Prediction and Feature-Level Analysis of Power Quality Indicators in Industrial Power Systems with Reactive Power Compensation Active and Inactive Using Ensemble Learning

Faruk Kürker

doi:10.5152/tepes.2026.25035

Prediction and Feature-Level Analysis of Power Quality Indicators in Industrial Power Systems with Reactive Power Compensation Active and Inactive Using Ensemble Learning

Abstract

Reactive power compensation systems are widely used in industrial power grids for power factor improvement and reactive power optimization. However, the reliable estimation of power quality indicators under conditions where compensation is active or inactive is a critical engineering problem that has not been sufficiently addressed in the literature. This study proposes a prediction approach that provides high accuracy, interpretability, and noise robustness for both operating conditions. The input space consists of 19 physical parameters, including active (P), reactive (Q), and apparent (S) powers for each of the three phases, RMS current and voltage values per phase (I1rms-3rms and U1rms–3rms), interphase voltages (U12, U13, U23), and neutral current (Ineutral). The predicted targets are 11 fundamental power quality indicators: power factor (PF1–3, Pftot), displacement power factor (dPF1–3, dPFtot), and total harmonic distortion ratio per phase (ITHD1–3). The dataset was split into training and test subsets using a hold-out strategy, and independent bagging-based ensemble regression models were constructed for each target variable. Experimental results showed that the R² values ranged from 0.987 to 0.997 and the mean absolute percentage error (MAPE) values ranged from 0.68% to 3.57% for all targets. Normalized feature importance scores revealed the dominant role of RMS current components per phase. Model robustness was maintained with ΔR² < 0.004 under 5% Gaussian noise. The findings prove that the proposed method can predict power quality indicators with high reliability in both compensation cases and is suitable for predictive maintenance applications with real-time monitoring on an industrial scale.

Keywords

References

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Details

Primary Language

English

Subjects

Electrical Engineering (Other)

Journal Section

Research Article

Authors

Faruk Kürker ^*
0000-0003-1544-9743
Türkiye

Publication Date

February 27, 2026

Submission Date

September 12, 2025

Acceptance Date

October 8, 2025

Published in Issue

Year 2026 Volume: 6 Number: 1

DOI

https://doi.org/10.5152/tepes.2026.25035

IZ

https://izlik.org/JA98BX76GZ

Cite

RIS / Bibtex

APA

Kürker, F. (2026). Prediction and Feature-Level Analysis of Power Quality Indicators in Industrial Power Systems with Reactive Power Compensation Active and Inactive Using Ensemble Learning. Turkish Journal of Electrical Power and Energy Systems, 6(1), 35-47. https://doi.org/10.5152/tepes.2026.25035

AMA

1.Kürker F. Prediction and Feature-Level Analysis of Power Quality Indicators in Industrial Power Systems with Reactive Power Compensation Active and Inactive Using Ensemble Learning. TEPES. 2026;6(1):35-47. doi:10.5152/tepes.2026.25035

Chicago

Kürker, Faruk. 2026. “Prediction and Feature-Level Analysis of Power Quality Indicators in Industrial Power Systems With Reactive Power Compensation Active and Inactive Using Ensemble Learning”. Turkish Journal of Electrical Power and Energy Systems 6 (1): 35-47. https://doi.org/10.5152/tepes.2026.25035.

EndNote

Kürker F (February 1, 2026) Prediction and Feature-Level Analysis of Power Quality Indicators in Industrial Power Systems with Reactive Power Compensation Active and Inactive Using Ensemble Learning. Turkish Journal of Electrical Power and Energy Systems 6 1 35–47.

IEEE

[1]F. Kürker, “Prediction and Feature-Level Analysis of Power Quality Indicators in Industrial Power Systems with Reactive Power Compensation Active and Inactive Using Ensemble Learning”, TEPES, vol. 6, no. 1, pp. 35–47, Feb. 2026, doi: 10.5152/tepes.2026.25035.

ISNAD

Kürker, Faruk. “Prediction and Feature-Level Analysis of Power Quality Indicators in Industrial Power Systems With Reactive Power Compensation Active and Inactive Using Ensemble Learning”. Turkish Journal of Electrical Power and Energy Systems 6/1 (February 1, 2026): 35-47. https://doi.org/10.5152/tepes.2026.25035.

JAMA

1.Kürker F. Prediction and Feature-Level Analysis of Power Quality Indicators in Industrial Power Systems with Reactive Power Compensation Active and Inactive Using Ensemble Learning. TEPES. 2026;6:35–47.

MLA

Kürker, Faruk. “Prediction and Feature-Level Analysis of Power Quality Indicators in Industrial Power Systems With Reactive Power Compensation Active and Inactive Using Ensemble Learning”. Turkish Journal of Electrical Power and Energy Systems, vol. 6, no. 1, Feb. 2026, pp. 35-47, doi:10.5152/tepes.2026.25035.

Vancouver

1.Faruk Kürker. Prediction and Feature-Level Analysis of Power Quality Indicators in Industrial Power Systems with Reactive Power Compensation Active and Inactive Using Ensemble Learning. TEPES. 2026 Feb. 1;6(1):35-47. doi:10.5152/tepes.2026.25035