Abstract
In a modern power grid known also as a Smart Grid (SG) its of paramount importance detecting a fault status both from the electricity operator and consumer feedback. The modern SG systems are equipped with Smart Sensors scattered within the real-world power distribution lines that are able to take a fine-grain picture of the actual power grid status gathering a huge amount of heterogeneous data. The Computational Intelligence paradigm has proven to be a useful approach in pattern recognition and analysis in facing problems related to SG. The present work deals with the challenging task of synthesizing a recognition model that learns from heterogeneous information that relates to environmental and physical grid variables collected by the Smart Sensors on MV feeders in the real-world SG that supplies the entire city of Rome, Italy. The recognition of faults is addressed by a combined approach of a multiple weighted Dissimilarity Measure, designed to cope with mixed data types like numerical data, Time Series and categorical data, and a One-Class Classification technique. For Categorical data the Semantic Distance (SD) is proposed, capable of grasping semantical information from clustered data. The faults model is obtained by a clustering algorithm (k-means) with a suitable initialization procedure capable to estimate the number of clusters k. A suited evolutionary algorithm has been designed to learn from the optimal weights of the Dissimilarity Measure defining a suitable performance measure computed by means of a cross-validation approach. In the present work a crisp classification rule on unseen test patterns is studied together with a soft decision mechanism based on a fuzzy membership function. Moreover a favorable discrimination performance between faults and standard working condition of the (One-Class) classifier will be presented comparing the SD with the well-known Simple Matching (SM) Distance for categorical data.
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Acknowledgments
The authors wish to thank ACEA Distribuzione S.p.A. for providing the faults data and for their useful support during the OCC system design and test phases. Special thanks to Ing. Stefano Liotta, Chief Network Operation Division, and to Ing. Silvio Alessandroni, Chief Electric Power Distribution Remote Control Division.
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De Santis, E., Mascioli, F.M.F., Sadeghian, A., Rizzi, A. (2016). A Dissimilarity Learning Approach by Evolutionary Computation for Faults Recognition in Smart Grids. In: Merelo, J.J., Rosa, A., Cadenas, J.M., Dourado, A., Madani, K., Filipe, J. (eds) Computational Intelligence. IJCCI 2014. Studies in Computational Intelligence, vol 620. Springer, Cham. https://doi.org/10.1007/978-3-319-26393-9_8
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