An Adaptive Multi-Task Gaussian Process Regression Approach for Harmonic Modeling of Aggregated Loads in High-Voltage Substations

Jiahui Zheng (), Kun Song, Jiaqi Duan and Yang Wang
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Jiahui Zheng: College of Electrical Engineering, Sichuan University, Chengdu 610065, China
Kun Song: Chengdu Electric Power Supply Company, State Grid Sichuan Power Supply Company, Chengdu 610041, China
Jiaqi Duan: College of Electrical Engineering, Sichuan University, Chengdu 610065, China
Yang Wang: College of Electrical Engineering, Sichuan University, Chengdu 610065, China

Energies, 2025, vol. 18, issue 17, 1-20

Abstract: To address the challenges of complex harmonic characteristics, multi-source coupling, and strong time variability in aggregated loads downstream of high-voltage substations, this paper proposes an Adaptive Multi-Task Gaussian Process Regression (AMT-GPR) method for harmonic modeling. First, field measurements from the medium-voltage side of a 500 kV substation are denoised and analyzed using Fourier transform to reveal the dynamic patterns and interdependencies of harmonic current magnitudes. Then, a multi-task GPR framework is constructed, incorporating task correlation modeling and adaptive kernel functions to capture inter-task coupling and differences in feature scales. Finally, a probabilistic harmonic model is developed based on multiple sets of measured data, and the modeling performance of AMT-GPR is compared with single-task GPR, conventional MT-GPR, and mainstream machine learning approaches including RBF, LS-SVM, and LSTM. Simulation results demonstrate that traditional harmonic modeling methods are insufficient to capture the dynamic behavior and uncertainty of aggregated loads and AMT-GPR maintains strong robustness under small-sample conditions, significantly reduces prediction errors, and yields narrower uncertainty intervals, outperforming the baseline models. These findings validate the effectiveness of the proposed method in modeling harmonics of aggregated loads in high-voltage substations and provide theoretical support for subsequent harmonic assessment and mitigation strategies.

Keywords: high-voltage substation; aggregated loads; gaussian process regression; multi-task learning; adaptive kernel function; probabilistic harmonic model (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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