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All Journal International Journal of Power Electronics and Drive Systems (IJPEDS) International Journal of Applied Power Engineering (IJAPE)
Vinayagam, Arangarajan
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Control & Systems Engineering Electrical & Electronics Engineering

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Predictions of solar power using ensemble machine learning techniques Vinayagam, Arangarajan; Mohandas, R.; Jeyabharath, R.; Mohan, B. S.; Lakshmanan, Srinivasan; Bharatiraja, C.
International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 16, No 4: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijpeds.v16.i4.pp2868-2878

Abstract

Predicting solar power production accurately is becoming more and more crucial for efficient power management and the grid's integration of renewable energy sources. Using data from an Australian photovoltaic (PV) power station, this study employs a variety of machine learning (ML) ensemble techniques, such as gradient boosting (GB), random forest (RF), and extreme gradient boosting (XGBoost), to forecast solar power production. ML models are developed utilizing pertinent information from electricity and meteorological data in order to forecast solar power. The predictive performance of trained ML models is verified in terms of metrics like mean absolute error (MAE), root mean square error (RMSE), and correlation coefficient (R2). With higher R2 values and lower error results (MAE and RMSE), XGBoost performs better than GB and RF. Optimizing the hyperparameters of the XGBoost model significantly improves its performance. The tweaked XGBoost model shows a significant improvement in R2 (more than 5% to 10%) and error results (reduced MAE and RMSE by 0.01 to 0.06), when compared to other ensemble approaches. Compared to other ensemble approaches, the tuned XGBoost methodology is more robust and generates more accurate forecasts in solar power.
High impedance fault discrimination in microgrid power system using stacking ensemble approach Vinayagam, Arangarajan; Mohandas, Raman; Chindamani, Meyyappan; Sujatha, Bhadravathi Gavirangapa; Mishra, Soumya; Sundaramurthy, Arivoli
International Journal of Applied Power Engineering (IJAPE) Vol 15, No 1: March 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijape.v15.i1.pp98-109

Abstract

High impedance (HI) faults in microgrid (MG) power systems are non-linear, intermittent, and have low fault current magnitudes, making them challenging to detect by typical protective systems. Consequently, it is imperative to implement a sophisticated protection system that is dependent on the precision of fault detection. In this study, a stacking ensemble classifier (SEC) is proposed to discriminate HI fault from other transients within a photovoltaic (PV) generated MG power system. The MG model is simulated with the introduction of faults and transients. The features of data set from event signals are generated using the discrete wavelet transform (DWT) technique. The dataset is used to train the individual classifiers (Naïve Bayes (NB), decision tree J48 (DTJ), and K-nearest neighbors (KNN)) at initial and meta learner in the final stage of SEC. The SEC outperforms other classification methods with respect to accuracy of classification, rate of success in detecting HI fault, and performance measures. The outcomes of the classification study conducted under standard test conditions (STC) of solar PV and the noisy environment of event signals clearly demonstrate that the SEC is more dependable and performs better than the individual base classification approaches.
Co-Authors Bharatiraja, C. Chindamani, Meyyappan Jeyabharath, R. Lakshmanan, Srinivasan Mohan, B. S. Mohandas, R. Mohandas, Raman Soumya Mishra, Soumya Sujatha, Bhadravathi Gavirangapa Sundaramurthy, Arivoli