Low cost digital measurement system for determination of temperature rise in dry type transformer

Murugesan, S.; Paramasivam, S.; Krishnan, A.

doi:10.1504/ijit.2011.043589

Cited by 3 publications

(1 citation statement)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The sensing and single conditioning are designed with simple resistors. The controller is implemented by software and executed by an eZdsp F2812 board (Srinivasan et al, 2011). This experimental setup enables to measure the hot resistance as per IEC standard and the results are stored in the register.…”

Section: Methodsmentioning

confidence: 99%

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

M.¹

2012

American Journal of Applied Sciences

View full text Add to dashboard Cite

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.

show abstract

Section: Methodsmentioning

confidence: 99%

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

M.¹

2012

American Journal of Applied Sciences

View full text Add to dashboard Cite

show abstract

Fault Classification of Dry Type Transformer Using Pattern Recognition Neural Network

Kumar

Verma

2023

Lecture Notes in Electrical Engineering

View full text Add to dashboard Cite

Assessing the Reliability of Transformer Top Oil Temperature Model

Murugesan¹,

Krishnan²

2012

Int. J. Rel. Qual. Saf. Eng.

View full text Add to dashboard Cite

The hot spot temperature (HST) plays a most important role in the insulation life of the transformer. Ambient temperature and environmental variable factors involved in the top oil temperature (TOT) computations in all transformer thermal models affects insulation lifetime either directly or indirectly. The importance of the ambient temperature in transformer's insulation life, a new semi-physically-based model for the estimation of TOT in transformers has been proposed in this paper. The winding hot-spot temperature can be calculated as function of the TOT that can be estimated by using the ambient temperature, wind velocity and solar heat radiation effect and transformer loading measured data. The estimated TOT is compared with measured data of a distribution transformer in operation. The proposed model has been validated using real data gathered from a 100 MVA power transformer. For a semi-physically-based model to be acceptable, it must have the qualities of: adequacy, accuracy and consistency. We assess model adequacy using the scale: prediction R2, and plot of residuals against fitted values. To assess model consistency, we use: variance inflation factor (VIF) (which measure multicollinearity), condition number. To assess model accuracy we use mean square error, maximum and minimum error values of semi-physically-based model parameters to the existing model parameters.

show abstract

Low cost digital measurement system for determination of temperature rise in dry type transformer

Cited by 3 publications

References 13 publications

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

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

Fault Classification of Dry Type Transformer Using Pattern Recognition Neural Network

Assessing the Reliability of Transformer Top Oil Temperature Model

Contact Info

Product

Resources

About