Identification of faultless condition parameters of oil-filled transformers
The basis of intellectual diagnostics of power oil-filled transformers (POFT) is represented by models which make it possible to identify accurately a faultless condition of electric equipment in a number of key parameters (signs) without switching it off. Diagnostic models are proposed to identify parameters of a faultless condition of the POFT insulating system in the course of its operation using uniform statistics of long-term observations of paper and oil characteristics. Records of the periodic dissolved gas chromatographic analysis (DGCA), the results of the physical and chemical analysis (PCA) of oil and high-voltage testing of cellulose insulation are used as diagnostic information. A technique using variational series of data synchronized in time is developed for constructing diagnostic models. The method of statistical (Bayesian) identification which makes it possible to differentiate reliably such conditions of POFT as a norm", "a norm with insignificant deviations", and "a norm with considerable (critical) deviations" as well as to estimate a type and degree of danger of defects forms the basis of the proposed technique. Parameters characterizing the condition of the POFT insulating system are provided by samples of two-dimensional random variables with their distribution areas, conditional population means and mean square deviations. It allows creating special boundary functions dividing classes of POFT conditions in space of controlled signs. An algorithm of ex-press evaluation of a condition and of decision making concerning further POFT operation is developed for implementing the method computing procedure. Reliability and adequacy of the recommended operational impacts are conditioned by the validity of decision-making criterions reflecting the accepted standard principles. Examples illustrating the reliability and efficiency of the developed approach which is intended for application in systems of operating condition evaluation and for planning repairs of the transformer equipment of electric networks are given.