Loss of Life Transformer Prediction Based on Stacking Ensemble Improved by Genetic Algorithm By IJISRT

🤖 Plain-English Summary

Prediction for loss of life transfomer is very important to ensure the reliability and efficiency of the power system. The results show a significant improvement in both transformers using stacking-GA, both TR-A and TR-B, with each prediction evaluation 99% and with a minimal error rate, namely approaching 0.the developed framework presents a promising solution for accurate and reliable transformer life prediction.

🔑 Key Findings

• In this paper, an innovative model is proposed to improve the accuracy of lost of life transfomer prediction using stacking ensembles enhanced with genetic algorithm (GA).
• The aim is to develop a robust model to estimate the remaining life of a transformer in order to generally increase the reliability of the electrical energy distribution system.
• This approach involves integrating various machine learning models as a basic model, namely Support Vector Machines (SVM) and K-Nearest Neighbor (KNN).

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