A Novel method to estimate Economic Replacing Time of Transformer Using Monte Carlo Algorithm and ANN
AbstractA hybrid method for developing a more principled approach is presented to determine the life expectancy of transformers. The approach is constructed on an economic analysis of the transformers operational characteristics in combination with the technical issues incorporated in the decision process. In this method, firstly life time of transformer is estimated using a hybrid method based on Monte Carlo algorithm and artificial neural network. Also Pareto distribution function is applied to consider health history of transformer and uncertainty in DP behavior of transformer. In the next step, a method is proposed in order to estimate economic replacement time of transformer. This method is based on the well-known bathtub failure model, containing repairs and scheduled maintenance, in order to achieve at a more economically aim replacing decision. This aim is obtained in part by considering the uncertainty intrinsic in transformer failures and the corresponding discontinuations in power. In essence, this method organizes a decision support system for determination the life expectancy of a transformer. Simulation results show the high accuracy and functionality of the proposed approach in estimating economic replacing time of the Transformer.
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