@inproceedings{sun2021intentional,

title = {Intentional Islanding of Power Systems Through Self-Embedding Learning},

author = {Zhonglin Sun and Yannis Spyridis and Achilleas Sessis and Georgios Efstathopoulos and Elisavet Grigoriou and Thomas Lagkas and Panagiotis Sarigiannidis},

url = {https://ieeexplore.ieee.org/abstract/document/9682069},

doi = {10.1109/GCWkshps52748.2021.9682069},

year  = {2021},

date = {2021-01-01},

urldate = {2021-01-01},

booktitle = {2021 IEEE Globecom Workshops (GC Wkshps)},

pages = {1--6},

organization = {IEEE},

abstract = {Intentional islanding is a procedure to divide the electrical grid into several parts to guarantee the stability of a system in the case of failure. This study provides an unsupervised deep neural network to deal with the issue of intentional islanding. We propose to use a self-learning neural network to improve the generalisation performance of the islanding task. In addition, we use a merging technology to assign isolated buses to their neighbour's label. Experiments are carried out on several grid cases to illustrate the effect of our solution.},

keywords = {Deep Learning, Intentional islanding, Smart Grid},

pubstate = {published},

tppubtype = {inproceedings}

}