Estimating the weight of main material for 63/20kV transformers with Artificial Neural Network (ANN)

Abstract

Power transformer is one of the most important components in electrical network which play effective role in the electrification. The same way that continuous performance of transformers is necessary to retaining the network reliability, forecasting its costs is also important for manufacturer and industrial companies. Since major amount of transformers costs is related to its raw materials, so having the amount of used raw material in various conditions in transformers has a high importance in costs estimating process. This paper presents a new method to estimate the weight of main material for 63/20kV transformers. The method is based on Multilayer Perceptron Neural Network (MPNN) with sigmoid transfer function. The back-propagation (BP) algorithm is used to adjust the parameters of MPNN. The required training data for MPNN are the obtained information from the transformers made by Iran-Transfo Company during last 4 years.