Physics-inspired time-frequency feature extraction and lightweight neural network for power quality disturbance classification

Physics-inspired time-frequency feature extraction and lightweight neural network for power quality disturbance classification

This study proposes a lightweight and efficient classification method for Power Quality Disturbances (PQDs) using the PowerMobileNet model, which combines the S-transform for time-frequency feature extraction and the MobileNetV3-CBAM neural network for enhanced classification performance. Extensive...

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Bibliographic Details
Main Authors: Zhiwen Hou, Boyu Wang, Jingrui Liu, Yumeng He, Yuxuan Yao
Format: Article
Language:English
Published: Frontiers Media S.A. 2025-07-01
Series:Frontiers in Physics
Subjects:
power quality disturbances
MobileNetV3-CBAM
S-transform
lightweight model
real-time monitoring
Online Access:https://www.frontiersin.org/articles/10.3389/fphy.2025.1616367/full
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Summary:This study proposes a lightweight and efficient classification method for Power Quality Disturbances (PQDs) using the PowerMobileNet model, which combines the S-transform for time-frequency feature extraction and the MobileNetV3-CBAM neural network for enhanced classification performance. Extensive experiments demonstrate that PowerMobileNet achieves a prediction accuracy of 99.33%, significantly surpassing traditional Convolutional Neural Networks (CNNs) at 97.07% and MobileNetV3-SE at 98.58%. Compared to other state-of-the-art models, PowerMobileNet outperforms KELM (97.4%), SqueezeNet (99.0%), ShuffleNet V2 (98.6%), and AlexNet (98.3%) in terms of classification accuracy. Additionally, it exhibits superior robustness under various signal-to-noise ratio (SNR) conditions, maintaining high accuracy even at low SNR levels (e.g., 90% accuracy at 20 dB). The model’s parameter count is drastically reduced to 374,632 (1.43 MB), compared to the traditional CNN’s 112,094,345 (427.61 MB), making it highly suitable for resource-constrained environments. Furthermore, PowerMobileNet demonstrates the shortest runtime, with a training duration of 925 s and a classification time of 0.57 s. These results validate the effectiveness and efficiency of PowerMobileNet for real-time PQD classification, offering significant potential for practical power quality monitoring applications.
ISSN:2296-424X

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