Estimation through ANN of Voltage Drop Resulting from Overloads on Power Transformers

Abstract

Along with the increasing population, technological developments and industrialization in the world, the need for electrical energy shows rapid increase day by day. For that reason, it is very important to ensure permanence in the process from the generation of electrical energy to its transmission to consumers. One of the most significant components of power systems is the power transformer, and it plays an important role in the process of energy transmission and distribution. Thus, continuous operation of the power transformers should be ensured for the quality and reliability of the power systems. Despite the institutions in charge of electric power generation, transmission and distribution carry out inspections for the continuous operation of the power transformers, failures resulting from voltage drop arise due to overloads. In this study, the estimation through ANN of voltage drop resulting from overloads was performed using the data of power transformers of different quality. It was observed that the values obtained as the result of estimations through ANN were correct with a rate of 99%. It is considered that this study will set an example for other studies in the field.

Estimation of the electricity to be generated at different wind speeds and turbines through fuzzy logic and ANN, A case study of Balıkesir

“…Mostly the net energy consumption of the researches was estimated [26,27]. Estimation of electric power consumption [28], research of electric power consumption [29], estimation of energy demand and economy [30], determination of the total energy demand [31], energy demand for the domain of transportation, and estimation of energy consumption in lodgings and industry sectors were performed with ANN [32]. Wind information and its complex coordinates were expressed with complex figures, and they were used as the input information for a neural network with complex values.…”