ProDER: A Continual Learning Approach for Fault Prediction in Evolving Smart Grids
Abstract
As smart grids evolve to meet growing energy demands and modern operational challenges, the ability to accurately predict faults becomes increasingly critical. However, existing AI-based fault prediction models struggle to ensure reliability in evolving environments where they are required to adapt to new fault types and operational zones. In this paper, we propose a continual learning (CL) framework in the smart grid context to evolve the model together with the environment. We design four realistic evaluation scenarios grounded in class-incremental and domain-incremental learning to emulate evolving grid conditions. We further introduce Prototype-based Dark Experience Replay (ProDER), a unified replay-based approach that integrates prototype-based feature regularization, logit distillation, and a prototype-guided replay memory. ProDER achieves the best performance among tested CL techniques, with only a 0.045 accuracy drop for fault type prediction and 0.015 for fault zone prediction. These results demonstrate the practicality of CL for scalable, real-world fault prediction in smart grids.
Keywords
Cite
@article{arxiv.2511.05420,
title = {ProDER: A Continual Learning Approach for Fault Prediction in Evolving Smart Grids},
author = {Emad Efatinasab and Nahal Azadi and Davide Dalle Pezze and Gian Antonio Susto and Chuadhry Mujeeb Ahmed and Mirco Rampazzo},
journal= {arXiv preprint arXiv:2511.05420},
year = {2025}
}