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This article was financed within the project 9/FPEDAS/2024 “Research on the possibilities
of implementing the measurement of particulate matter in exhaust gases of road vehicles during
emission control in the conditions of the Slovak Republic”.
The article contains an analysis of power generation by a photovoltaic system
with a peak power of 3 MWp and a wind turbine with a power of 3.45 MW. The acquired
time series of generated power was analyzed using traditional and modern analytical
methods. The power generated by these two Renewable Energy Sources was characterized
separately and then by their mix. In this article, the power signature was defined as
the power generated by the photovoltaic system and the wind turbine in the state space
over a period of one month. The state space was extracted from the results of cluster
analysis. The experiment with clustering was carried out into 10 classes. The K-Means
clustering algorithm was used to determine the clusters in a variant without prior labeling
of classes with the method of learning without the participation of the teacher. In this
way, the trajectories of the power generation process from two Renewable Energy Sources
were determined in the 10-state space. Knowing which class each data record belongs to,
the frequencies of staying in each state were determined. The computational algorithm
presented in the article may have great practical application in balancing the power grid
powered by energy produced from renewable sources.
