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

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.