Clustering Methods for Power Quality Measurements in Virtual Power Plant
In this article, a case study is presented on applying cluster analysis techniques to evaluate the level of power quality (PQ) parameters of a virtual power plant. The conducted research concerns the application of the K-means algorithm in comparison with the agglomerative algorithm for PQ data, whi...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-09-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/14/18/5902 |
| _version_ | 1797519423186141184 |
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| author | Fachrizal Aksan Michał Jasiński Tomasz Sikorski Dominika Kaczorowska Jacek Rezmer Vishnu Suresh Zbigniew Leonowicz Paweł Kostyła Jarosław Szymańda Przemysław Janik |
| author_facet | Fachrizal Aksan Michał Jasiński Tomasz Sikorski Dominika Kaczorowska Jacek Rezmer Vishnu Suresh Zbigniew Leonowicz Paweł Kostyła Jarosław Szymańda Przemysław Janik |
| author_sort | Fachrizal Aksan |
| collection | DOAJ |
| description | In this article, a case study is presented on applying cluster analysis techniques to evaluate the level of power quality (PQ) parameters of a virtual power plant. The conducted research concerns the application of the K-means algorithm in comparison with the agglomerative algorithm for PQ data, which have different sizes of features. The object of the study deals with the standardized datasets containing classical PQ parameters from two sub-studies. Moreover, the optimal number of clusters for both algorithms is discussed using the elbow method and a dendrogram. The experimental results show that the dendrogram method requires a long processing time but gives a consistent result of the optimal number of clusters when there are additional parameters. In comparison, the elbow method is easy to compute but gives inconsistent results. According to the Calinski–Harabasz index and silhouette coefficient, the K-means algorithm performs better than the agglomerative algorithm in clustering the data points when there are no additional features of PQ data. Finally, based on the standard EN 50160, the result of the cluster analysis from both algorithms shows that all PQ parameters for each cluster in the two study objects are still below the limit level and work under normal operating conditions. |
| first_indexed | 2024-03-10T07:42:35Z |
| format | Article |
| id | doaj.art-6ad3d0ab498d4d3e8e350b89097777af |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-10T07:42:35Z |
| publishDate | 2021-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-6ad3d0ab498d4d3e8e350b89097777af2023-11-22T12:54:48ZengMDPI AGEnergies1996-10732021-09-011418590210.3390/en14185902Clustering Methods for Power Quality Measurements in Virtual Power PlantFachrizal Aksan0Michał Jasiński1Tomasz Sikorski2Dominika Kaczorowska3Jacek Rezmer4Vishnu Suresh5Zbigniew Leonowicz6Paweł Kostyła7Jarosław Szymańda8Przemysław Janik9Faculty of Electrical Engineering, Wroclaw University of Science and Technology, 50-370 Wroclaw, PolandFaculty of Electrical Engineering, Wroclaw University of Science and Technology, 50-370 Wroclaw, PolandFaculty of Electrical Engineering, Wroclaw University of Science and Technology, 50-370 Wroclaw, PolandFaculty of Electrical Engineering, Wroclaw University of Science and Technology, 50-370 Wroclaw, PolandFaculty of Electrical Engineering, Wroclaw University of Science and Technology, 50-370 Wroclaw, PolandFaculty of Electrical Engineering, Wroclaw University of Science and Technology, 50-370 Wroclaw, PolandFaculty of Electrical Engineering, Wroclaw University of Science and Technology, 50-370 Wroclaw, PolandFaculty of Electrical Engineering, Wroclaw University of Science and Technology, 50-370 Wroclaw, PolandFaculty of Electrical Engineering, Wroclaw University of Science and Technology, 50-370 Wroclaw, PolandFaculty of Electrical Engineering, Wroclaw University of Science and Technology, 50-370 Wroclaw, PolandIn this article, a case study is presented on applying cluster analysis techniques to evaluate the level of power quality (PQ) parameters of a virtual power plant. The conducted research concerns the application of the K-means algorithm in comparison with the agglomerative algorithm for PQ data, which have different sizes of features. The object of the study deals with the standardized datasets containing classical PQ parameters from two sub-studies. Moreover, the optimal number of clusters for both algorithms is discussed using the elbow method and a dendrogram. The experimental results show that the dendrogram method requires a long processing time but gives a consistent result of the optimal number of clusters when there are additional parameters. In comparison, the elbow method is easy to compute but gives inconsistent results. According to the Calinski–Harabasz index and silhouette coefficient, the K-means algorithm performs better than the agglomerative algorithm in clustering the data points when there are no additional features of PQ data. Finally, based on the standard EN 50160, the result of the cluster analysis from both algorithms shows that all PQ parameters for each cluster in the two study objects are still below the limit level and work under normal operating conditions.https://www.mdpi.com/1996-1073/14/18/5902power qualitycluster analysisK-meansagglomerativevirtual power plant |
| spellingShingle | Fachrizal Aksan Michał Jasiński Tomasz Sikorski Dominika Kaczorowska Jacek Rezmer Vishnu Suresh Zbigniew Leonowicz Paweł Kostyła Jarosław Szymańda Przemysław Janik Clustering Methods for Power Quality Measurements in Virtual Power Plant Energies power quality cluster analysis K-means agglomerative virtual power plant |
| title | Clustering Methods for Power Quality Measurements in Virtual Power Plant |
| title_full | Clustering Methods for Power Quality Measurements in Virtual Power Plant |
| title_fullStr | Clustering Methods for Power Quality Measurements in Virtual Power Plant |
| title_full_unstemmed | Clustering Methods for Power Quality Measurements in Virtual Power Plant |
| title_short | Clustering Methods for Power Quality Measurements in Virtual Power Plant |
| title_sort | clustering methods for power quality measurements in virtual power plant |
| topic | power quality cluster analysis K-means agglomerative virtual power plant |
| url | https://www.mdpi.com/1996-1073/14/18/5902 |
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