Stacked Ensemble Regression Model for Prediction of Furan

Mohammad Amin Faraji, Alireza Shooshtari and Ayman El-Hag ()
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Mohammad Amin Faraji: Department of Mechanical Engineering, University of Tehran, Tehran 1439813141, Iran
Alireza Shooshtari: School of Electrical and Computer Engineering, University College of Engineering, University of Tehran, Tehran 14395515, Iran
Ayman El-Hag: Department of Electrical & Computer Engineering, University of Waterloo, Waterloo, ON N2L 3G1, Canada

Energies, 2023, vol. 16, issue 22, 1-11

Abstract: Furan tests provide a non-intrusive and cost-effective method of estimating the degradation of paper insulation, which is critical for ensuring the reliability of power grids. However, conducting routine furan tests can be expensive and challenging, highlighting the need for alternative methods, such as machine learning algorithms, to predict furan concentrations. To establish the generalizability and robustness of the furan prediction model, this study investigates two distinct datasets from different geographical locations, Utility A and Utility B. Three scenarios are proposed: in the first scenario, a round-robin cross-validation method was used, with 75% of the data for training and the remaining 25% for testing. The second scenario involved training the model entirely on Utility A and testing it on Utility B. In the third scenario, the datasets were merged, and round-robin cross-validation was applied, similar to the first scenario. The findings reveal the effectiveness of machine learning algorithms in predicting furan concentrations, and particularly the stacked generalized ensemble method, offering a non-intrusive and cost-effective alternative to traditional testing methods. The results could significantly impact the maintenance strategies of power and distribution transformers, particularly in regions where furan testing facilities are not readily available.

Keywords: furan; machine learning; transformer (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
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