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

Onur Akar
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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.
电力变压器过载压降的人工神经网络估计
随着世界人口的增长、科技的发展和工业化,对电能的需求日益迅速地增加。因此,确保从产生电能到传输给消费者的过程中的持久性是非常重要的。电力变压器是电力系统中最重要的部件之一,在电力输配电过程中起着重要的作用。因此,为了保证电力系统的质量和可靠性,必须保证电力变压器的连续运行。尽管发电、输配电主管机构对电力变压器的连续运行进行了检查,但由于过载导致的电压降故障时有发生。本研究利用不同质量的电力变压器数据,利用人工神经网络对过载引起的电压降进行估计。观察到,通过人工神经网络估计得到的值的正确率为99%。认为本研究将对该领域的其他研究起到示范作用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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