References
- A. D. Ashkezari, et al., “Multivariate analyses for correlations among different transformer oil parameters to determine transformer health index.” IEEE Power and Energy Society General Meeting, 2012.
- D. L. P. Feil, “Substituição de Transformadores de Potência em Subestações de Energia: Uma Estratégia Global,” thesis, Universidade Federal de Santa Maria (UFSM), Santa Maria, Brazil, 2019.
- A. N. Jahromi, et al., “An aprroach to power transformer asset management using health index,” IEEE Electr. Insul. Mag., vol. 25, no. 2, pp. 20–34, 2009. DOI: 10.1109/MEI.2009.4802595.
- R. Vilaithong, S. E. Tenbohlen and T. Stirl, “Neural network for transformer top-oil temperature prediction.” Symposium on High Voltage Engineering. Ljubljana, Slovenia, 2007.
- ABNT. NBR 5356-2: Aquecimento. Associação Brasileira de Normas Técnicas, 2007.
- IEC. “Std 60076-2: Temperature rise for liquid-immersed transformers,” International Electrotechnical Comission, 2011.
- ABNT. “NBR 5356-7: Guia de carregamento para transformadores imersos em líquido isolante.” Associação Brasileira de Normas Técnicas, 2017.
- IEC. “Std 60076-7: “Loading guide for mineral-oil-immersed power transformers.” International Electrotechnical Comission, 2018.
- F. Pereira, et al., “Nonlinear autoregressive neural network models for prediction of transformer oil-dissolved gas concentrations,” Energies., vol. 11, no. 7, pp. 1691, 2018. DOI: 10.3390/en11071691.
- M. T. Hagan and H. B. Demuth, Neural Network Design, 2nd ed., Martin Hagan, Ed. Stillwater, Oklahoma, 2016,
- Z. Moravej, D. N. Vishwakarma and S. P. Singh, “Protection and conditions monitoring of power transformer using ANN,” Electric Power Component Syst., vol. 30, no. 3, pp. 217–231, 2002. DOI: 10.1080/153250002753598447.
- R. A. Khalil, “Comparison of four neural network learning methods based on genetic algorithm for non-linear dynamic systems identification,” Al-Rafidain Eng., vol. 20, pp. 122–132, 2012.
- L. C. M. de Andrade, et al., “Very short-term load forecasting based on NARX recurrent neural networks.” Power & Energy Society General Meeting, Washington, DC, 2014.
- X. Su, et al., “Application of Elman neural network in top-oil temperature prediction of transformer.” ICHVE 2018. Athens, Greece, 2018.
- B. G. Wei, et al., “A method of optimized neural network by LM algorithm to transformer winding hot-spot temperature forecasting.” NEFES 2017. Kunming, China, 2017.
- H. Huang, et al., “Transformer top-oil modeling based on Kernel-based extreme learning machine.” ICEEA 2016. Kuala Lumpur, Malaysia, 2016.
- Q. He, J. Si and D. J. Tylavsky, “Prediction of top-oil temperature for transformers using neural networks,” IEEE Trans. Power Delivery, vol. 15, no. 4, pp. 1205–1211, 2000. DOI: 10.1109/61.891504.
- Kaminski, A. M, et al., “Emprego de Rede Neural Artificial para Predição de Temperatura de Topo de Óleo em Transformador de Potência.” SBSE 2020. Santo André, Brazil, 2020.
- S. Haykin, Neural Networks: A Comprehensive Foundation, 2nd ed. Upper Saddle River, NJ: Prentice Hall, 1999.
- M. M. Oliveira, et al., “Power transformers assessment applying health index and apparent age methods,” 2020 IEEE PES Transmission & Distribution Conference and Exhibition – Latin America (T&D LA), 2020. DOI: 10.1109/TDLA47668.2020.9326167.