Graph-Based Multi-Task Learning For Fault Detection In Smart Grid
Document Type
Conference Proceeding
Publication Date
2023
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Source Publication
IEEE 33rd International Workshop on Machine Learning for Signal Processing (MLSP)
Source ISSN
9798350324112
Original Item ID
DOI: 10.1109/MLSP55844.2023.10285865
Abstract
Timely detection of electrical faults is of paramount importance for efficient operation of the smart grid. To better equip the power grid operators to prevent grid-wide cascading failures, the detection of fault occurrence and its type must be accompanied by accurately locating the fault. In this work, we propose a multi-task learning architecture that encodes the graph structure of the distribution network through a shared graph neural network (GNN) to both classify and detect faults and their locations simultaneously. Deploying GNNs allows for representation learning of the grid structure which can later be used to optimize grid operation. The proposed model has been tested on the IEEE-123 distribution system. Numerical tests verify that the proposed algorithm outperforms existing approaches.
Recommended Citation
Chanda, Dibaloke and Yahyasoltani, Nasim, "Graph-Based Multi-Task Learning For Fault Detection In Smart Grid" (2023). Computer Science Faculty Research and Publications. 105.
https://epublications.marquette.edu/comp_fac/105
Comments
Published as part of the proceedings of the 33rd IEEE International Workshop on Machine Learning for Signal Processing (MLSP). DOI.