SELECTION OF POWER OIL TRANSFORMERS WITH RANDOM NON-PERIODIC NATURE OF ELECTRIC LOAD

Abstract

Unlike deterministic selection models, which often leads to significant errors, stochastic selection models can use more adequate model of electrical load as a random process that can be both periodic and non-periodic. In this paper presented the method of oil transformers power selection according to the criteria of their heating and acceptable insulation wear from thermal model according MGOST 14209-97 using statistical characteristics of the electric load as a non-periodic random process. This simulation was carried out by modelling oil and most heated point temperature of the transformer and the relative deterioration of its insulation to provide their statistical characteristics primarily determined by the quantile function with corresponding probabilities. Also presented a comparison of this approach with a selection in which the load is considered as stationary random process.