing methods to control the technical condition of electrical equipment. The error in predicting the cell resource based on one parameter (thermal imaging examination) was ∆ f(D-02) =1-Q D-02 =0.364, or 36.4 %. When applying an expert-statistical model for determining the probability of a cell failure, the error was ∆f (D-02) =1-Q apost =0.034, or 3.4 %. The application of a given approach has produced a more reliable estimate of the probability of cell failure. Implementing the developed approach in the field of electrical equipment diagnosing could improve the reliability level of forecasting results. The constructed model could be applied in the automated systems that diagnose "on-line" the DDHV electric devices