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.

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Transformer health assessment techniques, based on applicable standards, such as the dissolved gas analysis (DGA), through laboratory testing or online monitoring are used to analyze the symptoms of a failure which develops in transformers from an early stage. The DGA from a sample of dielectric liquid taken from the main tank generates information on the state of degradation of the active part. It was found that of all the furan derivatives which result from the degradation of insulation, 2-furfuraldehyde (2-FAL) is the only derivative which dissipates in large quantities in oil. Because of this, and due to its thermal stability as compared to other derivatives, 2-FAL is the best unit of measurement to determine and monitor the degree of polymerization (DP) of insulation. The poor accessibility of paper samples has led to difficulties in testing the ageing state of paper directly by measuring the tensile strength and the DP. It was developed methods to indirectly assess the ageing state of paper, by means of the chemical markers in oil which are associated with paper ageing. This article presents a method to determine the DP of solid insulation in transformers, which provides a faster and more accurate interpretation, as compared to the classical ones. The 2-FAL data resulting from the lab are recorded in a MySQL database, which is embedded in an intelligent system for diagnosis of DP (ISDDP) based on Adaptive Neuro-Fuzzy Inference System (ANFIS), generating at the output the report with the interpretation of the faults as word files. The automation of the DP diagnostic process will be achieved, allowing the operator to make timely decisions, to avoid any possible damages.