Search Results (15)

Power transformers are the most important and expensive assets in high-voltage power systems. To ensure an adequate level of reliability throughout the transformer’s lifetime, its maintenance strategy must be well defined. When an incipient fault occurs in the transformer insulation, a gas concentration pattern, representative of the type of fault, is generated. Fault-identification methods use gas concentrations and their ratios to identify the type of fault. None of the traditional or new fault-identification methods attempt to detect transformer oil contamination from on-load tap changer (OLTC) gases. In this study, from dissolved gas analysis (DGA) samples of transformers identified as contaminated in a previous study, fault-identification methods based on graphical representations were used to observe the patterns of results. From such patterns, Duval’s triangle and pentagon methods were modified to include a new zone indicating oil contamination (OC) from OLTC gases. Finally, the proposed modifications were validated using 75 DGA samples extracted from previous studies that were identified as D1 or D2 faults or contaminated from OLTC. This validation showed that only 14.7% and 13.3% of the DGA samples fell within the new OC zone of the proposed triangle and pentagon, respectively. Full article

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. Full article

A key component for the performance, availability, and reliability of power grids is the power transformer. Although power transformers are very reliable assets, the early detection of incipient degradation mechanisms is very important to preventing failures that may shorten their residual life. In this work, a comparative analysis of standard machine learning (ML) algorithms (such as single and ensemble classification algorithms) and automatic machine learning (autoML) classifiers is presented for the fault diagnosis of power transformers. The goal of this research is to determine whether fully automated ML approaches are better or worse than traditional ML frameworks that require a human in the loop (such as a data scientist) to identify transformer faults from dissolved gas analysis results. The methodology uses a transformer fault database (TDB) gathered from specialized databases and technical literature. Fault data were processed using the Duval pentagon diagnosis approach and user–expert knowledge. Parameters from both single and ensemble classifiers were optimized through standard machine learning procedures. The results showed that the best-suited algorithm to tackle the problem is a robust, automatic machine learning classifier model, followed by standard algorithms, such as neural networks and stacking ensembles. These results highlight the ability of a robust, automatic machine learning model to handle unbalanced power transformer fault datasets with high accuracy, requiring minimum tuning effort by electrical experts. We also emphasize that identifying the most probable transformer fault condition will reduce the time required to find and solve a fault. Full article

Transformers insulated with mineral oil tend to form gases, which might be caused by system faults or extended use. Based on an evaluation of the main failure analysis techniques using combustible gases, this study reviewed the conventional techniques for Dissolved Gas Analysis (DGA), present in the norms IEC 60599 and IEEE Std C57.104, and their failure analysis tendency. Furthermore, to illustrate distinct technique performances and failures, the performance of the following techniques was analyzed based on the IEC TC10 database: Dornenburg, Duval Triangle, Duval Pentagon, IEC ratio method, Key Gas, and Rogers. The objective of this work was to present relevant information to support students and professionals who work in failure analysis and/or assist in the development of new tools in the DGA field. Full article

Dissolved gas analysis is an important method for diagnosing the operating condition of power transformers. Traditional methods such as IEC Ratios and Duval Triangles and Pentagon methods are not applicable in the case of abnormal or missing values of DGA data. A novel transformer fault diagnosis method based on an extreme gradient boosting algorithm is proposed in this paper. First, the traditional statistical method is replaced by the random forest regression algorithm for filling in missing values of dissolved gas data. Normalization and feature derivation of the outlier data is adopted based on the gas content. Then, hyperparameter optimization of the transformer fault diagnosis model based on an extreme gradient boosting algorithm is carried out using the tree-structured probability density estimator algorithm. Finally, the influence of missing data and optimization algorithms on transformer fault diagnosis models is analyzed. The effects of different algorithms based on incomplete datasets are also discussed. The results show that the performance of the random forest regression algorithm on missing data filling is better than classification and regression trees and traditional statistical methods. The average accuracy of the fault diagnosis method proposed in the paper is 89.5%, even when the missing data rate reaches 20%. The accuracy and robustness of the TPE-XGBoost model are superior to other machine learning algorithms described in this paper, such as k-nearest neighbor, deep neural networks, random forest, etc. Full article

Several high voltage condenser type OIP (oil impregnated paper) bushings used in the electrical industry are filled with dodecylbenzene, because of its ability to absorb hydrogen formed by corona partial discharges in the thick paper insulation of these pieces of equipment. Some of them form large quantities of ethane, raising the concern of overheating faults in their paper insulation, which may be risky for their safe operation in service. The article presents dissolved gas analysis results of oil samples taken from the bushings with high ethane formation, together with results of laboratory tests of stray gassing of dodecylbenzene performed according to CIGRE procedure. By using Duval Pentagon 2 it is possible to compare patterns in the laboratory and in bushings and evaluate the temperature range of possible defects. Stray gassing/overheating of dodecylbenzene in bushings within the stray gassing temperature range and whatever the possible other causes, is not a concern for their safe operation according to observations published by CIGRE. Full article

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. Full article

A Duval triangle is a diagram used for fault type identification in dissolved-gas analysis of oil-filled high-voltage transformers and other electrical apparatus. The proportional concentrations of three fault gases (such as methane, ethylene, and acetylene) are used as coordinates to plot a point in an equilateral triangle and identify the fault zone in which it is located. Each point in the triangle corresponds to a unique combination of gas proportions. Diagnostic pentagons published by Duval and others seek to emulate the triangles while incorporating five fault gases instead of three. Unfortunately the mapping of five gas proportions to a point inside a two-dimensional pentagon is many-to-one; consequently, dissimilar combinations of gas proportions are mapped to the same point in the pentagon, resulting in mis-diagnosis. One solution is to replace the pentagon with a four-dimensional simplex, a direct generalization of the Duval triangle. In a comparison using cases confirmed by inspection, the simplex outperformed three ratio methods, Duval triangle 1, and two pentagons. Full article

The healthiness and or faultiness of oil-immersed electrical equipment using dissolved gas characterization has remained a critical and challenging task in power systems. Dissolved gas analysis (DGA) continues to be the utmost preferred technique of detecting mainly slow evolving thermal and electrical faults. However, DGA can reveal more than just faults in equipment. This research looks at broad areas where DGA can be applied to determine the healthiness or faultiness of equipment in addition to fault identification. In equipment considered normal—i.e., fault-free—DGA can give the degree of healthiness (DOH) based on Rogers ratios C₂H₂/C₂H₄ < 0.1, 0.1 < CH₄/H₂ < 1, and C₂H₄/C₂H₆ < 1, plus the 3 < CO₂/CO < 10 ratio for identifying fault-free devices. This answers the question: How healthy or normal is the equipment? Similarly, when these ratios are violated, it signifies the presence of faults, and two things ought to be determined. One is to identify the type of fault(s), which has been the norm. The other thing that can be evaluated is the degree of faultiness (DOF), based on the extent to which the ratios have been violated. Rarely has this been done. This might answer the question for the same fault class: How severe is the fault? To synthesize the DOH and/or DOF, fuzzy logic is applied. To diagnose faults, fuzzy logic and fuzzy-evidential tools are proposed. The accuracy and effectiveness of the proposed fuzzy techniques are better than those of the IEC60599 and Rogers methods, and they are comparable to those of the Duval Triangle 1 and Pentagon 1 methods using the six IEC faults. Results from DOF evaluation have shown electrical faults to be more impactful relative to the rest. Full article

The aim and contribution of this paper is to identify with Duval Pentagon 2 the stray gassing (SG) patterns of inhibited and uninhibited mineral oils in transformers in service and in the well-established laboratory SG tests of CIGRE and ASTM, so that SG in transformers can be easily distinguished from the other types of faults occurring in them. The SG test of IEC 60296-2020 is inadequate or much less effective for that purpose. Full article

Operational factors are known to affect the health of an in-service power transformer and to reduce the capabilities and readiness for energy transmission and distribution. Hence, it is important to understand the degradation rate and corresponding behavioral aspects of different insulating fluids under various fault conditions. In this article, the behavior of mineral oil and two environmentally friendly fluids (a synthetic and a natural ester) are reported under arcing, partial discharges, and thermal fault conditions. Arcing, partial discharges and thermal faults are simulated by 100 repeated breakdowns, top oil electrical discharge of 9 kV for five hours, and local hotspots respectively by using different laboratory-based setups. Some physicochemical properties along with the gassing tendency of fresh and aged insulating liquids are investigated after the different fault conditions. UV spectroscopy and turbidity measurements are used to report the degradation behavior and dissolved gas analysis is used to understand the gassing tendency. The changes in the degradation rate of oil under the influence of various faults and the corresponding dissolved gasses generated are analyzed. The fault gas generations are diagnosed by Duval’s triangle and pentagon methods for mineral and non-mineral oils. It is inferred that; the gassing tendency of the dielectric fluids evolve with respect to the degradation rate and is dependent on the intensity and type of fault. Full article

The paper describes a newly proposed combination of the two existing Duval Pentagons method utilized for the identification of mineral oil-insulated transformers. The aim of the combination is to facilitate automatic fault identification through computer programs, and at the same time, apply the full capability of both original Pentagons, now reduced to a single geometry. The thorough classification of a given fault (say, of the electrical or thermal kind), employing individual Pentagons 1 and 2, as originally defined, involves a complex geometrical problem that requires the build-up of a convoluted geometry (a regular Pentagon whose axes represent each of five possible combustible gases) to be constructed using computer language code and programming, followed by the logical localization of the geometrical centroid of an irregular pentagon, formed by the partial contribution of individual combustibles, inside two similar structures (Pentagons 1 and 2) that, nonetheless, have different classification zones and boundaries, as more thoroughly explained and exemplified in the main body of this article. The proposed combined approach results in a lower number of total fault zones (10 in the combined Pentagons against 14 when considering Pentagons 1 and 2 separately, although zones PD, S, D1 and D2 are common to both Pentagons 1 and 2), and therefore eliminates the need to solve for two separate Pentagons. Full article

In determining the severity of power transformer faults, several approaches have been previously proposed; however, most published studies do not accommodate gas level, gas rate, and Dissolved Gas Analysis (DGA) interpretation in a single approach. To increase the reliability of the faults’ severity assessment of power transformers, a novel approach in the form of fuzzy logic has been proposed as a new solution to determine faults’ severity using the combination of gas level, gas rate, and DGA interpretation from the Duval Pentagon Method (DPM). A four-level typical concentration and rate were established based on the local population. To simplify the assessment of hundreds of power transformer data, a Support Vector Machine (SVM)-based DPM with high agreements to the graphical DPM has been developed. The proposed approach has been implemented to 448 power transformers and further implementation was done to evaluate faults’ severity of power transformers from historical DGA data. This new approach yields in high agreement with the previous methods, but with better sensitivity due to the incorporation of gas level, gas rate, and DGA interpretation results in one approach. Full article

The intent of this work is to understand the influence of low energy discharge electric faults in mineral oil and synthetic esters on liquid degradation and gassing tendency at different aging conditions (based on acidity values). A low energy discharge electric fault has been created by continuous discharge of 9 kV for five hours on the liquid surface using a suitable laboratory setup. Liquid degradation is reported by adopting UV spectroscopy, turbidity, and particle counter measurements. The gassing tendency is understood by dissolved gas analysis using Duval’s triangle and Duval’s pentagon methods for mineral oil and non-mineral oils accordingly. It is observed that the influence of low energy discharges on liquid degradation is higher in mineral oils than synthetic esters. The fault gasses in mineral oil are involved with electrical and thermal faults accompanied by stray gassing whereas only partial discharge activity is noticed for synthetic esters. Importantly, the existence of low energy discharge faults like corona discharges will involve a generation of excess high molecular weight products as compared to low molecular weight products that are soluble in liquid volume. Full article

Dissolved gas analysis (DGA) is attracting greater and greater interest from researchers as a fault diagnostic tool for power transformers filled with vegetable insulating oils. This paper presents experimental results of dissolved gases in insulating oils under typical electrical and thermal faults in transformers. The tests covered three types of insulating oils, including two types of vegetable oil, which are camellia insulating oil, Envirotemp FR3, and a type of mineral insulating oil, to simulate thermal faults in oils from 90 °C to 800 °C and electrical faults including breakdown and partial discharges in oils. The experimental results reveal that the content and proportion of dissolved gases in different types of insulating oils under the same fault condition are different, especially under thermal faults due to the obvious differences of their chemical compositions. Four different classic diagnosis methods were applied: ratio method, graphic method, and Duval’s triangle and Duval’s pentagon method. These confirmed that the diagnosis methods developed for mineral oil were not fully appropriate for diagnosis of electrical and thermal faults in vegetable insulating oils and needs some modification. Therefore, some modification aiming at different types of vegetable oils based on Duval Triangle 3 were proposed in this paper and obtained a good diagnostic result. Furthermore, gas formation mechanisms of different types of vegetable insulating oils under thermal stress are interpreted by means of unimolecular pyrolysis simulation and reaction enthalpies calculation. Full article