Evaluación de la calidad de la energía eléctrica suministrada por una planta solar fotovoltaica conectada a la red haciendo uso de algoritmos de aprendizaje automático y de minería de datos

César Aristóteles Yajure Ramírez

doi:10.14482/inde.43.01.489.326

Authors

César Aristóteles Yajure Ramírez Universidad Central de Venezuela (Venezuela) https://orcid.org/0000-0002-3813-7606

DOI:

https://doi.org/10.14482/inde.43.01.489.326

Keywords:

Electrical frequency, harmonic distortion, importance level, power factor, subgroups

Abstract

The presence of photovoltaic solar plants to produce electricity implies the reduction of the use of fossil fuels, and the reduction of polluting emissions. The availability of solar energy depends on weather conditions, so the parameters of the electrical energy to be delivered could be affected. The objective of this research is to present a methodology based on data science for the evaluation of the energy quality of photovoltaic solar plants connected to the grid, considering current standards. It is applied to a 260 kWp plant of the National Institute of Standards and Technology of the United States. The parameters used are total harmonic distortion, voltage fluctuations and unbalance, electrical frequency, and power factor. Almost 100% of the records comply with the limits established by the standards for the parameters, except for power factor, with 63.56%. From the power factor classification model, it was obtained that apparent and active power, and frequency, are the most important variables. From the subgroup discovery algorithm, it was obtained that solar irradiance appears in 40% of the subgroups, and frequency in 50%.

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