Duval Triangle Research Articles

Evaluation of dissolved gas analysis and long-term performance of non-edible natural ester

This paper aims at investigating the dissolved gas analysis (DGA) and long-term performance of Environtemp (FR3) and Jatropha Curcas oil (JAT). Thermal stress is applied to both oils in a sealed setup at 150°C for up to 2000 hours and samples are removed and tested at 500 hour intervals. It is observed that FR3 is more prone to stray gassing as compared to JAT. The alterations in the structure of the oils are studied by Fourier-transform infrared spectroscopy (FTIR) and 600 MHz nuclear magnetic resonance (NMR) spectroscopy analyses. The DGA interpretation is done using the Duval triangle, Dornenburg ratios, Rogers ratios and IEC methods. In the proposed study, a regression model for the two types of oils is developed to predict the total dissolved combustible gases with time. The findings show that JAT holds considerable properties of an insulating fluid and is a substitute for mineral insulating oils.

Analysis of gas content in oil-filled equipment with defects for which ethane is the key gas

The article presents results of a comprehensive analysis of the gas content in oil of 252 pieces of high-voltage equipment with defects, for which ethane is the key gas. There are 14 types of defects with maximum ethane content, which correspond both to overheating with temperature 150-300°C and low temperature overheating accompanied by discharges with different energy density (partial discharges, spark discharges, discharges with low and high energy density, arc discharges). The most typical defects are analysed and examples of their recognition by different researchers are given. For each type of defect, ranges of gas percentage values and gas ratio values are obtained. This is particularly important for the recognition of combined defects (overheating that is accompanied by discharges), because for such defects the values of these criteria are not regulated in any known standard for the interpretation of the results of the analysis of gases dissolved in oil. For the first time, 14 nomograms and graphic areas of defects for which ethane is the key gas have been obtained, which makes it possible to significantly expand the range of reference nomograms, as the regulatory document in force in Ukraine does not contain reference nomograms for defects with the maximum content of ethane. For all 252 pieces of equipment, a comparative analysis of known diagnoses with diagnoses made using the Duval triangle was made. It has been determined that diagnoses made using different methods may differ significantly from each other as well as from real diagnoses. The ranges of gas percentage and gas ratio values obtained in the work, together with the defect nomograms, make it possible to use three of these criteria simultaneously to recognise the type of defect with a maximum ethane content, which in turn will improve the reliability of defect type recognition based on the analysis of gases dissolved in oil.

Application of Dissolved Gas Analysis in Assessing Degree of Healthiness or Faultiness with Fault Identification in Oil-Immersed Equipment

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.

Fault Prediction for Power Transformer Using Optical Spectrum of Transformer Oil and Data Mining Analysis

Periodic preventive maintenance of power transformer should be conducted for its health monitoring and early fault detection. Transformer oil is a vital element where its contents and properties need to be monitored during the service life of a power transformer. This paper presents an optical spectroscopy measurement from 200 nm to 3300 nm to characterize the transformer oil, which were sampled from the main tanks and ‘on-load tap changer’ of power transformers. The correlation of the optical characteristics in the range of 2120 nm to 2220 nm to the Dissolved Gas Analysis results and Duval Triangle interpretation demonstrates that the low energy electrical discharges, high energy electrical discharges as well as the thermal faults rated at temperatures above 700°C in power transformers can be accurately predicted. For faster and accurate analysis of fault prediction, a data mining analytics tool was constructed using Rapid Miner server to analyze and verify the predictions for a total of 108 oil samples. For the optimization, continuous iterations were performed to determine the best absorbance-wavelength combination that can improve the accuracy of the prediction. The performance of the optical spectroscopy technique integrated with data analytic tool was analyzed and it was found that the technique contributes to a high accuracy of 98.1% in fault prediction. It is a cost-effective and quicker complementing approach to carry out pre-screening of the transformer oil in order to know the condition of the power transformers based on the transformer oil’s optical characteristics.

A Transformer Fault Diagnosis Model Based On Hybrid Grey Wolf Optimizer and LS-SVM

Dissolved gas analysis (DGA) is a widely used method for transformer internal fault diagnosis. However, the traditional DGA technology, including Key Gas method, Dornenburg ratio method, Rogers ratio method, International Electrotechnical Commission (IEC) three-ratio method, and Duval triangle method, etc., suffers from shortcomings such as coding deficiencies, excessive coding boundaries and critical value criterion defects, which affect the reliability of fault analysis. Grey wolf optimizer (GWO) is a novel swarm intelligence optimization algorithm proposed in 2014 and it is easy for the original GWO to fall into the local optimum. This paper presents a new meta-heuristic method by hybridizing GWO with differential evolution (DE) to avoid the local optimum, improve the diversity of the population and meanwhile make an appropriate compromise between exploration and exploitation. A fault diagnosis model of hybrid grey wolf optimized least square support vector machine (HGWO-LSSVM) is proposed and applied to transformer fault diagnosis with the optimal hybrid DGA feature set selected as the input of the model. The kernel principal component analysis (KPCA) is used for feature extraction, which can decrease the training time of the model. The proposed method shows high accuracy of fault diagnosis by comparing with traditional DGA methods, least square support vector machine (LSSVM), GWO-LSSVM, particle swarm optimization (PSO)-LSSVM and genetic algorithm (GA)-LSSVM. It also shows good fitness and fast convergence rate. Accuracies calculated in this paper, however, are significantly affected by the misidentifications of faults that have been made in the DGA data collected from the literature.

IEC 60599와 Duval Triangle Method 기반의 전문가시스템을 활용한 유입변압기의 결함 진단 기법

IEC 60599와 Duval Triangle Method 기반의 전문가시스템을 활용한 유입변압기의 결함 진단 기법

An Alternative Approach for Oil-immersed High Voltage Power Transformer Dissolved Gas Analysis Diagnostic Techniques

This article presents alternative analyzing method of extracted dissolved gases related to insulating oil of power transformers. Analysis of soluble and free gas is one of the most commonly used troubleshooting methods for detecting and evaluating equipment damage. Although the analysis of oil-soluble gases is often complex, it should be expertly processed during maintenance operation. The destruction of the transformer oil will produce some hydrocarbon type gases. The development of this index is based on two examples of traditional evaluation algorithms along with fuzzy logic inference engine. Through simulation process, the results of the initial fractures in the transformer are obtained in two ways by the "Duval Triangle method” and "Rogers’s ratios". In continue, three digit codes containing the fault information are created based on the fuzzy logic inference engine to achieve better results and eliminate ambiguous zones in commonly used methods, especially in the “Duval Triangle method”. The proposed method is applied to 80 real transformers to diagnose the fault by analyzing the dissolved oil based on fuzzy logic. The results illustrate the proficiency of this alternative proposed algorithm. Finally, with utilization of a neural network the alternative practical inference function is derived to make the algorithm more usable in the online condition monitoring of power transformers.

Analysis of Gas Content in Oil-Filled Equipment with Spark Discharges and Discharges with High Energy Density

The paper presents the results of the analysis of the gas content in 444 units of oil-filled equipment, which revealed spark and creeping discharges, as well as discharges with high energy density. The values of gas ratios and the percentage of gases in oil samples are calculated. Nomograms and graphical areas of defects are constructed. The description of the most characteristic damages in the equipment is given. Diagnostics of the equipment using the Duval triangle is carried out. The results obtained make it possible to increase the reliability of defect type recognition based on the results of the analysis of gases dissolved in oil.

Condition assessment of power transformers based on dissolved gas analysis

DGA is more sensitive and reliable technique for detecting and evaluating the incipient fault conditions in oil-immersed transformers. The most frequently interpretation technique that used in dissolved gas analysis is Duval triangle due to its simplicity and accuracy. Unfortunately the impacts of Ethane (C2H6) and Hydrogen (H2) were not included in Duval triangle in spite of their importance in diagnosing certain faults types. In addition, the problem of no decision associated with some cases that lie out of the specified codes appeared in Doernenburg, Roger's and IEC techniques. Accordingly, this paper presented a new proposed graphical triangle technique using new three gas concentration ratios. These ratios were converted to new three ratio percentages of DGA based on the consideration of all five combustible gases. The aim of that was to overcome the conflict takes place in Duval triangle and other traditional techniques. The accuracy of the proposed triangle technique was evaluated using practical DGA database obtained from different transformers of different ratings life spans reported by Egyptian Electricity Holding Company, IEC TC 10 and related databases surveyed from actual incipient cases. The comparison with Duval and other traditional techniques showed good diagnostic accuracy of the new proposed triangle technique.

Analysis of gas composition in oil-filled faulty equipment with acetylene as the key gas

The article presents results of oil-dissolved gas analysis for 239 units of high-voltage equipment with faults under which acetylene is the key gas. The analysis revealed 13 types of fault with acetylene as the key gas that are differentiated by values of the dissolved gas ratios, their concentrations, and fault nomographs. For each type of fault, graphic domains are plotted that, unlike the nomographs, allow taking into account a possible coordinate drift. A graphic domain based fault identification technique is introduced. The types of fault are briefly described, examples of their identification by different investigators given. Duval Triangle based comparative analysis of the equipment diagnosis data is performed. It is revealed that diagnoses made by different methods may differ significantly both from each other and from actual diagnoses. The results presented allow increasing fault identification accuracy via dissolved gas analysis data.

Smart Diagnosis of Incipient Faults Using Dissolved Gas Analysis-Based Fault Interpretation Matrix (FIM)

An intelligent transformer fault diagnostic model is the urgent need of reliable power system. To achieve this goal, a diagnostic system using a dissolved gas analysis (DGA)-based fault interpretation matrix (FIM) is proposed. FIM overcomes the issues of contradictory decisions of independent methods and provides low-complexity solution in comparison with the previous literature for transformer fault diagnosis. The developed system has accuracy enhancement and decision-making stages. In the first stage, fuzzy augmentation of three important DGA methods, namely Rogers’, IEC and Duval triangle, is carried out followed by the FIM stage and an intermediate normalisation stage. The output of fuzzy models serves as input to the matrix which smartly interprets the fault along with its criticality by exploiting the advantages of individual methods in diagnosing particular fault. This matrix integrates fuzzy augmented methods as per priorities assigned to their outputs. The rules of integration are constructed based on performance assessing factors of individual fuzzy models, and overall decision is made on sensitivity of method for the particular fault type. The performance evaluation of FIM shows its improved diagnostic ability which is intended to improve reliability of DGA-based condition monitoring.

A Dynamic Adam Based Deep Neural Network for Fault Diagnosis of Oil-Immersed Power Transformers

This paper presents a Dynamic Adam and dropout based deep neural network (DADDNN) for fault diagnosis of oil-immersed power transformers. To solve the problem of incomplete extraction of hidden information with data driven, the gradient first-order moment estimate and second-order moment estimate are used to calculate the different learning rates for all parameters with stable gradient scaling. Meanwhile, the learning rate is dynamically attenuated according to the optimal interval. To prevent over-fitted, we exploit dropout technique to randomly reset some neurons and strengthen the information exchange between indirectly-linked neurons. Our proposed approach was utilized on four datasets to learn the faults diagnosis of oil-immersed power transformers. Besides, four benchmark cases in other fields were also utilized to illustrate its scalability. The simulation results show that the average diagnosis accuracies on the four datasets of our proposed method were 37.9%, 25.5%, 14.6%, 18.9%, and 11.2%, higher than international electro technical commission (IEC), Duval Triangle, stacked autoencoders (SAE), deep belief networks (DBN), and grid search support vector machines (GSSVM), respectively.

Application of Parzen Window estimation for incipient fault diagnosis in power transformers

Accurate faults diagnosis in power transformers is important for utilities to schedule maintenance and minimises the operation cost. Dissolved gas analysis (DGA) is one of the proven and widely accepted tools for incipient fault diagnosis in power transformers. To improve the accuracy and solve the cases that cannot be classified using Rogers’ Ratios, IEC ratios and Duval triangles methods, a novel DGA technique based on Parzen window estimation have been presented in this study. The model uses the concentrations of five combustible hydrocarbon gases: methane, ethane, ethylene, acetylene and hydrogen to compute the probability of transformers fault categories. Performance of the proposed method has been evaluated against different conventional techniques and artificial intelligence-based approaches such as support vector machines, artificial neural networks, rough sets analysis and extreme learning machines for the same set of transformers. A comparison with other soft computing approaches shows that the proposed method is reliable and effective for incipient fault diagnosis in power transformers.

Analysis of Transformer Conditions using Triangle Duval Method

Electrical or thermal failure that occurred in the transformer oil can produce a dangerous gas called gas fault. By indicating the content of dissolved gases in the transformer oil, can be obtained information about what happened to the oil so it can be estimated indication of the type and cause of the failure in the transformer. The method used to identify and analyze the dissolved gases in oil is called Dissolved Gas Analysis (DGA). In this study conducted by the DGA Duval Triangle method for transformer oil used in geothermal power plant UPJP Kamojang PT. Indonesia Power. The results using Duval Triangle method produces thermal failure and partial discharge.

Combining and comparing various machine‐learning algorithms to improve dissolved gas analysis interpretation

Since the discovery of dissolved gas analysis (DGA), it is considered as a leading technique for the diagnosis of liquid insulated power equipment. However, accurate analysis results can only be achieved if the measured gases closely reflect the actual equipment condition to enable an appropriate interpretation of these gases. In general, conventional techniques such as the ratio method, key gases, and Duval triangle combined or not with artificial intelligence techniques such as machine-learning algorithms are used for DGA interpretation. Here, four well-known machine-learning algorithms are compared in terms of DGA fault classification – Bayes network, multilayer perceptron, k-nearest neighbour, and J48 decision tree. Moreover, the effect of applying ensemble methods such as boosting through the Adaboost algorithm and bootstrap aggregation (bagging) is analysed, and the performances of these algorithms are evaluated. The data for developing classification models was transformed into three forms, other than the raw data. The obtained results clearly presented the efficiency and stability of some algorithms such as the J48 tree and Bayes networks for DGA fault classification, in particular, when the data is appropriately pre-processed. Moreover, the performance of these algorithms was found to consistently improve by integrating the concepts of multiple models or ensemble methods.

Proposed three ratios technique for the interpretation of mineral oil transformers based dissolved gas analysis

Dissolved gas analysis (DGA) of oil is one of the major diagnostic tools that are used to detect the incipient faults of oil-immersed transformers through the correlation between the dissolved gases content in transformer oil and a particular malfunction. There are several techniques of DGA to interpret the incipient transformer faults such as Doernenburg ratio, Rogers's ratio, IEC ratio and Duval triangle techniques. This study presents a new proposed technique for transformer fault diagnosis using a new three gas ratios concentration of DGA to overcome the conflict that takes place in the traditional interpretation techniques. The accuracy of the proposed diagnosis technique is verified using practical DGA data obtained from 688 samples that have been collected from different transformers of different rating and different lifespan reported by Egyptian Electricity Holding Company, IEC TC10 and related databases surveyed from actual incident cases. The comparison with traditional DGA techniques showed that the proposed DGA ratio technique has good diagnosis accuracy. It is found that the suggested three ratios technique method has 99.86% accuracy as compared with 85.67% for Duval, 75.08% for Doernenburg, 47.34% for IEC and 39% for Rogers when testing 688 cases.

Dissolved Gas Analysis (DGA) of natural ester oils under arcing faults

Dissolved gas analysis is one of diagnostic technique for monitoring the internal condition of transformer. However, DGA analyses for natural ester especially palm oil and rice bran oil still lacking. Therefore, this paper presents the investigation on natural esters regarding gassing behaviour under lightning impulse breakdown. Two type of natural ester oils were used namely Palm oil (PO) and Rice Bran oil (RBO). The DGA result showed that C2H2 and H2 detected the highest amount of gases for both samples. The gaseous obtained from the experiments were interpreted in different DGA method such as Dornenburg ratio, Roger ratio, IEC Gas method and Duval Triangle method to find the best technique for natural ester oils. The conducted experiment shows that PO and RBO have comparable dielectric performance and gassing characteristics to mineral oil, but still requiring modification of design and operating parameters .

Multi‐DGAS: A pattern based educational framework design for power transformers faults interpretation and comparative performance analysis

AbstractTaking necessary measures in advance for early prediction of the transformer failures are very crucial. Dissolved gas in oil analysis (DGA) techniques that used in electrical power systems has become an internationally accepted standard in condition based maintenance (CBM) studies have high complexity and increasing demands of the quick‐service‐ready paradigm in power transformers. This standard includes the implementation steps of various complex DGA methods named basic gas, Rogers, Doernenburg, Duval triangle, and pentagon. Mentioned methods are quite complicated and their application steps are strict rules defined in detailed procedures. The adaptation of these methods to transformer maintenance procedures that prepared according to standards and tested for correctness creates a valuable tool. In this study, a pattern based educational framework has been designed to integrate state of arts DGA techniques help for trainers, graduate researchers working on DGA techniques, and also for maintenance teams while taking critical decisions on power transformers. Developed Multi‐DGAS educational framework is designed for modular construction that can be integrated with online condition monitoring and diagnostic systems for electric power systems. Success of the proposed framework revealed with newly proposed pentagon method in regard with comparison of classical methods by using oil samples which have been taken under the energized condition of different 135 transformers within multi‐phase which failure histories already known. Analysis of dissolved gases in transformer oil is performed according to ASTM‐D 3612 procedure using 3800 GC Varian gas chromatograph apparatus.

Investigate Transformer Fault Diagnosis Performance of Dissolved Gas Analysis with Measurement Error

Dissolved gas analysis (DGA) method is an essential and efficient method for incipient fault detection of an oil-immersed power transformer. Condition assessment is often conducted by using DGA fault interpretation methods, but, little consideration has been given to the measurement uncertainty of dissolved gases. This paper is focused on the analysis of current DGA interpretation methods under a condition of reasonable measurement error. The measurement error is obtained by analysis of the accuracy of IEC/CIGRE laboratories. New variants of DGA gas records are generated by using ±10% as the measurement error rate. Duval Triangle, IEC, Roger and Doemenburg methods are re-examined with the DGA original gas records and their variants for error redundancy investigation. Three specific zones of Duval Triangle are concluded for improving its reliability performance. Shortcomings of interpretation codes in IEC, Roger and Doemenburg methods, which affect the fault detection performance, are discussed.

Bayesian Duval Triangle Method for Fault Prediction and Assessment of Oil Immersed Transformers

Dissolved gas analysis of transformer insulating oil is considered the best indicator of a transformer’s overall condition and is most widely used. In this study, a Bayesian network was developed to predict failures of electrical transformers. The Duval triangle method was used to develop the Bayesian model. The proposed prediction model represents a transformer fault prediction, possible faulty behaviors produced by this transformer (symptoms), along with results of possible dissolved gas analysis. The model essentially captures how possible faults of a transformer can manifest themselves by symptoms (gas proportions). Using our model, it is possible to produce a list of the most likely faults and a list of the most informative gas analysis. Also, the proposed approach helps to eliminate the uncertainty that could exist, regarding the fault nature due to gases trapped in the transformer, or faults that result in more simultaneous gas percentages. The model accurately provides transformer fault diagnosis and prediction ability by calculating the probability of released gases. Furthermore, it predicts failures based on their relationships in the Bayesian network. Finally, we show how the approach works for five distinct electrical transformers of a power plant, by describing the advantages of having available a Bayesian network model based on the Duval triangle method for the fault prediction tasks.