Authors :
Rosena Shintabella; Catur Edi Widodo; Adi Wibowo
Volume/Issue :
Volume 9 - 2024, Issue 3 - March
Google Scholar :
https://tinyurl.com/eu3kufdc
Scribd :
https://tinyurl.com/rv8bebdc
DOI :
https://doi.org/10.38124/ijisrt/IJISRT24MAR1125
Abstract :
Prediction for loss of life transfomer is very
important to ensure the reliability and efficiency of the
power system. 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). A
stacking ensemble framework is then used to combine the
predictions of these base models using a meta model
namely Logistic Regression (LR). 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. By integrating a variety of basic models,
applying improved stacking layouts using GA, these
models offer valuable insights to improve maintenance
strategies and system reliability in power grids.
Keywords :
Genetic Algorithm, Stacking Ensemble, Stacking- GA, Loss of Life Transformer Prediction.
Prediction for loss of life transfomer is very
important to ensure the reliability and efficiency of the
power system. 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). A
stacking ensemble framework is then used to combine the
predictions of these base models using a meta model
namely Logistic Regression (LR). 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. By integrating a variety of basic models,
applying improved stacking layouts using GA, these
models offer valuable insights to improve maintenance
strategies and system reliability in power grids.
Keywords :
Genetic Algorithm, Stacking Ensemble, Stacking- GA, Loss of Life Transformer Prediction.
