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.

Since power transformers are the most important pieces of equipment in electricity transmission and distribution systems, special attention must be paid to their maintenance in order to keep them in good condition for a long time. This paper reviews the main steps in the process of diagnosing the health of power transformer insulation, which involves the science of analysing the gases dissolved in power transformer oil for effective identification of faults. An accurate diagnosis of incipient faults is favourable to sustainable development and necessary to maintain a reliable supply of electricity. The methods presented for fault diagnosis in mineral-oil-immersed power transformers are divided into analytical and graphical methods and have been found to be simple, economical and effective. After describing the methods, both their strengths and weaknesses were identified, and over the years, the methods were complemented to provide highly accurate information, validated by field inspections. This paper focuses on practical information and applications to manage maintenance based on accurate and up-to-date data. The contents of this paper will be of particular use to engineers who manufacture, monitor and/or use high-power transformers in the energy sector, as well as to undergraduate, master’s and PhD students interested in such applications.

The purpose of this study was to validate a designed three phase power transformer with modern software that use finite element method, by analyze it from two points of view: magnetic and thermal. For that, on the analysis it will be used two modules from ANSYS software, ANSYS Maxwell 3D and ANSYS Mechanical, both coupled in the same environment, ANSYS Workbench. It has used the latest versions from ANSYS, Workbench 14.5 and Maxwell 3D 16. [1] On the same time, it can be seen the 3D magnetic nonlinear behavior of this three phase power transformer and thermal effects for the ferromagnetic core. Also it can be found the magnitude of B field, the regions with problems and the value of the total core losses for the same transformer but at 3 types of voltages: 330 kV, 400 kV and 430 kV.

Starting from the current need for the safety of energy systems, in which power transformers play a key role, the study of the health of power transformers in service is a difficult and complex task, since the assessment consists of identifying indicators that can provide accurate data on the extent of degradation of transformer components and subcomponents, in order to establish a model for predicting the remaining life of transformers. Therefore, this paper proposes a model for assessing the remaining service life by diagnosing the condition of the transformer based on the health index (HI) obtained from a multi-parameter analysis. To determine the condition of power transformers, a number of methods are presented based on the combination of the combined Duval pentagon (PDC) method and ethylene concentration (C₂H₄) to determine the fault condition, the combination of the degree of polymerisation (DP) and moisture to determine the condition of the cellulose insulation and the use of the oil quality index (OQIN) to determine the condition of the oil. For each of the classification methods presented, applications based on machine learning (ML), in particular support vector machine (SVM), have been implemented for automatic classification using the Matlab development environment. The global algorithmic approach presented in this paper subscribes to the idea of event-based maintenance. Two case studies are also presented to validate SVM-based classification methods and algorithms.

On-line monitoring of function and condition parameters and the efficiency of the cooling system are key elements in determining the working condition of transformers. This paper presents the implemented hardware and software solutions for carrying out the winding temperature on-line monitoring system using fiber optic sensors. The actual winding temperature is a measurand which defines the condition, load capacity and operating life of transformers. By carrying out the temperature control and monitoring system for power transformer windings using fiber optic sensors, ICMET Craiova aligned itself to the requirements of the international standards.

This paper presents a diagnostic system which implements several most widely used methods, using fuzzy logic, for fault diagnosis in power transformers using the dissolved gas analysis and furan derivatives analysis. A case study carried out for a power transformer is presented for the validation of the diagnosis system. The results obtained led to the rehabilitation of the transformer and putting it into service within normal parameters. Our system is open that mean it can to exchange data (data input and results) in wide range of data formats like Excel files, MySQL database and OPC client/server technology.