Global Power Quality Index application in Virtual Power Plant

Author(s):  
Michal Jasinski ◽  
Tomasz Sikorski ◽  
Dominika Kaczorowska ◽  
Pawel Kostyla ◽  
Zbigniew Leonowicz ◽  
...  
Electronics ◽  
2021 ◽  
Vol 10 (6) ◽  
pp. 641
Author(s):  
Michał Jasiński

Analysis of the connection between different units that operate in the same area assures always interesting results. During this investigation, the concerned area was a virtual power plant (VPP) that operates in Poland. The main distributed resources included in the VPP are a 1.25 MW hydropower plant and an associated 0.5 MW energy storage system. The mentioned VPP was a source of synchronic, long-term, multipoint power quality (PQ) data. Then, for five related measurement points, the conclusion about the relation in point of PQ was performed using correlation analysis, the global index approach, and cluster analysis. Global indicators were applied in place of PQ parameters to reduce the amount of analyzed data and to check the correlation between phase values. For such a big dataset, the occurrence of outliers is certain, and outliers may affect the correlation results. Thus, to find and exclude them, cluster analysis (k-means algorithm, Chebyshev distance) was applied. Finally, the correlation between PQ global indicators of different measurement points was performed. It assured general information about VPP units’ relation in point of PQ. Under the investigation, both Pearson’s and Spearman’s rank correlation coefficients were considered.


Energies ◽  
2021 ◽  
Vol 14 (18) ◽  
pp. 5902
Author(s):  
Fachrizal Aksan ◽  
Michał Jasiński ◽  
Tomasz Sikorski ◽  
Dominika Kaczorowska ◽  
Jacek Rezmer ◽  
...  

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.


Author(s):  
C.S. Ioakimidis ◽  
L. Oliveira ◽  
K.N. Genikomsakis ◽  
P. Ryserski

Author(s):  
Jianlin Yang ◽  
Jingbang Li ◽  
Mingxing Guo ◽  
Yichao Huang ◽  
Aili Pang ◽  
...  

2021 ◽  
Vol 1966 (1) ◽  
pp. 012053
Author(s):  
Yong Cui ◽  
Fei Xiao ◽  
Jun Gu ◽  
Weihong Wang ◽  
Liang Cao ◽  
...  

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 206820-206834
Author(s):  
Jae-Won Chang ◽  
Hee Seung Moon ◽  
Seung-Il Moon ◽  
Yong Tae Yoon ◽  
Mark B. Glick ◽  
...  

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