Stacking ensemble learning for PCC voltage prediction in SEIG-ELC based off-grid micro-hydro systems
- 1Department of Electrical Engineering, National Institute of Technology Arunachal Pradesh
- 2Department of Electrical Engineering, SVERI’s College of Engineering
Abstract
Voltage stability in off-grid systems that use self-excited induction generators (SEIGs) is difficult to maintain. The limited generation capacity and the frequent load variations of rural micro-hydro settings are the main reasons. Accurate prediction of the point-of-common-coupling (PCC) voltage is therefore important for effective control and equipment safety. This research introduces a data-driven predictive model for forecasting the PCC voltage of a digitally controlled SEIG–electronic load controller (ELC) system that experiences sudden load changes. An experimental setup based on an STM32F407VG microcontroller-based ELC was built to gather real-time operational data. Three measured quantities—load power, dump power, and switching events—were used to describe the PCC voltage behaviour. A stacking ensemble (SE) learning model was then employed. It uses ridge regression as the meta-learner and integrates extreme gradient boosting, Gaussian process regression, and support vector regression as base learners. The choice of these learners is justified both qualitatively, from the structure of the SEIG steady-state model, and quantitatively, through a paired statistical comparison. The model’s performance was compared with the individual learners using standard statistical metrics, and the differences were assessed for statistical significance using paired Wilcoxon signed-rank and Diebold–Mariano tests. A convex combination-based data augmentation method, validated through kernel density estimation, was also examined but reduced predictive accuracy, which confirms the sufficiency of the original dataset. The proposed ensemble achieved superior performance ( R2 = 0.997 ) with minimal prediction error. The associated prediction uncertainty was quantified through the probabilistic Gaussian-process member to provide confidence bounds on the estimated voltage. These results show the potential of ensemble learning to improve voltage prediction and to support advanced control in SEIG–ELC based off-grid micro-hydro systems.
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BibTeX
@article{sinha2026stacking,
title = {{Stacking ensemble learning for PCC voltage prediction in SEIG-ELC based off-grid micro-hydro systems}},
author = {Shalini Sinha and Mrinal Kanti Rajak and Rajen Pudur},
journal = {Scientific Reports},
year = {2026},
month = jul,
doi = {10.1038/s41598-026-61776-0},
url = {https://doi.org/10.1038/s41598-026-61776-0},
note = {CC BY},
}
RIS
TY - JOUR
TI - Stacking ensemble learning for PCC voltage prediction in SEIG-ELC based off-grid micro-hydro systems
AU - Shalini Sinha
AU - Mrinal Kanti Rajak
AU - Rajen Pudur
PY - 2026
DA - 2026/07/01
JO - Scientific Reports
DO - 10.1038/s41598-026-61776-0
UR - https://doi.org/10.1038/s41598-026-61776-0
AB - Voltage stability in off-grid systems that use self-excited induction generators (SEIGs) is difficult to maintain. The limited generation capacity and the frequent load variations of rural micro-hydro settings are the main reasons. Accurate prediction of the point-of-common-coupling (PCC) voltage is therefore important for effective control and equipment safety. This research introduces a data-driven predictive model for forecasting the PCC voltage of a digitally controlled SEIG–electronic load controller (ELC) system that experiences sudden load changes. An experimental setup based on an STM32F407VG microcontroller-based ELC was built to gather real-time operational data. Three measured quantities—load power, dump power, and switching events—were used to describe the PCC voltage behaviour. A stacking ensemble (SE) learning model was then employed. It uses ridge regression as the meta-learner and integrates extreme gradient boosting, Gaussian process regression, and support vector regression as base learners. The choice of these learners is justified both qualitatively, from the structure of the SEIG steady-state model, and quantitatively, through a paired statistical comparison. The model’s performance was compared with the individual learners using standard statistical metrics, and the differences were assessed for statistical significance using paired Wilcoxon signed-rank and Diebold–Mariano tests. A convex combination-based data augmentation method, validated through kernel density estimation, was also examined but reduced predictive accuracy, which confirms the sufficiency of the original dataset. The proposed ensemble achieved superior performance ( R^2 = 0.997 ) with minimal prediction error. The associated prediction uncertainty was quantified through the probabilistic Gaussian-process member to provide confidence bounds on the estimated voltage. These results show the potential of ensemble learning to improve voltage prediction and to support advanced control in SEIG–ELC based off-grid micro-hydro systems.
SN - 2045-2322
ER -