QSSI: A NEW SIMILARITY INDEX FOR QUALITATIVE TIME SERIES. APPLICATION TO CLASSIFY VOLTAGE SAGS

Authors:
Fco. I. Gamero;Joaquim Melendez;Joan Colomer
Affiliations:
Control Engineering and Intelligent Systems group—eXiT, Institute of Informatics and Applications, University of Girona, Girona, Catalonia, Spain;Control Engineering and Intelligent Systems group—eXiT, Institute of Informatics and Applications, University of Girona, Girona, Catalonia, Spain;Control Engineering and Intelligent Systems group—eXiT, Institute of Informatics and Applications, University of Girona, Girona, Catalonia, Spain
Venue:
Applied Artificial Intelligence
Year:
2011

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Integration of sequence learning and CBR for complex equipment failure prediction

ICCBR'11 Proceedings of the 19th international conference on Case-Based Reasoning Research and Development

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Abstract

This work is focused on defining and implementing a new similarity criterion for sequences of symbolic representations. The proposed algorithm returns a normalized index related to the degree of matching between sequences of qualitative labels. Performance of this method has been tested in the classification of voltage sags (transient reduction of voltage magnitude) gathered at 25 kV distribution substations. The objective is to assist monitoring systems in locating the origin of such disturbances in the transmission (HV) or distribution (MV) system. The promising classification accuracy achieved when this method was used with test data suggests that the presented algorithm could be applied satisfactorily and confirms its utility in classification approaches.