Comparative Study of DGA for Transformer Service Life

This study discusses the application of four Dissolved Gas Analysis (DGA) methods in identifying failure indicators in power transformers. Transformer failures can occur in the form of thermal and electrical failures, producing hazardous gases known as fault gases. The insulating oil used in power transformers plays a role in cooling and dissolving these dangerous gases to prevent unrestricted circulation. Determining the type and concentration of gases dissolved in the oil can provide information about transformer failure indicators. Therefore, DGA analysis is conducted to determine effective methods for identifying these failure signs. Oil samples were taken from transformers and analyzed using four different DGA methods. The DGA analysis results revealed varying levels of gas concentration. Based on the analysis results using the Key Gas Method, Roger's Ratio Method, Doernenburg's Ratio Method, and Duval's Triangle Method, it was found that the transformers experienced thermal damage. The Duval's Triangle Method analysis concluded that two transformers had overheated. Thus, the Duval's Triangle Method analysis can be considered an effective method for identifying disturbances in power transformers due to its simple yet effective calculations

In any power system or grid network transformer device use for power from one circuit to another circuit without changing the frequency with high efficiency levels. Due to number of usages Transformer protection is very important now a days for electric supply which is fault free, efficiency and to increase the transformer life cycle. This paper is a brief discussion about the concentration of different gases like CO, CO2, H2, C2H6, C2H4, C2H2 and CH4 related faults which is known as DGA analysis with the help of various classical techniques gives different conditions for the same sample unit. This paper presents MATLAB simulation of ANN and Machine learning based high accuracy design techniques for DGA analysis and the results are compared with the classical techniques like Key Gas Method, IEC Ratio method, Duval triangle Method and Rogers Ratio Method, but in this paper we have done only Duval triangle Method for Comparison using Matlab. The Simulation result of the proposed methods shows that overall DGA analysis using Machine learning algorithm is better than conventional Duval triangle method Performance.

In any power system or grid network transformer device use for power from one circuit to another circuit without changing the frequency with high efficiency levels. Due to number of usages Transformer protection is very important now a days for electric supply which is fault free, efficiency and to increase the transformer life cycle. This paper is a brief discussion about the concentration of different gases like CO, CO2, H2, C2H6, C2H4, C2H2 and CH4 related faults which is known as DGA analysis with the help of various classical techniques gives different conditions for the same sample unit. This paper presents MATLAB simulation of ANN and Machine learning based high accuracy design techniques for DGA analysis and the results are compared with the classical techniques like Key Gas Method, IEC Ratio method, Duval triangle Method and Rogers Ratio Method, but in this paper we have done only Duval triangle Method for Comparison using Matlab. The Simulation result of the proposed methods shows that overall DGA analysis using Machine learning algorithm is better than conventional Duval triangle method Performance.

DGA is one of the most recent techniques developed to diagnose the fault condition on oil filled insulation transformers. There are more than 6 known different methods of DGA fault interpretation technique and so there is the likelihood that they may vary in their interpretations. A series of combined interpretation methods that can determine the power transformer condition faults in one assessment is therefore needed. This paper presents a computer program- based system developed to combine four DGA assessment techniques; Rogers Ratio Method, IEC Basic Ratio Method, Duval Triangle method and Key Gas Method. An easy to use Graphic User Interface was designed to give a visual display of the four techniques. The result shows that this assessment method can increase the accuracy of DGA methods by up to 20% and the no prediction result had been reduced down to 0%.

Early detection of the generated dissolved gases due to different electrical and thermal stresses in transformer oil will prevent the malfunction of the transformer and hence maintain the continuity of the power system network operation. Dissolved gas analysis (DGA) is widely spread technique that is very useful in detecting the incipient faults in the oil filled power transformers. There are many interpretation techniques based on DGA such as Dornenburg method, Key-Gas method, IEC Standard Code, Duval triangle and Rogers methods. The diagnostic accuracy of these techniques is very limited and a significant instability is occurring with data uncertainty. In this paper a modification will be made on the IEC code and Rogers four ratios method based on the case study dataset (320 samples) to improve the accuracy of them. The data is collected from the central chemical laboratory of Egyptian electricity utility and from literatures. The results point out the accuracy improvement using the proposed form of IEC Code and Rogers four ratios methods.

Power Transformer being essential and costlier equipment in the power sector, maintaining its healthiness is a crucial task. Condition monitoring of transformers can help in predicting the kinds of fault and prevent the mishap. It can be achieved through Dissolved Gas Analysis. But interpretation of DGA to find the nature of fault that has occurred within transformer is a difficult job. There are many conventional methods like key gas method, Rogers ratio method, Duval triangle etc. These methods may not provide accurate and precise results in desired time. Therefore, modern heuristic methods can be helpful in reducing the computation time. One on such heuristic method is Artificial Bee Colony algorithm. The results of this method are validated by comparing with the Fuzzy Inference System method on the basis of accuracy, precision, specificity and sensitivity.

Severity is the intrinsic property of transformer faults. DGA of gaseous species evolving from power transformers due to electric and thermal stresses distinguishes between types of faults. No information about severity is provided by the DGA based techniques. For the severity study of incipient faults, thermodynamics involved in their formation is considered. Energy weighted DGA (EWDGA) which uses energy differences between fault gases is used for severity prediction. In this paper, severity of faults detected by Duval triangle method has been evaluated. Prediction of fault severity by Duval triangle method limits the concept of EWDGA to EWMEA (Energy Weighted Methane Ethylene and Acetylene). EWMEA requires subset comprising of three of primary fault gases MEA(methane, ethylene and acetylene) to be energy weighed. Duval triangle also classifies the faults using the same set of gases besides showing least percentage of inaccurate results. Henceforth, EWDGA is applied to Duval triangle to further enhance its fault diagnosis capabilities. The paper also compares the unweighted and weighed counterparts of MEA gas combinations for various fault categories of Duval triangle method. Results show the higher sensitivity of these gases for high-energy faults. The concept of energy weighing has been implemented for Duval triangle by using fuzzy inference system (FIS). The FIS uses both unweighted and weighed inputs for detection of faults and their respective severities simultaneously.

This contribution presents a two‐step hybrid diagnostic approach, combining k ‐means clustering for subset formation, followed by subset analysis conducted by human experts. As the feature input vector has a significant influence on the performance of unsupervised machine learning algorithms, seven feature input vectors derived from traditional methods, including Duval pentagon method, Rogers ratio method, three ratios technique, Denkyoken method, ensemble gas characteristics method, Duval triangle method, and Gouda triangle method were explored for the subset formation stage. The seven proposed individual methods, corresponding to the seven feature input vectors, were implemented using a dataset of 595 DGA samples and tested on an additional 254 DGA samples. Furthermore, a combined technique based on a support vector machine was introduced, utilising the diagnostic results of the individual methods as input features. From training and testing, with diagnostic outcomes of 91.09% and 90.94%, the combined technique demonstrated the highest overall diagnostic accuracies. Using the IEC TC10 database, the diagnosis accuracies of the proposed diagnostic methods were compared to existing methods of literature. From the results obtained, the combined technique outperformed the proposed individual methods and existing methods used for comparison.

This paper deals with the gas production by monoester oil intended to be used as insulating oil under an electrical discharge of low energy. The monoester contains only saturated fatty acids in its hydrocarbon chain. The electrical fault was realized by implementing an AC high voltage to hemispherical shaped electrode pairs with the gap of 2.5 mm immersed in the oil sample. The voltage application was paused when the breakdown occurred in oil and re-applied repeatedly up to 50 and 75 times to allow a high concentration of gasses produced by the oil sample. The resulting gasses were extracted from the oil sample using the headspace method and then analyzed using gas chromatography (GC). Fault identification methods, like DGA status, Key Gas, Duval Triangle, and IEC Ratio, were performed to predict the fault causing the production of such gasses. The results are compared with those of the monoester of unsaturated type. It is found that the Key Gas method is applicable for both oils under electrical discharge. The Duval Triangle and the IEC Ratio methods diagnose the electrical discharge in both monoesters but overestimate them as high energy discharge.

By their structure and design, transformers are devices that are reliable in operation and easy to maintain. In the operation of transformers, the main factors are timely inspection, performance of current and major repairs, as well as the necessary preventive tests. One of the important conditions for the correct use of this device is the correct introduction of the temperature regime and observance to the allowed overloading. Monitoring the condition of the oil is fundamental to the correct operation of the transformer. During the period of use, the oil ages and it loses its original properties. In addition to the deterioration of the properties of the oil due to aging, there is also a change in its properties under abnormal operating conditions of the transformer. So with local heating, build-up of insulation between the steel sheets of the magnetic circuit, poor contact in the tap changer or at the winding terminals under the transformer cover, and with turn short circuits in the windings, oil decomposes. Transformer oil has at least three functions in transformers. Task of transformer oil in oil-filled transformers cooling function, extinguishing the arc, and providing insulation is the main [1]. The appearance of defects caused by both electrical and thermal faults is accompanied by the formation of gases, and their further dissolution in transformer oil. In addition to normal operation, during the operation of the transformer, local thermal overheating (of an insulated conductor, core, etc.) and electrical discharges (partial, spark and arc discharges) can occur. Such defects lead to the destruction of insulation, physical wear and tear, and irreversible damage to the active part of the transformer. To determine the state of oil-filled electrical equipment, gases are used: hydrogen (H2), methane (CH4), ethane (C2H6), ethylene (C2H4), acetylene (C2H2), carbon monoxide (CO), carbon dioxide (CO2). In addition to the above gases, oxygen (O2) and nitrogen (N) are also present, and their concentration varies depending on the tightness of the transformer tank and gases such as propane, butane, butene and others can be released. The study of these gases for diagnostic purposes is not widespread [2]. In this regard, the method of analysis of gases dissolved in oil (DGR) has proven itself all over the world. The main advantage of this method is the timely diagnosis of the malfunction, identification of the nature and control of the defect, which leads to lower economic costs and time savings. Fault diagnostics can be performed using various methods, such as: the ratio of pairs of gas concentrations using the Duval triangle, Japanese ETRA, monogram method, etc. The most important tool for diagnosing the condition of a transformer is observing the evolution of gases by comparing different analyzes of the amount of dissolved gases and understanding this information. The Duval Triangle Method is based on the existing IEC 60599 Gas Ratio Method. Within this triangle there are six potential damage zones. Zones include partial discharges, electrical and thermal damage. The use of this triangle method is based on three main gases (CH2, C2H4 and C2H2). Firstly the concentration levels of these gases are calculated. Then lines are drawn along three sides of an equilateral triangle: on each side, the relative value of a certain gas is laid, which can vary from 0 to 100% clockwise. The sum of the values of the concentrations of these gases is taken as 100. When drawing the boundaries of the zones of defects, the lines are drawn parallel to the lagging side of the triangle. The considered method not only clearly shows the "location" of the defect, but also allows you to observe the trajectory of its development [3]. The oil analysis results reflect the overall condition of the transformer. For this analysis, the oils are sent to a laboratory for testing. The most important indicators are the rate of formation of the total amount of combustible gases and individual gases. Carrying out timely sampling makes it possible to track the dynamics of malfunctions and take appropriate decisions in time. To obtain more reliable results, you must follow the sampling rules, store the samples and transport the samples to the analysis site correctly. Some gases dissolved in transformer oil (ethylene and acetylene) may indicate overheating in the contacts of switching devices, heating of the shield attachment points and bushing pins, short-circuiting of the winding conductors and other defects. The main goal of this article is to solve the problem associated with the extraction of hydrocarbon gases from oil, and to correct the interpretation based on the obtained data [4]. Keywords: DGR, Duval triangle, transformer oil, gas discharges.

DGA is the best technique which is used to identify the incipient fault of the power transformer. The recently most advanced technology used for online fault diagnosis of the transformer is dissolved gas analysis (DGA) and it is technically acceptable. There are some methods on DGA based which are used to solve this analysis are Key-Gas method, Dornenburg method, International Electro technical Commission (IEC) ratio codes, Duval triangle and Rogers's ratio method. Also, the most well-known strategies are key gas and Roger's ratio. Artificial Neural Network (ANN), Fuzzy logic (FL), Neuro-Fuzzy, Support vector machine etc are the techniques which are used to solve dissolved gas analysis. Taking the concentrations of key gases(CO, CO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> , H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> , C <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">6</sub> , C <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</sub> , C <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> & CH <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</sub> ) of different fault conditions for the same sample unit, we can be identified the fault by using various techniques. Because of the assortment of gas data and operational natures, sometimes it is difficult to identify fault by the traditional method and hence fails to diagnose. And because of that it is important to build up a preventive maintenance technique for detection of an incipient fault of transformer automatically through dissolved gas analysis in transformer insulating oil. In this paper, we focus on two methods, Roger's ratio and IEC for the fault diagnosis of transformer insulating oil with Fuzzy interface system (FIS). This diagnosis is totally based on the ratio of concentration gases generated from single or numerous faults however with same predominant nature. At the point when gases generated from two or more faults then the relation between different gases becomes too complicated and unable to match the predefined ratio codes and fails to diagnose. Hence, to avoid such kind of issues we briefly focused on fuzzy logic and carried out diagnosis using Roger's ratio and IEC ratio codes.

Dissolved gas analysis (DGA) is an important method and widely used for assessment the conditions of transformer insulations. The dissolved gases interpretation is one of the most significant procedures to identify the failure causes and/or degree of insulation degradation. The aim of this paper is to represent the performance of various DGA test result interpretation techniques i.e. Doernenburg Ratio, Roger Ratio, IEC Ratio, MSS ratio, Duval Triangle and Duval Pentagon. Fifty six fault cases occurring in transformers obtained from IEC TC 10 database were employed to evaluate the performance of the DGA interpretation techniques. It was found that the Duval triangle method provided the most accuracy DGA interpretation compared with other methods. However, some engineering points of view for evaluation the performance of DGA interpretation techniques for examples normal ageing insulation condition, correct and incorrect fault case identification, and unclear fault case identification including no identification of the fault cases have to be taken into account. Therefore, the performance of the Duval triangle method may be reduced while that of Duval Pentagon may be increased when such parameters are considered.

A common problem that occurs in transformers is failure of the insulating oil. One solution to prevent this disruption is to analyze indications of insulating oil failure. Several previous studies on the analysis of transformer insulating oil failure were based on dissolved gas content tests of transformer oil samples. The research was conducted using several methods in processing dissolved gas test results data, such as the TDCG (Total Dissolved Combustible Gas), Roger's Ratio, and Duval's Triangle methods. In this study, two methods were used for comparison, namely the Breakdown Voltage method and the Fuzzy Logic method. The results obtained show that the condition of the insulating oil using fuzzy logic in the Matlab simulation is in accordance with the condition of the transformer insulating oil based on its breakdown voltage characteristics, namely in a normal state. Meanwhile, gas content that is at a high or very high level has a significant impact on the indication of failure in transformer insulating oil, namely the concentration of TDCG and nitrogen gas content.

An automated transformer dissolved gas analysis evaluation system based on the recently released IEEE Standard C57.104-2019 for mineral oil-immersed transformers is implemented and its outputs demonstrated. The system includes diagnostic capabilities using the Duval Triangles 1-4-5 and Rogers Ratio method. In addition, a Health Index derived from the above Standards is proposed as a compact, yet informative metric for tracking transformer health. Finally, some of the challenges in developing such an implementation are highlighted.

Power transformers are one of the most crucial as well as costly equipment in power grids. Nowadays, a set of factors like the growing load, expanding power consumption, and market liberation affect on the transformer reliability and operational lifetime. At present, numerous approaches are on hand to diagnosis and evaluate their operational conditions. Dissolved gas analysis (DGA) in the insulation oil is an efficient diagnostic scheme which can offer an essential source for transformer failure diagnosis. In this paper, five existing approaches of utilizing dissolved gas data to identify fault kind in power transformers oil are employed. The considered methods are: key gas technique, IEC 60599 technique, Doernenburg ratio scheme, Duval triangle framework, as well as Rogers ratio scheme. These fault diagnosis methods are used to interpret and validate the dissolved gas analysis (DGA) results for four case studies. The experiments are performed on four 15.75 kV/400 kV, 3-Phase, 250 MVA power transformers in a thermal power plant. The obtained DGA results reveal that all considered DGA methods have effectively detected the fault type for each considered case except Duval triangle method which has failed to discover the fault type for two cases.