Complementary Analysis for DGA Based on Duval Methods and Furan Compounds Using Artificial Neural Networks

Ancuța-Mihaela Aciu, Claudiu-Ionel Nicola, Marcel Nicola and Maria-Cristina Nițu
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Ancuța-Mihaela Aciu: Research and Development Department, National Institute for Research, Development and Testing in Electrical Engineering-ICMET Craiova, 200746 Craiova, Romania
Claudiu-Ionel Nicola: Research and Development Department, National Institute for Research, Development and Testing in Electrical Engineering-ICMET Craiova, 200746 Craiova, Romania
Marcel Nicola: Research and Development Department, National Institute for Research, Development and Testing in Electrical Engineering-ICMET Craiova, 200746 Craiova, Romania
Maria-Cristina Nițu: Research and Development Department, National Institute for Research, Development and Testing in Electrical Engineering-ICMET Craiova, 200746 Craiova, Romania

Energies, 2021, vol. 14, issue 3, 1-22

Abstract: Power transformers play an important role in electrical systems; being considered the core of electric power transmissions and distribution networks, the owners and users of these assets are increasingly concerned with adopting reliable, automated, and non-invasive techniques to monitor and diagnose their operating conditions. Thus, monitoring the conditions of power transformers has evolved, in the sense that a complete characterization of the conditions of oil–paper insulation can be achieved through dissolved gas analysis (DGA) and furan compounds analysis, since these analyses provide a lot of information about the phenomena that occur in power transformers. The Duval triangles and pentagons methods can be used with a high percentage of correct predictions compared to the known classical methods (key gases, International Electrotechnical Commission (IEC), Rogers, Doernenburg ratios), because, in addition to the six types of basic faults, they also identify four sub-types of thermal faults that provide important additional information for the appropriate corrective actions to be applied to the transformers. A new approach is presented based on the complementarity between the analysis of the gases dissolved in the transformer oil and the analysis of furan compounds, for the identification of the different faults, especially when there are multiple faults, by extending the diagnosis of the operating conditions of the power transformers, in terms of paper degradation. The implemented software system based on artificial neural networks was tested and validated in practice, with good results.

Keywords: power transformer; insulation; dissolved gas analysis; furan compounds; radial basis function neural network; feed forward neural network (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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