Dissolved gas analysis

The second method for the detection of fault gases is the gas blanket analysis in which a sample of the gas in the space above the oil is analyzed for its composition. This method detects all of the individual components; however, it is also not applicable to the oil-filled conservator type units and it also suffers from the disadvantage that the gases must first diffuse into the gas blanket. In addition, this method is not at present best done in the field. A properly equipped laboratory is preferred for the required separation, identification, and quantitative determination of these gases at the part per million level. The third and most informative method for the detection of fault gases is the dissolved gas analysis ( DGA ) technique. In this method a sample of the oil is taken from the unit and the dissolved gases are extracted. Then the extracted gases are separated, identified, and quantitatively determined. At present this entire technique is best done in the laboratory since it requires

M. ALLAHBAKHSHI AND A. AKBARI ** Dept. of Electrical Engineering, K.N.Toosi University of Technology, Tehran, I. R. of Iran Email: akbari@kntu.ac.ir Abstract– Dissolved Gas Analysis (DGA) is the most reliable technique to identify the incipient faults in power transformers. There are several DGA techniques in use such as Doernenburg, Rogers, IEC, etc. On the other side there is an increasing tendency to combine data from multiple sources and models to achieve more reliable results than individuals. This investigation proposes two fusion approaches consisting of fusion architectures and respective combination methods to combine DGA techniques and the gas ratios utilized in these techniques. The proposed approaches in this article apply a modified flexible neuro-fuzzy and a gating network as combination methods.

In the transformer, insulation material and faulty equipment will result in the release of gas; hence can be attributed to some kind of electrical fault such as corona, pyrolysis, and arc. The resulting gas generation rate can indicate the severity of the offense and the information obtained can be very beneficial in any preventive maintenance program. By using any of the preventive maintenance programs, the identity of gas is very useful to determine that faults. The key gas considerations for evaluation are hydrogen (H 2 ), methane (CH 4 ), ethane (C 2 H 6 ), ethylene (C 2 H 4 ), and acetylene (C 2 H 2 ). Thus, interpretation of dissolved gas analysis (DGA) is used as the preventive maintenance program to detect the incipient faults. To study on DGA related to incipient fault inside power transformer, Rogers Ratio methods of DGA will be introduced. Rogers‟s ratio will be reviewed before it is applied in the system. In order to automate this program, the technique of MATLAB software using the technique of fuzzy logic is developed in this study. Fuzzy logic is selected because of its ability in storing knowledge and of their functions to make decision.

vii ABSTRAK Alat ubah adalah salah satu komponen yang paling penting dan mahal dalam rangkaian sistem kuasa elektrik. Permasalahan utama boleh menyebabkan kerosakan kepada alat ubah, bukan sahaja mengganggu rangkaian elektrik, tetapi juga mengakibatkan kerugian besar. Situasi akan menjadi sukar untuk mengelaskan permasalahan yang akan berlaku dalam alat ubah kuasa jika tiada analisis sesuai yang boleh digunakan. Kajian yang mendalam berkaitan “Dissolved Gas Analysis” (DGA) mendapati “Duval Triangle” dan teknik-teknik lain boleh digunakan untuk menganalisis permasalahan yang akan berlaku di dalam alat ubah kuasa. Pada masa kini, Tenaga Nasional Berhad (TNB) menggunakan perisian “Microsoft Excel” yang berkaitan dengan kaedah “Duval Triangle” untuk menganalisa alat ubah kuasa. Kajian mendapati bahawa kaedah “Duval Segitiga” memberikan keputusan 88% lebih tepat berbanding dengan teknik-teknik lain. “Microsoft Excel” adalah perisian berlesen dan pengguna perlu membeli pada harga yang tinggi untuk mendapatkan perisian berlesen penuh. Pengaturcaraan Java digunakan dalam membangunkan “Duval Triangle” baru dalam perisian Eclipse. Perisian Eclipse adalah perisian sumber terbuka dan lesen adalah percuma, maka isu lesen boleh diselesaikan. Keputusan daripada perisian “Duval Triangle” baru akan dibandingkan dengan keputusan daripada perisian yang sedia ada bagi membuktikan bahawa “Duval Triangle”

Transformer oil is one of the most common materials used for transformers. The oil has two important functions. The oil need to provide cooling and electrical insulation for the transformer. Any deterioration in the oil can lead to the premature failure of the transformer. When the mineral oil is subjected to high thermal and electrical stress, gases are generated from the decomposition of the mineral oil. Different type of faults will generate different gases, and the analysis of these gases will provide useful information about the condition of the oil and the identification of the type of fault in the transformer. The chemical analysis of these gases is called dissolved gas analysis or DGA. The DGA will require the removal of an oil sample from the transformer and this can be done without de-energization of the transformer. The oil sample is analysed in the laboratory using gas chromatography technique.

iii " I declare that this report entitle " Transformer Dissolved Gas Analysis Using Duval Triangle Method via Android " is the result of my own research except as cited in the references. The report has not been accepted for any degree and is not concurrently submitted in candidate of any other degree.

ABSTRACT Accurate diagnosis of power transformers is critical for the reliable and cost-effective operation of the power grid. Presently there are a range of methods and analytical models for transformer fault diagnosis based on dissolved gas analysis. However, these methods give conflicting results and they are not able to generate uncertainty information associated with the diagnostics outcome. In this situation it is not always clear which model is the most accurate. This paper presents a novel multiclass probabilistic diagnosis framework for dissolved gas analysis based on Bayesian networks and hypothesis testing. Bayesian network models embed expert knowledge, learn patterns from data and infer the uncertainty associated with the diagnostics outcome, and hypothesis testing aids in the data selection process. The effectiveness of the proposed framework is validated using the IEC TC 10 dataset and is shown to have a maximum diagnosis accuracy of 88.9%.

Therefore, their protection is of utmost importance so as to eliminate the chances of faults and outages and also to increase the transformer efficiency and shelf life. Transformer contains windings as its main part. To provide the insulation between these windings and also between winding as transformer body mineral oils are used as insulators. These mineral oils not only perform the insulation functions, but also act a coolant so as to absorb the heat produced. Transformer oils also provide diagnostics of the transformer health. Whenever the transformer is subjected to some fault, heat is produced inside the transformer. Different kinds of gases are generated in the transformer oil. Every fault produced its characteristic gases in the transformer oil in concentrations specific to that very fault. Therefore dissolved gas analysis is carried out to know the health status of the transformer. Detection of the specific gases indicates that the transformer is under fault. The faults indicated by dissolved gas analysis, are corrected according to the analysis. If these faults persist in the power system, the extent of damage may be enormous and also may consume lots of investment.

1 ME Student, Electrical Power System, PES COE/ Dr. B.A.M. University, (India) 2 Electrical Power System, PES COE/ Dr. B.A.M. University, India) ABSTRACT The transformer oil plays very important role to maintain healthy operating condition of transformer. When any fault occurs in the transformer due to different effects the formation of different gases takes place in the transformer oil. Dissolved Gas Analysis (DGA) has most proved accuracy method for condition assessment of power transformer. This gives prior information regarding mineral oil degradation level and generated dissolved gasses in mineral oil and concentration of dissolved gases by using Gas Chromatography. Taking the concentration of key gases (CO, CO2, CH4, C2H6, C2H2, H2 and C2H4) incident faults identified by various classical techniques gives different conditions for the same sample unit. In this work considered the point, which discussed in above line and design combine of all five diagnosis methods for better accuracy results to diagnosis of incipient faults. There are more than 6 known different methods of DGA fault interpretation technique. Every method varies according to their techniques. A series of combined interpretation methods that can determine the power transformer condition faults in one assessment is therefore needed. This paper presents to combine five DGA assessment techniques; Doernenburg Ratio Method Rogers Ratio Method, IEC Basic Ratio Method, Duval Triangle method and Key Gas Method.

Accurate diagnosis of power transformers is critical for the reliable and cost-effective operation of the power grid. Presently there are a range of methods and analytical models for transformer fault diagnosis based on dissolved gas analysis. However, these methods give conflicting results and they are not able to generate uncertainty information associated with the diagnostics outcome. In this situation it is not always clear which model is the most accurate. This paper presents a novel multiclass probabilistic diagnosis framework for dissolved gas analysis based on Bayesian networks and hypothesis testing. Bayesian network models embed expert knowledge, learn patterns from data and infer the uncertainty associated with the diagnostics outcome, and hypothesis testing aids in the data selection process. The effectiveness of the proposed framework is validated using the IEC TC 10 dataset and is shown to have a maximum diagnosis accuracy of 88.9%.

Transformer failure will cause huge loss to Industries & thus as a proactive approach transformers diagnosis of mineral oil must be carried out. Combined Dissolved gas analysis (DGA) is used for assessing oil for dissolved gases formed due to faults in the transformer. Five Classical methods in DGA are Key Gas Method, IEC Ratio method, Rogers Ratio Method, Doernenburg Ratio Method and Duval triangle Method. This paper presents Combined Dissolved Gas Analysis with five classical methods of 100% accuracy when compared to a reliable individual method of 90% accuracy.

ABSTRACT: Assessment of power transformer conditions plays crucial role to prevent incipient fault failures, to achieve reliability, efficiency and to enhance the transformer life period. Dissolved Gas Analysis (DGA) is useful for diagnostic analysis of incipient in power transformers. In this paper a novel method Artificial Neural Network (ANN) is applied to DGA for the interpretation incipient faults in power transformers. Fault interpretation can found to be a problem of multi-class classification. This paper presents ANN approach to DGA for interpretation of incipient faults in power transformers. ANN automatically tune the network parameters, connection weights and bias terms of the neural networks, to achieve the best model based on the proposed evolutionary algorithm, which provides the solution for complex classification problems. The proposed ANN algorithm applied to DGA has been tested by many real fault samples, and its results are compared with conventional DGA methods i.e. Doernenburg Ratios Method, Rogers Ratio method and IEC ratio methods. The result indicates that the proposed approach has remarkable diagnosis accuracy, and with it multiple incipient faults can be classified effectively.

ABSTRACT Power transformers are the highest value o f the equipment installed in high- voltage substations, comprising up to 60% o f total investment. There is a need for economic and financial reports to be provided to make asset decisions and ensuring balance between investment, maintenance costs and operational performance. Health index (HI) is the most common approach used in determining the condition o f the transformers. It is a tool that process information by creating a score that describe the condition o f an asset. A comparative analysis is made between HI calculation models that allow the evaluation o f the condition o f a power transformer. Through this index it is possible to objectively determine the condition o f power transformers to make maintenance or reinvestment decisions. Thus, it is possible to detect possible risk assets preventing them from failing, allowing an increase in the life time. Several studies have examined different power transformer condition assessment and life management techniques. These techniques include measuring or monitoring o f dissolved gas analysis (DGA) using Duval Triangle method. DGA technique is a reliable method and widely used to detect incipient faults which may occur in transformers such as partial discharge, thermal fault and electrical fault. This paper will focus on DGA using Duval Triangle & Pentagon method. The objective o f this paper is to compare and identify between Duval Triangle and Duval Pentagon methods which may provide more accurate interpretation o f DGA test result. This comparative study is based on real data provided by Malaysia utility company. The analysis using Duval Pentagon method give the accurate fault analysis and exactly same as the interpretation given by IEC 60599 Standard. An accurate fault analysis using Duval Pentagon M ethod give a better output o f life time prediction, types o f possible faults and recommendations for future maintenance action can be achieved.

ABSTRACT To detect transformer initial faults, DGA (Dissolved Gas Analysis) is one of the procedures which are most likely used. Among the different and diverse techniques of DGA, in this paper we have used IEC three-ratio method irrespective of not accurately diagnose in different conditions like no matching, multiple faults etc. also in conventional three-ratio method has different drawbacks like as the ratio crosses the coding boundary, codes change harshly, but in actuality the boundary should be fuzzied. Based on this opinion, this paper initially proposes the fuzzy membership functions for different codes. In this paper proposed System performance was studied via simulation results.This simulation results validated the satisfying operation of the proposed System.

This work describes a prediction model based on the dissolved gas analysis (DGA) data used as input data for the HMM to predict the deterioration level. 10 units of plant’s PTs (130 MVA, 90 MVA, 70 MVA, 87 MVA, 67.9MVA, 35 MVA, 15MVA (2 units), 3 MVA and 2 MVA) were used to provide the input to the model. The necessary algorithm is designed and implemented. The result of the life prediction of the PT is calculated using the HMM system and then compared with the real deterioration level from the result of latest DGA readings. The accuracy of PTs life estimation is discussed and for further improvements.

This paper presents methodologies for power transformer fault diagnosis using dissolved gas analysis and electrical test methods. These methods are widely used in determination of inception faults of power transformers. Dissolved gas analysis test provides fault diagnosis of power transformers. On the other hand the electrical test methods are used for detection of root causes and fault locations and they provide more specific information about the faults. The aim of this work is to study the faults that are measured and recorded in Turkish Electricity Transmission Company (TEIAS) power systems. For this purpose, four specific cases are considered and analyzed with dissolved gas analysis and electrical testing methods. Three of these cases are defective situations and one case is a non-defective situation. These real cases of measurements have been analyzed with both methods in detail. Assessment results showed that a single method cannot yield accurate enough results in some specific fault conditions.

The proposed SVM classifiers are applied to solve the practical problems of small samples and non-linear prediction better and it is suitable for the DGA in power transformers. The accuracy of an SVM model is largely dependent on the selection of the model parameters. This paper uses genetic algorithm to optimize the parameters of AUROCC-based genetic fuzzy SVM fusion model. Genetic algorithm uses selection, crossover and mutation operation to search the model parameter. Classifier fusion is to combine a set of classifiers in a certain way so that the combined classifier can receive a better performance than its composing individual classifiers. The reason that the combined classifier could outperform the best individual classifier is because the data examples misclassified by the different classifiers would not necessarily overlap, which leaves the room for the classifier complementariness. A fuzzy logic system (FLS) is constructed to combine multiple SVM classifiers in the light of the performance of each individual classifier. The memberships of the fuzzy logic system are tuned by genetic algorithms (GAs) to generate the optimal fuzzy logic system. One question here is how to evaluate classifier performance in the fusion model. Typically, accuracy is the standard criterion to evaluate a classifier performance. The Receiver Operating Characteristics (ROC) and the area under an ROC curve (AUC) have been shown to be statistically consistent with and more discriminating than accuracy empirically and theoretically. This paper will use AUC as the evaluation of classifier performance to build the genetic fuzzy fusion model to enhance the performance of SVM classifiers. Then proposed method is applied to measure the possibility and degree of aging as well as the faults occurred in the transformer. To demonstrate the validity of the proposed method, various experiments are performed and their results are presented. To demonstrate the validity of the proposed method, an experiment is performed and its results are illustrated. The objective of this paper is to develop a AUROCC-based genetic fuzzy SVM fusion model and then this model is used for Dissolved Gas analysis (DGA) in power transformer. The results compare diagnostic performance according to normal, care and healthy conditions with respect to our method and expert’s decision are discussed. Also aging degree of power transformer for insulation degradation and CO 2 excess for good, medium and low conditions are demonstrated.

in micro liters per liter, in the oil. 3. Nature, purpose, and basis for DGA for LTCs 3.1 Nature of LTC DGA Dissolved-gas analysis (DGA) for LTCs is a process of measurement, identification, and interpretation of gases dissolved in the LTC oil. Usually at regular intervals, a small sample of oil is drawn according to ASTM D923 (Section on DGA sampling), and the sample is sent to an analytical laboratory, which measures the dissolved-gas concentrations with a gas chromatograph according to ASTM D3612 and sends back a report. Alternatively, the analysis may be done on site with a portable gas analyzer instead of sending the sample to a laboratory. An on-line dissolved gas monitor attached to an LTC is another possible source of DGA data. The process of collecting the sample, transporting it, and analyzing its gas content is subject to various hazards, discussed below, which can affect the quality and usefulness of the data produced.

* Correspondence: chenglefeng_scut@163.com; taoyu1@suct.edu.cn; Tel.: +86-136-8223-6454， +86-130-0208-8518 Abstract: Compared with conventional methods of fault diagnosis for power transformers, which have defects such as imperfect encoding and too absolute encoding boundaries, this paper systematically discusses various intelligent approaches applied in fault diagnosis and decision making for large oil-immersed power transformers based on dissolved gas analysis (DGA), including expert system (EPS), artificial neural network (ANN), fuzzy theory, rough sets theory (RST), grey system theory (GST), swarm intelligence (SI) algorithms, data mining technology, machine learning (ML), and other intelligent diagnosis tools, and summarizes existing problems and solutions. From this survey, it is found that a single intelligent approach for fault diagnosis can only reflect operation status of the transformer in one particular aspect, causing various degrees of shortcomings that cannot be resolved effectively. Combined with the current research status in this field, the problems that must be addressed in DGA-based transformer fault diagnosis are identified, and the prospects for future development trends and research directions are outlined. This contribution presents a detailed and systematic survey on various intelligent approaches to faults diagnosing and decisions making of the power transformer, in which their merits and demerits are thoroughly investigated, as well as their improvement schemes and future development trends are proposed. Moreover, this paper concludes that a variety of intelligent algorithms should be combined for mutual complementation to form a hybrid fault diagnosis network, such that avoiding these algorithms falling into a local optimum. Moreover, it is necessary to improve the detection instruments so as to acquire reasonable characteristic gas data samples. The research summary, empirical generalization and analysis of predicament in this paper provide some thoughts and suggestions for the research of complex power grid in the new environment, as well as references and guidance for researchers to choose optimal approach to achieve DGA-based fault diagnosis and decision of the large oil-immersed power transformers in preventive electrical tests.

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