Comparison of multilayer perceptron neural network architecture in photovoltaic plants fault classification
Comparação de arquitetura de redes neurais perceptron de multicamadas para classificação de faltas em plantas fotovoltaicas
Palavras-chave:
Multilayer Perceptron, Artificial Neural Network, Photovoltaic, Fault ClassificationResumo
The goal of this study is to assess the application of Multilayer Perceptron Artificial Neural Networks in fault classification within photovoltaic panels, focusing on key characteristics such as the number of neurons and layers, activation functions, training techniques, and the resulting accuracy. The study employs a comparative analysis approach, examining various characteristics and hyperparameters applied to Multilayer Perceptron Artificial Neural Networks for fault classification in photovoltaic panels. The research methodology involves reviewing publications from the past decade to gather data on these characteristics and their impact on fault analysis in photovoltaic generation systems. This study contributes to the originality of the field by providing a comprehensive comparison of various parameters and techniques used in Multilayer Perceptron Artificial Neural Networks for fault classification in photovoltaic panels. The findings offer valuable insights for researchers and practitioners in the renewable energy sector, aiding in the development of more efficient and reliable fault diagnosis systems for photovoltaic generation.
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Referências
BASNET, B.; CHUN, H.; BANG, J. An Intelligent Fault Detection Model for Fault Detection in Photovoltaic Systems. Journal of Sensors, v. 2020, p. 1–11, 9 jun. 2020.
BHARATH, K. V. S.; HAQUE, A.; KHAN, M. A. Condition Monitoring of Photovoltaic Systems Using Machine Leaming Techniques. 2018 2nd IEEE International Conference on Power Electronics, Intelligent Control and Energy Systems (ICPEICES). Anais... Em: 2018 2ND IEEE INTERNATIONAL CONFERENCE ON POWER ELECTRONICS, INTELLIGENT CONTROL AND ENERGY SYSTEMS (ICPEICES). Delhi, India: IEEE, out. 2018. Disponível em: <https://ieeexplore.ieee.org/document/8897413/>. Acesso em: 27 maio. 2023
CAO, X. et al. Maloperation prevention for overcurrent protection in photovoltaic integration system under weather intermittency. Electric Power Systems Research, v. 223, p. 109566, out. 2023.
CHINE, W. et al. A novel fault diagnosis technique for photovoltaic systems based on artificial neural networks. Renewable Energy, v. 90, p. 501–512, maio 2016.
CHOUAY, Y.; OUASSAID, M. An intelligent method for fault diagnosis in photovoltaic systems. 2017 International Conference on Electrical and Information Technologies (ICEIT). Anais... Em: 2017 INTERNATIONAL CONFERENCE ON ELECTRICAL AND INFORMATION TECHNOLOGIES (ICEIT). Rabat, Morocco: IEEE, nov. 2017. Disponível em: <http://ieeexplore.ieee.org/document/8255225/>. Acesso em: 26 maio. 2023
DA COSTA, C. H. et al. A Comparison of Machine Learning-Based Methods for Fault Classification in Photovoltaic Systems. 2019 IEEE PES Innovative Smart Grid Technologies Conference - Latin America (ISGT Latin America). Anais... Em: 2019 IEEE PES INNOVATIVE SMART GRID TECHNOLOGIES CONFERENCE - LATIN AMERICA (ISGT LATIN AMERICA). Gramado, Brazil: IEEE, set. 2019. Disponível em: <https://ieeexplore.ieee.org/document/8895279/>. Acesso em: 26 maio. 2023
de Oliveira, R. S., de Oliveira, M. J. L., Nascimento, E. G. S., Sampaio, R., Nascimento Filho, A. S., & Saba, H. (2023). Renewable energy generation technologies for decarbonizing urban vertical buildings: A path towards net zero. Sustainability, 15(17), 13030.
DE VILLIERS, J.; BARNARD, E. Backpropagation neural nets with one and two hidden layers. IEEE Transactions on Neural Networks, v. 4, n. 1, p. 136–141, jan. 1993.
DJALAB, A. A. et al. Robust Method for Diagnosis and Detection of Faults in Photovoltaic Systems Using Artificial Neural Networks. Periodica Polytechnica Electrical Engineering and Computer Science, 12 mar. 2020.
Filho, A. S. N., Borges, T., Salvador, H., Ferreira, P., & Saba, H. (2022). Renewable sources to promote well-being in poor regions of Brazil. Frontiers in Physics, 10, 1048721.
GÉRON, A. Mãos a Obra: Aprendizado de Máquina com Scikit-Learn, Keras, e TensorFlow. 2. ed. [s.l.] O’Reilly Media, 2019.
HAYKIN, S. Neural Networks and Learning Machines. 3. ed. New Jersey: Pearson Prentice Hall, 2009.
IRENA. Global landscape of renewable energy finance 2023. 2023.
LAZZARETTI, A. E. et al. A Monitoring System for Online Fault Detection and Classification in Photovoltaic Plants. Sensors, v. 20, n. 17, p. 4688, 20 ago. 2020.
MAIOROV, V.; PINKUS, A. Lower bounds for approximation by MLP neural networks. Neurocomputing, v. 25, n. 1–3, p. 81–91, abr. 1999.
MCCULLOCH, W. S.; PITTS, W. A logical calculus of the ideas immanent in nervous activity. p. 19, 1943.
Murari, T. B., Filho, A. S. N., Moret, M. A., Pitombo, S., & Santos, A. A. (2020). Self-affine analysis of ENSO in solar radiation. Energies, 13(18), 4816.
Nascimento Filho, A. S., Pereira, E. J. A. L., Ferreira, P., Murari, T. B., & Moret, M. A. (2018). Cross-correlation analysis on Brazilian gasoline retail market. Physica A: Statistical Mechanics and its Applications, 508, 550-557.
Nascimento Filho, A. S., dos Santos, R. G., Calmon, J. G. A., Lobato, P. A., Moret, M. A., Murari, T. B., & Saba, H. (2022). Induction of a consumption pattern for ethanol and gasoline in Brazil. Sustainability, 14(15), 9047.
Nascimento Filho, A. S., Saba, H., dos Santos, R. G., Calmon, J. G. A., Araújo, M. L., Jorge, E. M., & Murari, T. B. (2021). Analysis of hydrous ethanol price competitiveness after the implementation of the fossil fuel import price parity policy in Brazil. Sustainability, 13(17), 9899.
Nascimento Filho, A. S., de Souza, J. W. G., Pereira, A. R. B., Santos, A. A. B., da Cunha Lima, I. C., da Cunha Lima, A. T., & Moret, M. A. (2017). Comparative analysis on turbulent regime: A self-affinity study in fluid flow by using OpenFoam CFD. Physica A: Statistical Mechanics and its Applications, 474, 260-266.
ONS. Relatório Anual 2022. Rio de Janeiro: ONS - Operador Nacional do Sistema, 2022.
RAO, S.; SPANIAS, A.; TEPEDELENLIOGLU, C. Solar Array Fault Detection using Neural Networks. 2019 IEEE International Conference on Industrial Cyber Physical Systems (ICPS). Anais... Em: 2019 IEEE INTERNATIONAL CONFERENCE ON INDUSTRIAL CYBER PHYSICAL SYSTEMS (ICPS). Taipei, Taiwan: IEEE, maio 2019. Disponível em: <https://ieeexplore.ieee.org/document/8780208/>. Acesso em: 26 maio. 2023
ROSENBLATT, F. The perceptron: A probabilistic model for information storage and organization in the brain. Psychological Review, v. 65, n. 6, p. 386–408, 1958.
RUMELHART, D.; HINTON, G.; WILLIAMS, R. Learning internal representationss by error propagation. La Jolla, California: University of California, set. 1985.
SABRI, N.; TLEMÇANI, A.; CHOUDER, A. Monitoring Tool for Stand-Alone Photovoltaic System Using Artificial Neural Network. Em: HATTI, M. (Ed.). Renewable Energy for Smart and Sustainable Cities. Lecture Notes in Networks and Systems. Cham: Springer International Publishing, 2019. v. 62p. 114–121.
Silva, L. C., Wilfinger, M. M., Murari, T. B., Nascimento Filho, A. S., Moret, M. A., & Santos, A. A. (2021). Experimental evaluation of thermal radiation and soot concentration rates for syngas flames in lean condition and oxygen enhanced combustion. Energy Reports, 7, 4139-4145.
VIEIRA, R. G. Aplicação de Técnicas de Inteligência Artificial para Identificação de Faltas em Módulos Fotovoltaicos. Natal, RN: Universidade Federal do Rio Grande do Norte, dez. 2021.
W. S. MCCULLOCH e W. PITTS, “A logical calculus of the ideas immanent in nervous activity”, p. 19, 1943.
