Research Article Open Access

Hot Resistance Estimation for Dry Type Transformer Using Multiple Variable Regression, Multiple Polynomial Regression and Soft Computing Techniques

M. Srinivasan1 and A. Krishnan1
  • 1 ,
American Journal of Applied Sciences
Volume 9 No. 2, 2012, 231-237

DOI: https://doi.org/10.3844/ajassp.2012.231.237

Submitted On: 9 November 2011 Published On: 22 December 2011

How to Cite: Srinivasan, M. & Krishnan, A. (2012). Hot Resistance Estimation for Dry Type Transformer Using Multiple Variable Regression, Multiple Polynomial Regression and Soft Computing Techniques. American Journal of Applied Sciences, 9(2), 231-237. https://doi.org/10.3844/ajassp.2012.231.237

Abstract

Problem statement: This study presents a novel method for the determination of average winding temperature rise of transformers under its predetermined field operating conditions. Rise in the winding temperature was determined from the estimated values of winding resistance during the heat run test conducted as per IEC standard. Approach: The estimation of hot resistance was modeled using Multiple Variable Regression (MVR), Multiple Polynomial Regression (MPR) and soft computing techniques such as Artificial Neural Network (ANN) and Adaptive Neuro Fuzzy Inference System (ANFIS). The modeled hot resistance will help to find the load losses at any load situation without using complicated measurement set up in transformers. Results: These techniques were applied for the hot resistance estimation for dry type transformer by using the input variables cold resistance, ambient temperature and temperature rise. The results are compared and they show a good agreement between measured and computed values. Conclusion: According to our experiments, the proposed methods are verified using experimental results, which have been obtained from temperature rise test performed on a 55 kVA dry-type transformer.

  • 1,417 Views
  • 3,729 Downloads
  • 3 Citations

Download

Keywords

  • Multiple Variable Regression (MVR)
  • Adaptive Neuro Fuzzy Inference System (ANFIS)
  • Artificial Neural Network (ANN)
  • Fuzzy Inference System (FIS)