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Sustainability
Volume 18
Issue 3
10.3390/su18031550
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This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

Sustainable and Reliable Smart Grids: An Abnormal Condition Diagnosis Method for Low-Voltage Distribution Nodes via Multi-Source Domain Deep Transfer Learning and Cloud-Edge Collaboration

by
Dongli Jia
*,
Tianyuan Kang
,
Xueshun Ye
,
Jun Zhou
and
Zhenyu Zhang
China Electric Power Research Institute, Beijing 100192, China
*
Author to whom correspondence should be addressed.
Sustainability 2026, 18(3), 1550; https://doi.org/10.3390/su18031550
Submission received: 14 December 2025 / Revised: 21 January 2026 / Accepted: 2 February 2026 / Published: 3 February 2026
(This article belongs to the Special Issue Microgrids, Electrical Power and Sustainable Energy Systems)
Versions Notes

Abstract

The transition toward sustainable and resilient new-type power systems requires robust diagnostic frameworks for terminal power supply units to ensure continuous grid stability. To ensure the resilience of modern power systems, this paper proposes a multi-source domain deep Transfer Learning method for the abnormal condition diagnosis of low-voltage distribution nodes within a cloud-edge collaborative framework. This approach integrates feature selection based on the Categorical Boosting (CatBoost) algorithm with a hybrid architecture combining a Convolutional Neural Network (CNN) and a Residual Network (ResNet). Additionally, it utilizes a multi-loss adaptation strategy consisting of Multi-Kernel Maximum Mean Difference (MK-MMD), Local Maximum Mean Difference (LMMD), and Mean Squared Error (MSE) to effectively bridge domain gaps and ensure diagnostic consistency. By balancing global commonality with local adaptation, the framework optimizes resource efficiency, reducing collaborative training time by 19.3%. Experimental results confirm that the method effectively prevents equipment failure, achieving diagnostic accuracies of 98.29% for low-voltage anomalies and 88.96% for three-phase imbalance conditions.
Keywords: low-voltage distribution node; abnormal condition diagnosis; cloud edge collaboration; deep transfer network; multi-source domain adaptation; smart grid sustainability; resource efficiency; infrastructure resilience low-voltage distribution node; abnormal condition diagnosis; cloud edge collaboration; deep transfer network; multi-source domain adaptation; smart grid sustainability; resource efficiency; infrastructure resilience

Share and Cite

MDPI and ACS Style

Jia, D.; Kang, T.; Ye, X.; Zhou, J.; Zhang, Z. Sustainable and Reliable Smart Grids: An Abnormal Condition Diagnosis Method for Low-Voltage Distribution Nodes via Multi-Source Domain Deep Transfer Learning and Cloud-Edge Collaboration. Sustainability 2026, 18, 1550. https://doi.org/10.3390/su18031550

AMA Style

Jia D, Kang T, Ye X, Zhou J, Zhang Z. Sustainable and Reliable Smart Grids: An Abnormal Condition Diagnosis Method for Low-Voltage Distribution Nodes via Multi-Source Domain Deep Transfer Learning and Cloud-Edge Collaboration. Sustainability. 2026; 18(3):1550. https://doi.org/10.3390/su18031550

Chicago/Turabian Style

Jia, Dongli, Tianyuan Kang, Xueshun Ye, Jun Zhou, and Zhenyu Zhang. 2026. "Sustainable and Reliable Smart Grids: An Abnormal Condition Diagnosis Method for Low-Voltage Distribution Nodes via Multi-Source Domain Deep Transfer Learning and Cloud-Edge Collaboration" Sustainability 18, no. 3: 1550. https://doi.org/10.3390/su18031550

APA Style

Jia, D., Kang, T., Ye, X., Zhou, J., & Zhang, Z. (2026). Sustainable and Reliable Smart Grids: An Abnormal Condition Diagnosis Method for Low-Voltage Distribution Nodes via Multi-Source Domain Deep Transfer Learning and Cloud-Edge Collaboration. Sustainability, 18(3), 1550. https://doi.org/10.3390/su18031550

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