Sistema de Hardware/Software para Estimativas de Perdas de Geração em Usinas Solares Fotovoltaicas
Resumo
A sujidade nos módulos fotovoltaicos tem um impacto direto na geração de energia solar. Uma forma de minimizar esse impacto é identificar as perdas na geração de energia devido à sujidade e acionar procedimentos de limpeza. Este trabalho apresenta um sistema de hardware/software composto por uma plataforma eletrônica para aquisição de variáveis ambientais e um modelo não-linear de efeitos mistos (NLME) para estimar a geração de energia das usinas solares. Esse sistema possibilita que usuários possam analisar as perdas de geração em função da sujidade em usinas solares fotovoltaicas. O modelo foi avaliado usando um conjunto de dados real composto de medições de energia gerada e variáveis ambientais de Outubro/2019 a Abril/2020. O modelo ajustado apresentou um erro quadrático médio de 0,0044 e os resultados mostraram perdas diárias de energia abaixo de 1,1%.
Palavras-chave:
Sujidade, Energia Solar Fotovoltaica, Deposição de Particulados, Estação de Sujidade, Preditor Log-logístico
Referências
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Catelani, M., Ciani, L., Cristaldi, L., Faifer, M., Lazzaroni, M., and Rossi, M. (2012).Characterization of photovoltaic panels: The effects of dust. InIEEE InternationalEnergy Conference and Exhibition, pages 45–50.
Coello, M. and Boyle, L. (2019). Simple model for predicting time series soiling ofphotovoltaic panels.IEEE Journal of Photovoltaics, 9(5):1382–1387.
El-Shobokshy, M. and Hussein, F. (1993). Effect of dust with different physical propertieson the performance of photovoltaic cells.Solar energy, 6(51):505–511.
Gostein, M., Caron, J. R., and Littmann, B. (2014). Measuring soiling losses at utility-scale pv power plants. InIEEE Photovoltaic Specialist Conference (PVSC), pages0885–0890.
Hammad, B., Al-Abed, M., Al-Ghandoor, A., Al-Sardeah, A., and Al-Bashir, A. (2018).Modeling and analysis of dust and temperature effects on photovoltaic systems’ perfor-mance and optimal cleaning frequency: Jordan case study.Renewable and SustainableEnergy Reviews, 82:2218–2234.
Hupa, L., Bergman, R., Fr ̈oberg, L., Vane-Tempest, S., Hupa, M., Kronberg, T., Pesonen-Leinonen, E., and Sj ̈oberg, A.-M. (2005). Chemical resistance and cleanability ofglazed surfaces.Surface science, 584(1):113–118.
Javed, W., Guo, B., and Figgis, B. (2017). Modeling of photovoltaic soiling loss as afunction of environmental variables.Solar Energy, 157:397–407.
Mani, M. and Pillai, R. (2010). Impact of dust on solar photovoltaic (pv) performance:Research status, challenges and recommendations.Renewable and Sustainable EnergyReviews, 14:3124–3131.
Mehta, S., Azad, A. P., Chemmengath, S. A., Raykar, V., and Kalyanaraman, S. (2018).DeepSolarEye: Power Loss Prediction and Weakly Supervised Soiling Localizationvia Fully Convolutional Networks for Solar Panels.Proceedings of the 2018 IEEEWinter Conference on Applications of Computer Vision (WACV), pages 333–342.
Pavan, A. M., Mellit, A., De Pieri, D., and Kalogirou, S. A. (2013). A comparisonbetween bnn and regression polynomial methods for the evaluation of the effect ofsoiling in large scale photovoltaic plants.Applied energy, 108:392–401.
Ribeiro, K., Santos, R., Saraiva, E., and Rajagopal, R. (2021). A statistical methodo-logy to estimate soiling losses on photovoltaic solar plants.Journal of Solar EnergyEngineering, 143(6):064501.
Schwarz, G. (1978). Estimating the dimension of a model.Ann. Statist., 6(2):461–464.
Zorrilla-Casanova, J., Philiougine, M., Carretero, J., Bernaola, P., Carpena, P., Mora-ópez, L., and Sidrach-de Cardona, M. (2011). Analysis of dust losses in photovoltaicmodules. InWorld Renewable Energy Congress, pages 8–13.
Catelani, M., Ciani, L., Cristaldi, L., Faifer, M., Lazzaroni, M., and Rossi, M. (2012).Characterization of photovoltaic panels: The effects of dust. InIEEE InternationalEnergy Conference and Exhibition, pages 45–50.
Coello, M. and Boyle, L. (2019). Simple model for predicting time series soiling ofphotovoltaic panels.IEEE Journal of Photovoltaics, 9(5):1382–1387.
El-Shobokshy, M. and Hussein, F. (1993). Effect of dust with different physical propertieson the performance of photovoltaic cells.Solar energy, 6(51):505–511.
Gostein, M., Caron, J. R., and Littmann, B. (2014). Measuring soiling losses at utility-scale pv power plants. InIEEE Photovoltaic Specialist Conference (PVSC), pages0885–0890.
Hammad, B., Al-Abed, M., Al-Ghandoor, A., Al-Sardeah, A., and Al-Bashir, A. (2018).Modeling and analysis of dust and temperature effects on photovoltaic systems’ perfor-mance and optimal cleaning frequency: Jordan case study.Renewable and SustainableEnergy Reviews, 82:2218–2234.
Hupa, L., Bergman, R., Fr ̈oberg, L., Vane-Tempest, S., Hupa, M., Kronberg, T., Pesonen-Leinonen, E., and Sj ̈oberg, A.-M. (2005). Chemical resistance and cleanability ofglazed surfaces.Surface science, 584(1):113–118.
Javed, W., Guo, B., and Figgis, B. (2017). Modeling of photovoltaic soiling loss as afunction of environmental variables.Solar Energy, 157:397–407.
Mani, M. and Pillai, R. (2010). Impact of dust on solar photovoltaic (pv) performance:Research status, challenges and recommendations.Renewable and Sustainable EnergyReviews, 14:3124–3131.
Mehta, S., Azad, A. P., Chemmengath, S. A., Raykar, V., and Kalyanaraman, S. (2018).DeepSolarEye: Power Loss Prediction and Weakly Supervised Soiling Localizationvia Fully Convolutional Networks for Solar Panels.Proceedings of the 2018 IEEEWinter Conference on Applications of Computer Vision (WACV), pages 333–342.
Pavan, A. M., Mellit, A., De Pieri, D., and Kalogirou, S. A. (2013). A comparisonbetween bnn and regression polynomial methods for the evaluation of the effect ofsoiling in large scale photovoltaic plants.Applied energy, 108:392–401.
Ribeiro, K., Santos, R., Saraiva, E., and Rajagopal, R. (2021). A statistical methodo-logy to estimate soiling losses on photovoltaic solar plants.Journal of Solar EnergyEngineering, 143(6):064501.
Schwarz, G. (1978). Estimating the dimension of a model.Ann. Statist., 6(2):461–464.
Zorrilla-Casanova, J., Philiougine, M., Carretero, J., Bernaola, P., Carpena, P., Mora-ópez, L., and Sidrach-de Cardona, M. (2011). Analysis of dust losses in photovoltaicmodules. InWorld Renewable Energy Congress, pages 8–13.
Publicado
18/07/2021
Como Citar
SOUZA, Guilherme G.; SANTOS, Ricardo R.; SARAIVA, Erlandson F..
Sistema de Hardware/Software para Estimativas de Perdas de Geração em Usinas Solares Fotovoltaicas. In: SEMINÁRIO INTEGRADO DE SOFTWARE E HARDWARE (SEMISH), 48. , 2021, Evento Online.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2021
.
p. 214-224.
ISSN 2595-6205.
DOI: https://doi.org/10.5753/semish.2021.15825.
