The Application of Hierarchical Clustering to Power Quality Measurements in an Electrical Power Network with Distributed Generation

This article presents the application of data mining (DM) to long-term power quality (PQ) measurements. The Ward algorithm was selected as the cluster analysis (CA) technique to achieve an automatic division of the PQ measurement data. The measurements were conducted in an electrical power network (...

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Bibliographic Details
Published in:Energies (Basel) 2020-05, Vol.13 (9), p.2407
Main Authors: Jasiński, Michał, Sikorski, Tomasz, Leonowicz, Zbigniew, Borkowski, Klaudiusz, Jasińska, Elżbieta
Format: Article
Language:English
Subjects:
Algorithms
Classification
Cluster analysis
Clustering
Cooperation
Copper industry
Data analysis
Data mining
different working conditions
Distributed generation
Electric power
Electric power distribution
Electricity distribution
Fuzzy sets
Localization
Logic programming
Methods
Mining industry
Order parameters
power quality
Quality
Reconfiguration
ward algorithm
Working conditions
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Summary:This article presents the application of data mining (DM) to long-term power quality (PQ) measurements. The Ward algorithm was selected as the cluster analysis (CA) technique to achieve an automatic division of the PQ measurement data. The measurements were conducted in an electrical power network (EPN) of the mining industry with distributed generation (DG). The obtained results indicate that the application of the Ward algorithm to PQ data assures the division with regards to the work of the distributed generation, and also to other important working conditions (e.g., reconfiguration or high harmonic pollution). The presented analysis is conducted for the area-related approach—all measurement point data are connected at an initial stage. The importance rate was proposed in order to indicate the parameters that have a high impact on the classification of the data. Another element of the article was the reduction of the size of the input database. The reduction of input data by 57% assured the classification with a 95% agreement when compared to the complete database classification.
ISSN:1996-1073
1996-1073
DOI:10.3390/en13092407