Combined Correlation and Cluster Analysis for Long-Term Power Quality Data from Virtual Power Plant

Abstract

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.

Microgrid Working Conditions Identification Based on Cluster Analysis—A Case Study From Lambda Microgrid

“…The investigation is based on four Lambda Microgrid measurement points -point of common coupling (PCC), the connection point of the 12 kWh peak 3-phase PV system, the 6.5 kWh battery energy storage system and the University laboratory as a specific load. Therefore, to connect these measurement points, an area-related approach [28] was proposed for long-term data, 18 months with a resolution of 15 minutes of the data. It is worth noting that the CA algorithm considers the input database that consists of data from all measurement points simultaneously.…”