Review on Learning System for Detecting Transformer Internal Faults

Authors

  • Kumar Shri Ram PG Student, Department of Electrical Engineering, IET Bhaddal, Ropar, Punjab, India.
  • Ankush Sood Department of Electrical Engineering, IET Bhaddal, Ropar, Punjab, India.
  • Priya Sharma Department of Electrical Engineering, IET Bhaddal, Ropar, Punjab, India.
  • Navdeep Singh Department of Electrical Engineering, IET Bhaddal, Ropar, Punjab, India

Keywords:

Miniature transformer, internal fault, neural network (NN), Back propagation algorithm, fault detection

Abstract

Miniature transformer is one of the most important components of electronic devices. A serious failure of such kind of transformer may cause loss of time and money. This paper presents a learning system to recognize internal fault of such kind of transformer. The different kinds of faults are made to occur intentionally and data are collected at various conditions. The faults include turn to turn, winding to ground, and dielectric faults. The data are then processed and entered in the learning algorithms to recognize the type of fault. We devise a learning system to recognize the various types of faults. Several versions of learning algorithms such as standard back propagation, Levenberg-Marquardt, Bayesian regulation, Resilient back propagation, Gradient descent, One-step secant, Elman recurrent network are used. The result of Levenberg-Marquardt algorithm was found to be faster than that of other algorithms. Therefore it is suitable for real time faultdetection.

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Published

2020-07-16 — Updated on 2020-07-17

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How to Cite

Shri Ram, K., Sood, A., Sharma, P., & Singh, N. (2020). Review on Learning System for Detecting Transformer Internal Faults. International Journal of Recent Advances in Science and Technology, 6(3), 01-11. Retrieved from https://www.ijrast.com/index.php/ijrast/article/view/1 (Original work published July 16, 2020)