Abstract
Condition monitoring of power transformers is of vital importance to prevent electricity supply stoppages and reduce power plant maintenance costs. To that end, the use of techniques to evaluate and classify the condition of these devices is highly recommended in order to obtain good quality information for their proper maintenance planning. This article presents and details a general methodology for the creation of methods to evaluate and classify these devices, by means of computational modeling and optimization. The results indicate a higher than 93% accuracy rate compared to that of numerical evaluations and symbolic classifications expected by experts, thus demonstrating the applicability of the proposed methodology, which is found to be superior in comparisons against Computational Intelligence and Statistical Learning methods.
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Acknowledgements
The authors thank the Federal University of Goiás and the Federal Institute of Education, Science, and Technology of Goiás for their practical support, as well as the Goiás State Research Foundation (FAPEG) and the Brazilian Electricity Regulatory Agency (ANEEL) for their financial support of this work.
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Appendices
Appendix A: Physicochemical Evaluation and Classification Method
The results of the optimization of evaluation and classification model with respect to physicochemical parameters (Marques et al. 2017a) are presented in Table 2.
Appendix B: Electrical Evaluation and Classification Method
The results of the optimization of evaluation and classification model with respect to insulation resistance parameters (Marques 2017b) are presented in Table 3.
The results of the optimization of evaluation and classification model with respect to power factor parameters (Marques et al. 2018) are presented in Table 4.
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da Cunha Brito, L., Marques, A.P., de Jesus Ribeiro, C. et al. A General Methodology for Evaluation and Classification of Oil-Immersed Power Transformers: Application to Electrical and Physicochemical Parameters. J Control Autom Electr Syst 30, 832–839 (2019). https://doi.org/10.1007/s40313-019-00487-6
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DOI: https://doi.org/10.1007/s40313-019-00487-6