To address the issues of incomplete data caused by inconsistent sampling intervals among condition assessment indicators of protection devices

as well as the difficulty of deploying deep learning models on edge devices with limited computational resources

a condition assessment method of relay protection devices based on edge intelligence and knowledge distillation is proposed. First

a relay protection edge intelligent architecture composed of cloud platform

edge computing layer

and physical entity layer is constructed to provide support for distributed training and on-site deployment of the model. Second

an assessment indicator set for protection devices is constructed

and the Raindrop graph neural network model is employed to analyze and learn indicators with irregular sampling intervals

thereby establishing a directed graph that characterizes the relationships among the indicator data. Finally

the Raindrop model is selected as the teacher model and the TabNet model as the student model to construct a knowledge distillation framework. The soft-label knowledge distilled by the teacher model is used to guide the learning model training

enabling lightweight deployment of the evaluation method on edge devices. Case study results show that the accuracies of the proposed method on the training set and the test set are 98.98% and 98.15%

respectively. Moreover

it has low deployment complexity and computational requirements

enabling accurate local condition analysis and assessment of the operational status of relay protection devices on edge devices.