Research Article
A Minute-Level High-Frequency Data Acquisition and Lightweight Edge Diagnosis Algorithm for Low-Voltage Distributed Photovoltaic Systems Based on a Temporal Graph Convolutional Network
@ARTICLE{10.4108/ew.14123, author={Xuekai Hu and Shiwei Xue and Xingzhi Chang and Kun Jiang and Da Zhang}, title={A Minute-Level High-Frequency Data Acquisition and Lightweight Edge Diagnosis Algorithm for Low-Voltage Distributed Photovoltaic Systems Based on a Temporal Graph Convolutional Network}, journal={EAI Endorsed Transactions on Energy Web}, volume={13}, number={1}, publisher={EAI}, journal_a={EW}, year={2026}, month={8}, keywords={Temporal Graph Convolutional Network, Low-Voltage Distributed Photovoltaics, High-Frequency Data Acquisition, Lightweight Processing, Edge Diagnosis}, doi={10.4108/ew.14123} }- Xuekai Hu
Shiwei Xue
Xingzhi Chang
Kun Jiang
Da Zhang
Year: 2026
A Minute-Level High-Frequency Data Acquisition and Lightweight Edge Diagnosis Algorithm for Low-Voltage Distributed Photovoltaic Systems Based on a Temporal Graph Convolutional Network
EW
EAI
DOI: 10.4108/ew.14123
Abstract
The performance disparities and operational characteristics among diverse devices within low-voltage distributed photovoltaic (PV) systems lead to variations in fault manifestations. The limited sampling frequency inherent in conventional approaches fails to capture critical fault features and temporal information in a timely manner. Moreover, the presence of periodic fluctuations in PV fault data contributes to persistently high false alarm rates. This paper proposes a high-frequency, minute-level data acquisition framework and a lightweight edge diagnosis algorithm for low-voltage distributed PV systems based on a temporal graph convolutional network. The proposed approach employs intelligent PV edge terminals to enable high-frequency data acquisition from distributed PV generation units. A graphical representation of the low-voltage distributed PV plant is constructed, from which dynamic temporal features of PV generation data are extracted using temporal convolutional layers, while topological correlations among PV devices are captured through graph convolutional layers. This enables the spatiotemporal joint modeling of fault characteristics. To accommodate the computational constraints of edge devices, a customized adaptation of the MobileNet-V3 architecture is introduced. By integrating attention mechanisms and implementing layer pruning, the model is tailored for enhanced performance in photovoltaic fault classification, thereby achieving lightweight edge diagnosis. Experimental results demonstrate that the proposed algorithm accurately diagnoses output voltage fluctuation faults in PV inverters and effectively identifies abnormal phase voltage fluctuation faults. It achieves high precision, recall, and F1 scores across various fault types. The model exhibits rapid training convergence with a low loss function value, satisfying the requirements for lightweight edge diagnosis in low-voltage distributed PV systems.
Copyright © 2026 Xuekai Hu et al., licensed to EAI. This is an open access article distributed under the terms of the CC BY-NC-SA 4.0, which permits copying, redistributing, remixing, transformation, and building upon the material in any medium so long as the original work is properly cited.
