Complementary Analysis for DGA Based on Duval Methods and Furan Compounds Using Artificial Neural Networks
| Autor: | Claudiu-Ionel Nicola, Maria-Cristina Nițu, Marcel Nicola, Ancuța-Mihaela Aciu |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Control and Optimization
Computer science Transformer oil 020209 energy Dissolved gas analysis power transformer Energy Engineering and Power Technology 02 engineering and technology lcsh:Technology law.invention law Thermal 0202 electrical engineering electronic engineering information engineering feed forward neural network Electrical and Electronic Engineering dissolved gas analysis Transformer Engineering (miscellaneous) insulation Artificial neural network lcsh:T Renewable Energy Sustainability and the Environment 020208 electrical & electronic engineering furan compounds Reliability engineering Identification (information) Feedforward neural network radial basis function neural network Electric power Energy (miscellaneous) |
| Zdroj: | Energies; Volume 14; Issue 3; Pages: 588 Energies, Vol 14, Iss 588, p 588 (2021) |
| ISSN: | 1996-1073 |
| DOI: | 10.3390/en14030588 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|
| Nepřihlášeným uživatelům se plný text nezobrazuje | K zobrazení výsledku je třeba se přihlásit. |