Paper Info
Title
Multilabel learning for the online transient stability assessment of electric power systems
Abstract
Dynamic security assessment of a large power system operating over a wide range of conditions requires an intensive computation for evaluating the system's transient stability against a large number of contingencies. In this study, we investigate the application of multilabel learning for improving training and prediction time, along with the prediction accuracy, of neural networks for online transient stability assessment of power systems. We introduce a new multilabel learning method, which uses a contingency clustering step to learn similar contingencies together in the same multilabel multilayer perceptron. Experimental results on two different power systems demonstrate improved accuracy, as well as significant reduction in both training and testing time.
Year
DOI
Venue
2018
10.3906/elk-1805-151
TURKISH JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCES
Keywords
DocType
Volume
Dynamic security assessment,transient stability assessment,multilabel learning,neural networks
Journal
26
Issue
ISSN
Citations 
5.0
1300-0632
0
PageRank 
References 
Authors
0.34
0
3
Name
Order
Citations
PageRank
Peyman Beyranvand100.34
Veysel Murat Istemihan Genc211.03
Zehra Cataltepe316616.39