Gas Formation from Arcing Faults in Transformers—Part II

In an electric power system, a power transformer is one of the most critical equipment and cannot be separated from possible abnormal conditions due to fault. Dissolved Gas Analysis (DGA) is a reliable technique for detecting the presence of a fault condition that just occurred in oil immersed transformer. Basically DGA is a process to calculate the levels or values of hydrocarbon gases that are formed due to abnormalities. The gas inside the transformer can function as a marker for various types of faults. In this paper, for DGA testing and evaluation of the type of fault in the power transformer using the interpretation of IEEE std C57.104 and IEC 60599. The method used for DGA testing according to the IEEE interpretation is total dissolved combustible gas (TDCG), key gas, doernenburg ratio, and roger ratio. While the method used for DGA testing at IEC 60599 is the duval triangle, the basic gas ratio and CO2/CO ratio. From the results of DGA tests that have been carried out, all of these methods will be used to ascertain the type of fault that occurs in the power transformer.

The most common fault diagnosis method for oil-immersed power transformers is dissolved gas analysis (DGA). Doernenburg ratios, Rogers ratios, IEC (International Electrotechnical Commission) ratios, and Duval's triangle are conventional DGA techniques for insulating oil in power transformers. In this study, Scikit-learn known as a popular open-source free machine learning tool for Python programming language has been used to develop different machine learning (ML) classifiers to effectively detect defects in oil-immersed power transformers. These classifiers are Decision Trees, Support Vector Machines, Gaussian Naive Bayes, k-Nearest Neighbours, Random Forests, and Multi-Layer Perceptron. The input vector of each classifier has been formed by Doernenburg ratios, Rogers ratios, IEC ratios, and CSUS (California State University Sacramento) method. After these classifiers are completely trained, unseen DGA data sets are then used to evaluate their performances. Based on a statistical analysis, the study can indicate the most effective type of the input vector and ML classifier for precisely detecting faults in power transformers.

The power transformer is an essential component of the electrical network that can be used to step up and step down voltage. Dissolved gas analysis (DGA) is the most reliable method for the identification of incipient faults in power transformers. Various DGA methods are used to observe the generated key gases after oil decomposition. The main gases included are hydrogen (H2), ethylene (C2H4), acetylene (C2H2), methane (CH4), and ethane (C2H6). There is a lack of research that can compare the performance of various DGA methods in identification of faults in power transformer. In addition, it is also not clear which DGA method is optimal for identification of faults in power transformer. In this paper, the comparative performance study of seven DGA methods such as Roger’s ratio, key gas, IEC ratio, the Doernenburg ratio, the Duval triangle, three-ratio method, and the relative percentage of four gases is carried out in order to identify the optimal technique for fault identification in transformer. The data of various power transformers installed in “RAWAT” NTDC grid station, Islamabad, and “UCH-II” power station, Balochistan, are considered for the comparative analysis. This analysis shows that the three-ratio method provides better performance than other DGA methods in accurately identifying the faults in power transformers. The three-ratio method has 90% accuracy in identifying the faults in power transformer.

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.

This paper proposes an integrated approach utilizing Fuzzy Logic and Decision Tree algorithms to diagnose early-stage faults in power transformers based on Dissolved Gas Analysis (DGA) test results of transformer insulation oil. Overcoming limitations in conventional methods such as Duval Triangle, Key Gas Analysis, Rogers Ratio, IEC Ratio, and Doernenburg Ratio, our Fuzzy Logic and Decision Tree models address issues like inaccurate diagnosis, inconsistent diagnosis, lack of decisions or out-of-code results, and time-intensive manual calculations for large DGA datasets. The Decision Tree algorithm, a machine learning technique is applied to categorize faults into thermal and electrical types. Trained with over 300 DGA samples from transformers with known faults, the models exhibit robust performance during testing with different datasets. Notably, the Duval Triangle decision tree model attains the highest accuracy among the ten developed models, achieving a 98% accuracy rate when tested with 50 samples with known faults. Moreover, Decision Tree models for KGA, Doernenburg, Rogers, and IEC also demonstrate substantial prediction accuracy at 92%, 86%, 92%, and 90% respectively underscoring the efficacy of artificial intelligence methods over traditional approaches.

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.

A new approach of DGA interpretation technique for transformer fault diagnosis

Electrical energy is an important factor to support people's lives. Electrical energy from the generator is channeled through a transmission line to the substation. Transformer (transformer) is a very important equipment in the electric power system. Inside there is oil which functions as an insulating medium and also as a cooling medium. This electrical and thermal load can cause dissolved gases in the transformer oil. This DGA (Dissolved Gas Analysis) test aims to determine the content of dissolved gases in transformer oil and then to analyze using three fault indication methods Total Dissolved Combustible Gas (TDCG) Method, Roger Ratio, and Duval Triangle. Results The analysis uses these three methods to determine corrective actions according to the condition of the transformer oil. Corrective action to return the dissolved gases in the transformer oil back to normal. Transformer oil filtering aims to remove or reduce potentially damaging gases from the transformer oil content. On December 29, 2020 the transformer experienced an increase in the content of flammable hydrogen gas and acetylene gas, the TDCG value was 1520 ppm and after corrective action the dissolved gas content decreased to 481 ppm, so that the condition of the transformer oil returned to normal.

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.

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.

To allow utilities to fulfill self-imposed and regulative performance targets the demand for new optimized tools and techniques to Estimate the performance of modern Transformers has increased. The modern power transformers has subjected to different types of faults, which affect the continuity of power supply which in turn causes serious economic losses. To avoid the interruption of power supply, various fault diagnosis approaches are adopted to detect faults in the power transformer and has to eliminate the impacts of the faults at the initial stage. Among the fault diagnosis methods, the hybrid technique of Particle Swarm Optimization (PSO) with Support Vector Machine (SVM) learning algorithm is simple conceptually derived and its implementation process is faster with better scaling properties for complex problems with non linearity and load variations but performance factor related to accuracy has a declined value in case of correlations implicit . In order to obtain better fault diagnosis to improve the service of the power transformer, SVM is optimized with Improved PSO technique to achieve high interpretation accuracy for Dissolved Gas Analysis (DGA) of power transformer through the extracting positive features from both the techniques. Primary SVM is applied to establish classification features for faults in the transformer through DGA. The features are applied as input data to Autonomous optimized Technique for faults analysis. The proposed methodology obtains the DGA data set from diagnostic gas in oil of 500 KV main transformers of Pingguo Substation in South China Electric Power Company. The simulations are carried out in MATLAB software with an Intel core 3 processor with speed of 3 GHZ and 4 GB RAM PC. The result obtained by Autonomous optimized Technique (IPSO-SVM) is compared against PSO-SVM to estimate the performance of the classifiers in terms of execution time and quality of classification for precision. The test results indicate that the Autonomous optimization of IPSO-SVM approach has significantly improved the classification accuracy and computational time for power transformer fault classification. Keywords: Transformer Fault Analysis, Improved Particle Swarm Optimization, Hybrid Optimization, Dissolved Gas Analysis, Support Vector Machine

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.

Dissolved gas analysis (DGA) and its key-gas ratios (C2H2/C2H4, CH4/H2, C2H4/C2H6) are the most widely used fault diagnostic tests for transformers. This technique monitors the concentration of various gases in transformer oil and uses it to interpret the type of fault. In this study, support vector machines (SVM) is proposed to classify and predict electrical faults in transformers depending on the key-gas ratios concentrations. A dissolved gas analysis data obtained from published papers are used as a sample for the training and test set with a supervised machine learning from MATLAB software. Results indicate that SVM method can achieve good accuracy under the circumstance of small training data.

— Transformer oil is very important because it functions as a coolant and also as an insulator. In transformer oil there are dissolved gases that can cause failure of the transformer due to gas failure (fault gas). This research was conducted to determine the condition of transformer oil by analyzing the results of the Dissolved Gas Analysis (DGA) test. The DGA (Dissolved Gas Analysis) test is used to determine the type of failure in a transformer that has been operating for years. The DGA test is carried out by monitoring fault gases such as H2, CH4, CO, CO2, C2H4, C2H6, C2H2, O2, N2 and O2N2 which generally cannot be detected in testing the characteristics of transformer oil. Based on the test results using the Total Dissolve Gas Analysis (TDCG) method condition 1 with the amount of flammable gas 202.845 ppm with the interpretation that the transformer can operate normally. The test results using the Roger's Ratio method on the Power Transformer 1 GI Kotabumi obtained codes 0 0 0. From the combination of the 3 codes above, it shows that the transformer oil is in good condition. The results of the test using the Key Gas method on Power Transformer 1 GI Kotabumi obtained a CO value of 56% caused by overheating of cellulose or overheating of solid insulation in the transformer but that value is still far from the threshold&#x0D; Keywords— Dissolved Gas Analysis (DGA),Transformer oil, Roger'r ratio method, KeyGas

Dissolved Gas Analysis (DGA) is a critical method for transformer fault diagnosis. However, the effective identification of internal faults in transformers is challenging due to the lack of a clear correspondence between the limited information from DGA sensors and the complex gas diffusion patterns within the transformer. Therefore, this paper conducted experimental research on the transmission characteristics of H<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf>, CH<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</inf>, and CO in stagnant oil, yielding key parameters such as gas solubility. The study reveals that the gas transport process in stagnant oil comprises three distinct stages. The gases exhibit a dual diffusion characteristic: centrifugal diffusion (from the center to the periphery) and vertical diffusion. This research provides support for fault tracing in DGA and the optimization of oil sampling locations.