Abstract
This work aims to build generation hourly electric power groups from the data of 50 existing turbines at the Santo Antônio hydroelectric plant located in the state of Rondônia, as well as to rank and analyze their potential through the application of multivariate techniques of Manhattan distance in the turbines combined with the method of hierarchical clustering of average linkage. Thus, the formation of 7 productive groups of electric energy generation at the plant was verified, in which the highest and lowest average, standard deviation, and total generation of groups are groups 1 and 7. And there is also an increasing ordination of these measures from groups 1 to 7, a result that can serve as another tool for monitoring potentials and bottlenecks of the power generation of the plant broken down into groups, and this methodology can be replicated year after year and in other hydroelectric plants in Brazil and around the world.
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Acknowledgments
Thanks go to God first, and then to the Coordination for the Improvement of Higher Education Personnel (CAPES) in Brazil and to the postgraduate program in Experimental and Agronomic Statistics at the State University of São Paulo (USP/ESALQ) campus from Piracicaba-SP and also the University of the State of Mato Grosso (UNEMAT-FACET-Electrical Engineering) campus of Sinop-MT, for all the support to carry out this research. The ideas and opinions expressed herein are those of the authors alone, and endorsement by the authors’ institution is not intended and should not be inferred.
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Ribeiro, J.G., dos Santos Dias, C.T., de Stefano Piedade, S.M., Vale, G.M.d., de Jesus Silva Oliveira, V. (2023). Application of Cluster Analysis to Electricity Generation Data from the Santo Antônio Hydroelectric Plant in the State of Rondônia, Brazil. In: Iano, Y., Saotome, O., Kemper Vásquez, G.L., de Moraes Gomes Rosa, M.T., Arthur, R., Gomes de Oliveira, G. (eds) Proceedings of the 8th Brazilian Technology Symposium (BTSym’22). BTSym 2022. Smart Innovation, Systems and Technologies, vol 353. Springer, Cham. https://doi.org/10.1007/978-3-031-31007-2_6
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