Optimal Asset Management for Smart Grid Applications

Abstract

Asset management is now one of the most important issues in the electrical energy industry since it is one of the requirements of smart grid. The conventional way of forming the hazard rate function of transformers applies the best modeling based on the history data using Hazard Plotting Approach (HPA) under different functions: Normal Distribution, Lognormal Distribution, Weibull Distribution, and Smallest Extreme Value Distribution. In this thesis, a new method is proposed to develop the best modeling using Artificial Neural Network- based polynomial model with minimum error to represent the hazard rate function of the power transformers. The procedure of applying the proposed methodology is simple. The quality of the obtained results ensures the adequacy of applying this methodology for expecting the failure age of the transformer. The developed polynomial equation is used to estimate the