Dissolved Gas Analysis Results Research Articles

Method of fault‐type recognition based on the dissolved gas analysis using a set of diagnostic criteria

AbstractThis paper proposes a method for recognising the fault type using a set of diagnostic criteria. Existing approaches to the interpretation of dissolved gas analysis (DGA) results usually rely on single diagnostic criteria. The proposed method solves the problems of incomplete use of diagnostic information and discrepancies in the norms and criteria for recognising the same fault inherent in the existing methods. The feature of the method is the analysis of the compliance of the DGA results of the diagnosed equipment with the ones of the equipment with a confirmed diagnosis from the fault database. This approach outperforms the existing methods in the number of faults recognised, allowing a wider range of faults to be detected, including combined faults. In addition, the method provides an opportunity to identify possible causes and localise the fault. This feature is unique and provides a significant advantage when interpreting DGA results. The proposed method demonstrates significantly higher accuracy (100%) in recognising various fault types, including combined faults, in comparison to existing methods (about 50%). The proposed method has a 60% success rate in determining the cause and location of a fault, but this figure depends on the database with faults of various types used.

Analysis Effect of Loading on the DGA Results of the UAT 2A Transformer PLTU Paiton 1&2 for Optimizing Self-Use

In this study, the analysis of the effect of loading on the DGA results of the Paiton UAT 2A transformer units 1 and 2 was carried out using the DGA (Dissolved Gas Analysis) test tool with the X-DGA kelman tool to determine the gas content contained in oil or oil. The UAT 2A transformer is a type of oil-cooled distribution transformer that uses the Nytro Libra oil type. The oil in the UAT 2A transformer is useful for insulating and cooling media so that the UAT 2A transformer does not get hot. In the oil content there is a gas content that causes transformer failure. Gas failure is called gas fault which results in thermal and electrical failure. The results of the DGA (Dissolved Gas Analysis) test analysis show that loading greatly influences the DGA test results because there are contents that are indicated to experience Low Energy Electrical Discharge, Hight Energy Electrical Discharge and Thermal Fault, namely H2, CH4, C2H4, C2H6, C2H2, CO, CO2, and H2O continue to increase due to Thermal Fault

Improvement of transformer dissolved gas analysis interpretation using j48 decision tree model

<span lang="EN-US">Dissolved gas analysis (DGA) is widely accepted as an effective method to detect incipient faults within power transformers. Gases such as hydrogen, methane, acetylene, ethylene and ethane are normally utilized to identify the transformer fault conditions. Several techniques have been developed to interpret DGA results such as the key gas method, Doernenburg, Rogers, IEC ratio-based methods, Duval Triangles, and the latest Duval Pentagon methods. However, each of these approaches depends on the experts' shared knowledge and experience rather than quantitative scientific methods, therefore different diagnoses may be reported for the same oil sample. To overcome these shortcomings, this paper proposed the use of decision tree method to interpret the transformer health condition based on DGA results. The proposed decision tree model employed three main fault gases; methane, acetylene, ethylene as inputs, and classified the transformer into eight fault conditions. The J48 algorithm is used to train and developed the decision tree model. The performance of the proposed model is validated with the pre-known condition of transformers and compared with the Duval Triangle method. Results show that the proposed model delivers better precision and accuracy in predicting transformer fault conditions compared to DTM with 81% and 69% respectively.</span>

Power transformer faults diagnosis using undestructive methods (Roger and IEC) and artificial neural network for dissolved gas analysis applied on the functional transformer in the Algerian north-eastern: a comparative study

Introduction. Nowadays, power transformer aging and failures are viewed with great attention in power transmission industry. Dissolved gas analysis (DGA) is classified among the biggest widely used methods used within the context of asset management policy to detect the incipient faults in their earlier stage in power transformers. Up to now, several procedures have been employed for the lecture of DGA results. Among these useful means, we find Key Gases, Rogers Ratios, IEC Ratios, the historical technique less used today Doernenburg Ratios, the two types of Duval Pentagons methods, several versions of the Duval Triangles method and Logarithmic Nomograph. Problem. DGA data extracted from different units in service served to verify the ability and reliability of these methods in assessing the state of health of the power transformer. Aim. An improving the quality of diagnostics of electrical power transformer by artificial neural network tools based on two conventional methods in the case of a functional power transformer at Setif province in East North of Algeria. Methodology. Design an inelegant tool for power transformer diagnosis using neural networks based on traditional methods IEC and Rogers, which allows to early detection faults, to increase the reliability, of the entire electrical energy system from transport to consumers and improve a continuity and quality of service. Results. The solution of the problem was carried out by using feed-forward back-propagation neural networks implemented in MATLAB-Simulink environment. Four real power transformers working under different environment and climate conditions such as: desert, humid, cold were taken into account. The practical results of the diagnosis of these power transformers by the DGA are presented. Practical value. The structure and specific features of power transformer winding insulation ageing and defect state diagnosis by the application of the artificial neural network (ANN) has been briefly given. MATLAB programs were then developed to automate the evaluation of each method. This paper presents another tool to review the results obtained by the delta X software widely used by the electricity company in Algeria.

Power transformer faults diagnosis using undestructive methods (Roger and IEC) and artificial neural network for dissolved gas analysis applied on the functional transformer in the Algerian north-eastern: a comparative study

Introduction. Nowadays, power transformer aging and failures are viewed with great attention in power transmission industry. Dissolved gas analysis (DGA) is classified among the biggest widely used methods used within the context of asset management policy to detect the incipient faults in their earlier stage in power transformers. Up to now, several procedures have been employed for the lecture of DGA results. Among these useful means, we find Key Gases, Rogers Ratios, IEC Ratios, the historical technique less used today Doernenburg Ratios, the two types of Duval Pentagons methods, several versions of the Duval Triangles method and Logarithmic Nomograph. Problem. DGA data extracted from different units in service served to verify the ability and reliability of these methods in assessing the state of health of the power transformer. Aim. An improving the quality of diagnostics of electrical power transformer by artificial neural network tools based on two conventional methods in the case of a functional power transformer at Sétif province in East North of Algeria. Methodology. Design an inelegant tool for power transformer diagnosis using neural networks based on traditional methods IEC and Rogers, which allows to early detection faults, to increase the reliability, of the entire electrical energy system from transport to consumers and improve a continuity and quality of service. Results. The solution of the problem was carried out by using feed-forward back-propagation neural networks implemented in MATLAB-Simulink environment. Four real power transformers working under different environment and climate conditions such as: desert, humid, cold were taken into account. The practical results of the diagnosis of these power transformers by the DGA are presented. Practical value. The structure and specific features of power transformer winding insulation ageing and defect state diagnosis by the application of the artificial neural network (ANN) has been briefly given. MATLAB programs were then developed to automate the evaluation of each method. This paper presents another tool to review the results obtained by the delta X software widely used by the electricity company in Algeria.

Identification of Stray Gassing of Dodecylbenzene in Bushings

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.

Determination of Transformer Oil Contamination from the OLTC Gases in the Power Transformers of a Distribution System Operator

Power transformers are considered to be the most important assets in power substations. Thus, their maintenance is important to ensure the reliability of the power transmission and distribution system. One of the most commonly used methods for managing the maintenance and establishing the health status of power transformers is dissolved gas analysis (DGA). The presence of acetylene in the DGA results may indicate arcing or high-temperature thermal faults in the transformer. In old transformers with an on-load tap-changer (OLTC), oil or gases can be filtered from the OLTC compartment to the transformer’s main tank. This paper presents a method for determining the transformer oil contamination from the OLTC gases in a group of power transformers for a distribution system operator (DSO) based on the application of the guides and the knowledge of experts. As a result, twenty-six out of the 175 transformers studied are defined as contaminated from the OLTC gases. In addition, this paper presents a methodology based on machine learning techniques that allows the system to determine the transformer oil contamination from the DGA results. The trained model achieves an accuracy of 99.76% in identifying oil contamination.

Power Transformer Fault Severity Estimation Based on Dissolved Gas Analysis and Energy of Fault Formation Technique

Decision making on transformer insulation condition based on the evaluated incipient faults and aging stresses has been the norm for many asset managers. Despite being the extensively applied methodology in power transformer incipient fault detection, solely dissolved gas analysis (DGA) techniques cannot quantify the detected fault severity. Fault severity is the core property in transformer maintenance rankings. This paper presents a fuzzy logic methodology in determining transformer faults and severity through use of energy of fault formation of the evolved gasses during transformer faulting event. Additionally, the energy of fault formation is a temperature-dependent factor for all the associated evolved gases. Instead of using the energy-weighted DGA, the calculated total energy of related incipient fault is used for severity determination. Severity of faults detected by fuzzy logic-based key gas method is evaluated through the use of collected data from several in-service and faulty transformers. DGA results of oil samples drawn from transformers of different specifications and age are used to validate the model. Model results show that correctly detecting fault type and its severity determination based on total energy released during faults can enhance decision-making in prioritizing maintenance of faulty transformers.

Analysis of Gas Generated in Mineral Oil, Synthetic Ester, and Natural Ester as a Consequence of Thermal Faults

The gases generated by electrical and thermal faults are similar in the case of mineral oil, synthetic ester, and natural ester. However, there are considerable differences in the amount of the generated gases and their solubility in these liquids, what should be taken into account during power transformer diagnostics by means of dissolved gas analysis (DGA) method. The aim of the research was to compare the gases generated as a result of thermal faults (250°C, 350°C, and 450°C) in mineral oil and both esters. The following gases were analyzed: hydrogen, methane, ethane, ethylene, acetylene, propane, propylene, carbon oxide, and carbon dioxide. A qualitative and quantitative analysis of these gases was carried out by means of gas chromatography. A large share of propane in the sum of the gases generated in the esters, mainly in the synthetic ester, was found. Propane and propylene in most of the gas interpretation approaches are omitted. The authors proved that in the case of synthetic ester, using propane as the key gas for thermal faults detection is justified. The obtained results can be used to improve the interpretation of the DGA results, in the case of ester liquids used in power transformers.

Using dissolved gas analysis results to detect and isolate the internal faults of power transformers by applying a fuzzy logic method

One of the most important tools for condition monitoring is the gas chromatography test of transformer oil, which is known as dissolved gas analysis (DGA). In this research, the DGA results of >3000 power transformers operating in Iran's power grid were carefully studied and from among them, the results related to transformers suspicious of being faulty were used to validate the fault detection accuracy of the presented fuzzy inference system (FIS). In most of the previously published papers, the detection and isolation of transformer faults has been based on one or two of the following parameters: absolute concentrations of free and dissolved gases in transformer oil, total dissolved combustible gases, total combustible gases, ratios of some gases to each other, and the rates of gas increase. However, in this research, most of these parameters have been used for fault detection and isolation, according to the IEC 60599 standards. Also, no attempt has been previously made to detect the decomposition of insulation papers of transformers; but the presented FIS is able to detect this fault as well. The overall performance accuracy of the presented system is F1 = 91.2%, which seems to be a suitable value.

Diagnosis of solid insulation deterioration for power transformers with dissolved gas analysis‐based time series correlation

Dissolved gas analysis (DGA) is a prevailing methodology being widely used to detect incipient faults in power transformers. Although various methods have been developed to interpret DGA results, they may sometimes fail to diagnose precisely, especially when solid insulation deterioration is involved. This study presents a time series correlation technique, in which the sampled data of dissolved gases in the transformer oil are considered as a time series and the series correlation scheme in statistics is adopted to explore and manipulate the fault information. Both the constant and variable characteristic parameters are initially chosen based on analysis in terms of frequency histograms. With quantitative correlation analysis between the constant and variable characteristic parameters, a new criterion for solid insulation diagnosis is thereby proposed, which can be used to diagnose whether solid insulation deterioration is involved in a transformer fault, as well as further distinguish the electrical faults from the thermal ones. According to the proposed technique, explorative tests regarding two power transformers have shown promising results. With regard to the 91 fault samples collected from China Power Grid, the diagnosis accuracies for electrical and thermal faults were 86.5 and 77.7%, respectively, whereas it was 61.5% for the prevailing CO2/CO criterion.

English

Dissolved Gas Analysis (DGA) (1-4) of insulation oil in an oil insulated transformer is one of the most useful techniques for the detection of the incipient faults. The various methods, such as Gas key method, Ratios method, Graphical representation method are well known to interpret DGA results. When more than one fault exists in a transformer, these methods sometimes fail to diagnose. IEC Three Ratio Method is widely used, but in many cases this method can not accurately diagnose (such as no matching, multiple faults). This paper proposes a Fuzzy Three Ratio Method; it is considered that the drawbacks of the Conventional Three Ratio Method lie in: when the ratio crosses the coding boundary, codes change sharply, but in reality the boundary should be fuzzied. This paper first propose the fuzzy membership functions for codes zero, one, two, then it transfer the conventional logic AND and OR used in IEC three-ratio method into fuzzy logic and put forward the diagnosing steps of this method. MATLAB based scripts prove that the proposed method can overcome the drawbacks of the Conventional Three Ratio Method that cannot diagnose multi-fault and no matching codes for diagnosis. In this paper a Fuzzy Logic based approach which can diagnose multiple faults is presented.

Hybrid Dissolved Gas-in-Oil Analysis Methods

Dissolved gas analysis is the most widely used diagnostic test in power transformers. The aim of this paper is to introduce the dissolved gas analysis (DGA) methods able to diagnose the transformer conditions. The faults cause the transformer oil, pressboard, and other insulating materials to decompose and generate gases, some of which dissolve in the oil. The results of DGA must be accurate if faults are to be diagnosed reliably. There are different established methods used in industry for interpreting DGA results. We will compare the result of IEEE Key Gas Methods and Rogers’ Ratios. The transformer conditions are evaluated by the Key Gas Method with total combustible gas method (TCGM) and then verified by the Rogers’ Ratios. As result, the aging pattern and trend of the power transformer deterioration can be determined. The 30 sample data from IEEE with known faults and dissolved gas concentrations were used as the basis of comparison.

A new fuzzy logic approach to identify power transformer criticality using dissolved gas-in-oil analysis

Abstract Dissolved gas analysis (DGA) of transformer oil is one of the most effective power transformer condition monitoring tools. There are many interpretation techniques for DGA results however all current techniques rely on personnel experience more than analytical formulation. As a result, the current techniques do not necessarily lead to the same conclusion for the same oil sample. A significant number of DGA results fall outside the proposed codes of the ratio-based interpretation techniques and cannot be diagnosed using these methods. Moreover, ratio methods fail to diagnose multiple fault conditions due to the mixing up of produced gases. To overcome these limitations, this paper introduces a new fuzzy logic approach that aids in standardizing DGA interpretation and identifies transformer critical ranking based on DGA data. The approach relies on incorporating all traditional DGA interpretation techniques (Roger, Doerenburg, IEC, key gas and Duval triangle methods) into one expert model. In this context, DGA results of 338 oil samples of pre-known fault conditions that were collected from different transformers of different rating and different life span are used to establish the model. Traditional DGA interpretation techniques are used first to analyze the DGA results to evaluate the consistency and accuracy of each method in identifying various faults. Results of this analysis were then used to develop the proposed fuzzy logic model. The model is validated using another set of DGA data that were collected form previously published papers.

Improvement in Transformer Diagnosis by DGA using Fuzzy Logic

Power transformer is one of the most important equipments in electrical power system. The detection of certain gases generated in transformer is the first indication of a malfunction that may lead to failure if not detected. Dissolved gas analysis (DGA) of transformer oil has been one of the most reliable techniques to detect the incipient faults. Many conventional DGA methods have been developed to interpret DGA results obtained from gas chromatography. Although these methods are widely used in the world, they sometimes fail to diagnose, especially when DGA results falls outside conventional method codes or when more than one fault exist in transformer. To overcome these limitations, fuzzy inference system (FIS) is proposed. 250 different cases are used to test the accuracy of various DGA methods in interpreting the transformer condition.

A new fuzzy logic approach for consistent interpretation of dissolved gas-in-oil analysis

Dissolved gas analysis (DGA) of transformer oil is one of the most effective power transformer condition monitoring tools. There are many interpretation techniques for DGA results however all these techniques rely on personnel experience more than analytical formulation. As a result, various interpretation techniques do not necessarily lead to the same conclusion for the same oil sample. Furthermore, significant number of DGA results fall outside the proposed codes of the current based-ratio interpretation techniques and cannot be diagnosed by these methods. Moreover, ratio methods fail to diagnose multiple fault conditions due to the mixing up of produced gases. To overcome these limitations, this paper introduces a new fuzzy logic approach to reduce dependency on expert personnel and to aid in standardizing DGA interpretation techniques. The approach relies on incorporating all existing DGA interpretation techniques into one expert model. DGA results of 2000 oil samples that were collected from different transformers of different rating and different life span are used to establish the model. Traditional DGA interpretation techniques are used to analyze the collected DGA results to evaluate the consistency and accuracy of each interpretation technique. Results of this analysis were then used to develop the proposed fuzzy logic model.

Fuzzy Algorithm for Power Transformer Diagnostics

Dissolved gas analysis (DGA) of transformer oil has been one of the most reliable techniques to detect the incipient faults. Many conventional DGA methods have been developed to interpret DGA results obtained from gas chromatography. Although these methods are widely used in the world, they sometimes fail to diagnose, especially when DGA results fall outside conventional methods codes or when more than one fault exist in the transformer. To overcome these limitations, the fuzzy inference system (FIS) is proposed. Two hundred different cases are used to test the accuracy of various DGA methods in interpreting the transformer condition.

A new approach to identify power transformer criticality and asset management decision based on dissolved gas-in-oil analysis

Dissolved gas analysis (DGA) of transformer oil is one of the most effective power transformer condition monitoring tools. There are many interpretation techniques for DGA results. However, all of these techniques rely on personnel experience more than standard mathematical formulation and significant number of DGA results fall outside the proposed codes of the current methods and cannot be diagnosed by these methods. To overcome these limitations, this paper introduces a novel approach using Gene Expression Programming (GEP) to help in standardizing DGA interpretation techniques, identify transformer critical ranking based on DGA results and propose a proper maintenance action. DGA has been performed on 338 oil samples that have been collected from different transformers of different rating and different life span. Traditional DGA interpretation techniques are used in analyzing the results to measure its consistency. These data are then used to develop the new GEP model. Results show that all current traditional techniques do not necessarily lead to the same conclusion for the same oil sample. The new approach using GEP is easy to implement and it does not call for any expert personnel to interpret the DGA results and to provide a proper asset management decision on the transformer based on DGA analysis.

Comparison of dissolved gas-in-oil analysis methods using a dissolved gas-in-oil standard

Dissolved gas analysis (DGA) is routinely used to determine the concentrations of gases dissolved in the insulating oil of transformers. These concentrations are used to investigate and diagnose electrical or thermal faults [1]. Such faults cause the transformer oil, pressboard, and other insulating materials to de compose and generate gases, some of which dissolve in the oil. The results of DGA must be accurate if faults are to be diag nosed reliably. Commercial testing laboratories understandably prefer measurements that can be made easily and quickly. In this paper, DGA results from five independent testing lab oratories are compared and discussed. A dissolved gas-in-oil standard with known dissolved gas concentrations was used as the basis of comparison.

A review of faults detectable by gas-in-oil analysis in transformers

IEC Publication 60599 provides a coded list of faults detectable by dissolved gas analysis (DGA): PD = partial discharges; D1 = discharges of low energy; D2 = discharges of high energy; T1 = thermal faults of temperature < 300/spl deg/C; T2 = thermal faults of temperature 300/spl deg/C < T < 700/spl deg/C; T3 = thermal faults of temperature > 700/spl deg/C. The IEC TC10 databases of DGA results corresponding to faults identified by visual inspection of faulty transformers in service have been presented in a previous paper (see IEEE Elec. Insulation Mag., vol. 17, no. 2, p.31-41, 2001). The present paper reviews these DGA results in a more user-friendly graphical form. It also reviews the DGA results of laboratory models attempting to simulate these faults, as published in the scientific literature or technical reports. The specific case of on-load tap changers (OLTC) is reviewed much more extensively, and separately, since DGA interpretation in this case must take into account the large background of residual gases resulting from the normal current-breaking operation of the OLTC. Particular attention is also given to DGA results related to PDs and low-temperature thermal faults.