Performance Comparison of Deep Learning Architectures for Current Total Harmonic Distortion Estimation Using Thermal Infrared Images
Year: 2026
Type: article
Abstract: This study proposes a non-intrusive method for estimating Total Harmonic Distortion (THD) in electrical systems using thermal infrared images of components in low-voltage distribution networks.Conventional THD measurement requires direct electrical access, which poses safety and practicality challenges; thus, a contactless alternative based on thermal signatures offers significant operational advantages.A real-world dataset consisting of 189 thermal images was collected from three locations: NRC (84 images), RS_Roemani (57 images), and GKB2 (48 images).Each image was labeled with the corresponding THD percentage measured using a power quality analyzer.Three deep learning architectures, VGG16, ResNet50, and EfficientNetB1, were adapted as regression models via transfer learning, with their final layers reconfigured to output continuous THD values.All models were trained on 80% of the data and evaluated on the remaining 20% using standard regression metrics: Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), and coefficient of determination (R² ).Experimental results show that VGG16 outperforms the other models, achieving the lowest MAE (1.22%), RMSE (1.73%), and MAPE (16.63%), as well as the highest R² (0.38).Although EfficientNetB1 converges fastest and ResNet50 demonstrates stable training behavior, both show reduced effectiveness in capturing THD variability, particularly under high-THD operating conditions observed in the RS_Roemani dataset.Visual and quantitative analyses confirm that VGG16 best captures the physical relationship between thermal anomalies and harmonic distortion.This work demonstrates the feasibility of vision-based, non-intrusive THD monitoring.It establishes VGG16 as a promising backbone for non-intrusive THD estimation in power-quality monitoring applications across industrial and smart-grid environments.
DOI: 10.18280/jesa.590709
Language: English
Topic: Power Quality and Harmonics
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7204710074