Neural techniques for transformer insulator analysis
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Keywords

high voltage electrical equipment
partial discharge
neural networks
failures

How to Cite

Quiroga, J., & Segura D´ RouvelJ. J. (2020). Neural techniques for transformer insulator analysis. Athenea Engineering Sciences Journal, 1(2), 12-20. https://doi.org/10.47460/athenea.v1i2.7

Abstract

This article presents the Analysis of Partial Discharge using Neural Techniques. Rotating machines used in the industry tend to have insulation failures caused by a lack of maintenance and ignorance of their status. It is important to carry out periodic tests and continuous evaluations of the state of the insulation to guarantee the correct operation of the machines. One of the methods used to detect these faults is Partial Discharge. Which consist of small discharges produced in a portion of gas that is dissolved in the oil or dielectric that constitutes the insulation of electrical machines. In this research work, an analysis of two works developed around partial discharges is carried out, where artificial intelligence techniques have been implemented. The results showed the high effectiveness of neural networks to achieve the classification of partial discharges and contribute to the maintenance of high-power electrical equipment.

Keywords: high voltage electrical equipment, partial discharge, neural networks, failures

https://doi.org/10.47460/athenea.v1i2.7
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