ProDER: A Continual Learning Approach for Fault Classification and Localization in Evolving Smart Grids

Excerpt

arXiv:2511.05420v3 Announce Type: replace-cross Abstract: Data-driven fault diagnosis models for smart grids are usually trained once on a fixed dataset, whereas in operation new fault types appear and monitoring is extended to new grid zones. Retraining from scratch on all accumulated data is costly, while naively updating the model on new data causes catastrophic forgetting. To address this problem, we formulate fault type classification and fault zone localization as continual learning (CL) p