Collaborative Multi-Teacher Distillation for Multi-Task Fault Detection in Power Distribution Grid

Under the background of complicated fault detection scenarios and diversified data in the current power distribution grid, it is a significant challenge to deploy high-performance fault detection models for efficient multi-task collaboration processing on lightweight edge intelligent devices. A collaborative multi-teacher distillation for multi-task fault detection in the power distribution grid(CMT-KD) is introduced, which enhances multi-task fault detection by redefining Residual Network (ResNet). We further implement model compression and acceleration by adopting the weight quantization method, while enhancing the performance of the multi-task fault detector via dynamically adapting the knowledge weights from multiple teachers. The proposed method allows for the detection of multiple power distribution grid faults with a single model, significantly reducing the number of model parameters, floating point operations, and training time, while also demonstrating favorable performance in terms of improving accuracy and reducing prediction errors.