An innovative method of fault detection in power transformers

The Bayesian classifier is a priori the optimal solution for minimizing the total error in problems of statistical pattern recognition. The article suggests using the classifier as a regular tool to increase the reliability of defect recognition in power oil-filled transformers based on the results of the analysis of gases dissolved in oil. The wide application of the Bayesian method for solving tasks of technical diagnostics of electrical equipment is limited by the problem of the multidimensional distribution of random parameters (features) and the nonlinearity of classification. The application of a generalized feature of a defect in the form of a nonlinear function of the transformer state parameters is proposed. This simultaneously reduces the dimension of the initial space of the controlled parameters and significantly improves the stochastic properties of the random distribution of the generalized feature. A special algorithm has been developed to perform statistical calculations and the procedure for recognizing the current technical condition of the transformer using the generated decision rule. The presented research results illustrate the possibility of the practical application of the developed method in the conditions of real operation of power transformers.

The structure of the park of power transformers with a higher voltage of 6-10 Kv on the example of the electric grid organization of the branch of pjsc «Rosseti center»-«Orelenergo», serving rural electric networks

THE PURPOSE. Evaluation of the power transformer fleet 6-10/0,23-0,4 kV on the example of a branch of PJSC «Rosseti Center»-«Orelenergo».METHODS. In the work, based on the database of the branch of PJSC «Rosseti Center»-«Orelenergo», an analysis was made of the fleet of power transformers with a higher voltage of 6-10 kV in terms of their number, circuits and groups of connection of windings, rated power, terms of service, as well as energy efficiency classes, taking into account the current standards of the technical organization of PJSC «Rosseti».RESULTS. According to the results of the study, it was revealed that among the transformer fleet of the branch of PJSC «Rosseti Center»-«Orelenergo», the number of which is 6026 units, 4528 (73% of the total number) transformers have a circuit and a group of winding connections Y/Y0. The most numerous are transformers with rated capacities of 63 kVA, 100 kVA, 160 kVA, 250 kVA (respectively 853, 1454, 1252, 802 pieces of equipment). It was also revealed that only 268 transformers out of 6206, i.e. 4.3% of the total amount comply with the standard of PJSC «Rosseti» in terms of modern requirements for the level of energy efficiency class.CONCLUSION. A variant of the strategy for replacing power transformers in the branch of PJSC «Rosseti Center»-«Orelenergo» is proposed, within the framework of which trasformers with a given design, circuit and winding connection group, rated capacities and energy efficiency classes are replaced. The implementation of the strategy proposed in the work will make it possible to reduce total electricity losses by 2.3%, as well as increase the share of energy-efficient transformers from 4.3% to 20.4% in the branch of PJSC «Rosseti Center»-«Orelenergo».

Oil health index calculation and incipient fault diagnosis in power transformers using fuzzy logic

In this paper, a new fuzzy logic (FL) model is proposed for assessing the health status of power transformers. In addition, the detection of incipient faults is achieved where two or more faults exist simultaneously. The process is carried out by integrating a fuzzy logic model with the conventional International Electric Committee (IEC) ratio codes method. As transformer oil insulation deteriorates, excess percentages of dissolved gases such as hydrogen, methane, ethane, acetylene and ethylene are induced within the trasnformer. The status of oil health is generally assessed using these gas concentrations. Therefore, in the proposed model, 31 fuzzy rules are designed based on the severity levels of these gases in order to determine the health index (HI) of the oil. Similarly, any incipient faults along with their severity are also detected using the proposed fuzzy logic model with 22 expert rules. To validate the proposed fuzzy logic model, the data for dissolved gases in 50 working transformers operated by the Himachal Pradesh State Electricity Board (HPSEB), India, are collected. Over the years, calculations for the health index have been performed using conventional dissolved gas analysis (DGA) interpretation methods. The shortcomings of these methods, such as non-reliability and inaccuracy, are successfully overcome using the proposed model. The detection of incipient faults is normally performed using key gas, Rogers ratios, the Duval triangle, Dornenburg ratios, modified Rogers ratios and the IEC ratio codes methods. The shortcomings of these conventional ratio code methods in identifying incipient faults in some typical cases, ie multiple incipient fault cases, are overcome by the proposed fuzzy logic model.

A Comparison Between Half and Full Fins at Nanofluids in Transformers

Power transformers characterize the biggest section of capital investment within the distribution substations as well as transmission. Additionally, outages of those transformers have a substantial economic influence on the functioning of an electrical network due to the fact that the power transformers are one of the utmost overpriced constituents in an electricity structure. A suggested thermal model for a distribution transformer is investigated. The temperature distribution in the three-phase transformer (250 KVA 11/.416 KV core type, mineral oil) was obtained using “COMSOL PROGRAM” after a 3D simulation utilizing a transient analysis in light of the Finite Element Method (FEM). Meanwhile, the suggested model is being used to examine the impacts of different types of oil on HOST. To test the effect of nanoparticles on heat transfer process, the insulation oil was changed with Nanofluids and hybrid nanofluids; For present work, can be concluded when add nanofluids (Al2O3, CuO, SiC) for oil of transformer under different concentration ratio (0.3,0.5,0.8,1,1.2,1.4 % wt) and add hybrid nanofluids (oil+ Al2O3+CuO), (oil+ Al2O3+SiC), (oil+ SiC +CuO) at different concentration ratio (1,1.2,1.4 % wt). The concentration of nanofluids show a direct influence on the temperature reduction for the studied cases. Finally it can be said, the proposed model was succeeded in simulating the distribution transformer, which is in good agreement with the experimental tests adopted for this work, and it could be used as a design tool with assist of COMSOL Multiphysics Package. The present model successfully accomplished for expecting the temperature distribution at any locations in the transformer when compared with practical measurement.