Dissolved Gas-in-oil Analysis Research Articles

Intelligent Expert System for Diagnosing Faults and Assessing Quality of Power Transformer Insulation Oil by DGA Method

Accurate assessment of oil and fault diagnostics of electrical power transformer insulation for lifelong endurance are the key issues addressed in this research. The durability of a transformer is significantly determined by the quality of its insulation oil, which deteriorates over time due to temperature fluctuations and moisture content. Protecting transformers from potential failure through the early and precise diagnosis of faults and efficient assessment of oil quality during the actual conduct of the operation can avoid sizeable economic losses. The ANFIS Expert System that uses intelligent software plays an important role. The dissolved gas analysis (DGA) in oil is reliable for diagnosing faults and assessing insulation oil quality in transformers. Transformer power plant protection teams often suffer sudden breakdowns that lead to massive damage and huge financial losses. The oil in transformers must be appropriately treated to circumvent such failures. In this research, an ANFIS Expert System was used to diagnose faults and assess insulation oil status and quality in power transformers. by the Rogers ratio method depending on the DGA in oil, a suitable treatment was identified. The graphical user interface from the MATLAB environment was used and proven effective for fault diagnosis and oil quality evaluation. The training algorithm can assess oil quality according to the specifications of the IEEE standard C57-104 and the IEC standard 60599.

Research on hybrid feature selection method of power transformer based on fuzzy information entropy

The data of dissolved gas in oil analysis (DGA) is uncertain affected by the influence of transformer capacity and fault location, which makes transformer fault diagnosis model based on DGA has low accuracy. Therefore, we propose a hybrid feature selection method based on fuzzy information entropy, whereby optimizing the reasonable DGA feature parameter according to the feature information between the parameter and fault type, to reduce the influence of DGA data uncertainty on the fault diagnosis accuracy. Firstly, the characteristic relevance and redundancy functions are constructed based on fuzzy information entropy theory. Secondly, these functions are taken as the optimization objectives of binary-chaotic multi-objective particle swarm optimization algorithm(B-CMOPSO), to search for the feature subsets in the feature space composed of 46 DGA feature parameters. Then, the optimal feature subset is selected based on the simulation accuracy of ELM, SVM, Adaboost.M1 and BPNN on the feature subsets. Finally, 30 simulation experiments are carried out to compare with several multi-objective optimization algorithms, common Filter methods and common DGA feature combinations, and the rationality of the proposed method is verified by the t-test method. The results show that the 4 classifiers accuracy means is maximatily improved by 18.95%, 20.77%, 19.85% and 21.27% respectively compared with common DGA feature combinations, indicating that the optimal feature subset preserves more feature information and can effectively reduce the influence of DGA data uncertainty on diagnostic accuracy.

Overheating and discharge fault identification for novel three-element mixed oil–paper insulation system based on new characteristic gas combination parameters

To support the safe operation of the novel three-element mixed insulation oil (TEMO) transformer, gas generation behaviors of the TEMO oil–paper insulation system under overheating faults and discharge faults were investigated. Results show that that the content of H2 and C2H6 generated by TEMO–paper insulation system is much higher than that in conventional mineral oil (MO)–paper insulation system under different overheating temperatures. Under partial discharge fault, the content of H2 dissolved in oil changes distinctly, while the content of C2H2 also increases with the discharge time. Under surface flashover breakdown, AC breakdown and lightning impulse breakdown fault, the content of H2 and C2H2 changes greatly compared to other gases, while the concentration of total hydrocarbon gases in TEMO–paper insulation system is smaller than that in MO–paper insulating system after the same breakdown times. The diagnosis model of overheating and discharge faults for the TEMO–paper insulation system was proposed based on the oil dissolved gas analysis, while H2, C2H2 and C2H4 were chosen as the characteristic gases and the boundary settings were determined. The verified result for the low-temperature overheating, lightning impulse and surface flashover fault for the TEMO–paper insulation system presents that the new diagnosis model is effective.

Power Transformer Fault Diagnosis using DGA and Artificial Intelligence

Background: Power transformers are one of the most applicable electricity network devices which transmit output power of the generator to the network through increasing voltage and decreasing current. Due to high cost of such devices and cost of disconnecting device upon failure, disconnection and failure of the transformer should be avoided as much as possible. Objective: In addition, in order to increase reliability and reduce maintenance costs, such devices should be monitored constantly. Internal faults ionize and warm up oil and as a result, gases like carbon dioxide, methane, ethane, ethylene and acetylene are produced. Various methods have been proposed for diagnosing fault in power transformers where one of the most well-known methods is dissolved gas analysis (DGA). DGA in oil is one of the effective tools for diagnosing initial faults in transformers. Methods: Common fault detection methods using oil-dissolved gas analysis include Dornemburge, Duval’s triangle, IEC/IEEE standard, key gases and Rogers. In recent years, artificial intelligence like genetic algorithm, fuzzy logic and neural networks have been used to detect faults using DGA. In this paper, support vector machine (SVM) and decision tree are used to detect internal faults in power transformers. Results: By evaluation of the proposed methods, total accuracies of classifiers using SVM and decision tree were 90% and 97.5%, respectively. Conclusion: Decision tree shows better performance and it is suggested as a proper method for obtaining promising results.

Analisis Kegagalan Minyak Isolasi Pada Transformator Daya Berbasis Kandungan Gas Terlarut

Common problem in power transformers is the emergence of operational failure, whether the failure ofboth thermal and electrical failures. Thermal and electrical failures generally produce hazardous gases, known asfault gas. Most power transformers that use insulating oil as a coolant also serves other than to dissolve theharmful gases in order not to circulate freely. Identify the type and amount of dissolved gas concentration in theoil, to provide information to the indication of failure that occurs in the transformer. Methods for identifying andanalyzing the gases dissolved in the oil referred to as DGA (Dissolved Gas in Oil Analysis). IEEE std. C57.104 -1991 set a Total Dissolved Combustible Gas (TDCG) of ≤ 720 part per million (ppm) as a normal condition.Having analyzed both through tools and through manual calculation, Wärtsilä transformer no. 2, with thecapacity of 2000 kVA; 20 kV / 0.4 kV; Dyn5; 3 phase, 50 Hz; brand Starlit, and PLN transformer no 5 B, with thecapacity of 2000 kVA; 20 kV / 0.4 kV; Dyn5; 3 phase, 50 Hz; Unindo brand, has exceeded a predetermined limitthe IEEE std. C57.104 - 1991, with a tool TDCG 4055 ppm for Wärtsilä transformer No. 2 and 6766 ppm fortransformer PLN no 5 B. While manually TDCG 3781 ppm for Wärtsilä transformer No. 2 and 6313 ppm for PLNtransformer no 5 B. From analysis through tools and manual calculation obtained the same results by IEEE std.C57.104 - 1991 and confirmed on condition of Wärtsilä no. 3 for Transformers no.2 and PLN transformercondition no. 4 to 5B

Classification of Fault and Failure Types Determined by Dissolved Gas Analysis for Transformers

In this study, the locations of fault and failure that can be visually identified during internal inspection are determined by classifying the fault and failure types by dissolved gas analysis (DGA). The fault types are classified by analyzing 195 internal inspected transformers that faults were identified, and the failure types are classified by analyzing 338 failed transformers of Korea Electric Power Corporation. The fault types that affected DGA in oil are classified as winding, core, clamp, bushing, on load tap changer (OLTC), and oil. The faults are classified into 18 types according to subcomponent and fault cause. The failure types that affected DGA in oil are classified as winding, bushing, and OLTC. The failures are classified into 21 types according to subcomponent and failure cause. The failure results are classified as turn-to-turn breakdown, breakdown, and oil leakage. The phenomena caused by faults and failures that can be identified by visual during internal inspection are classified as carbon, discharge, and overheat traces.

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.

Impact of Load Ramping on Power Transformer Dissolved Gas Analysis

Dissolved gas in oil analysis (DGA) is one of the most reliable condition monitoring techniques, which is currently used by the industry to detect incipient faults within the power transformers. While the technique is well matured since the development of various offline and online measurement techniques along with various interpretation methods, no much attention was given so far to the oil sampling time and its correlation with the transformer loading. A power transformer loading is subject to continuous daily and seasonal variations, which is expected to increase with the increased penetration level of renewable energy sources of intermittent characteristics, such as photovoltaic (PV) and wind energy into the current electricity grids. Generating unit transformers also undergoes similar loading variations to follow the demand, particularly in the new electricity market. As such, the insulation system within the power transformers is expected to exhibit operating temperature variations due to the continuous ramping up and down of the generation and load. If the oil is sampled for the DGA measurement during such ramping cycles, results will not be accurate, and a fault may be reported due to a gas evolution resulting from such temporarily loading variation. This paper is aimed at correlating the generation and load ramping with the DGA measurements through extensive experimental analyses. The results reveal a strong correlation between the sampling time and the generation/load ramping. The experimental results show the effect of load variations on the gas generation and demonstrate the vulnerabilities of misinterpretation of transformer faults resulting from temporary gas evolution. To achieve accurate DGA, transformer loading profile during oil sampling for the DGA measurement should be available. Based on the initial investigation in this paper, the more accurate DGA results can be achieved after a ramping down cycle of the load. This sampling time could be defined as an optimum oil sampling time for transformer DGA.

Transformer Fault Diagnosis Based on BP‐Adaboost and PNN Series Connection

Dissolved gas‐in‐oil analysis (DGA) is a powerful method to diagnose and detect transformer faults. It is of profound significance for the accurate and rapid determination of the fault of the transformer and the stability of the power. In different transformer faults, the concentration of dissolved gases in oil is also inconsistent. Commonly used gases include hydrogen (H2), methane (CH4), acetylene (C2H2), ethane (C2H6), and ethylene (C2H4). This paper first combines BP neural network with improved Adaboost algorithm, then combines PNN neural network to form a series diagnosis model for transformer fault, and finally combines dissolved gas‐in‐oil analysis to diagnose transformer fault. The experimental results show that the accuracy of the series diagnosis model proposed in this paper is greatly improved compared with BP neural network, GA‐BP neural network, PNN neural network, and BP‐Adaboost.

On-Line Analysis of Oil-Dissolved Gas in Power Transformers Using Fourier Transform Infrared Spectrometry

To address the problem of on-line dissolved gas analysis (DGA) of a power transformer, a Fourier transform infrared (FT-IR) spectrometer was used to develop an analysis instrument. Carbon monoxide (CO), carbon dioxide (CO2), methane (CH4), ethane (C2H6), ethylene (C2H4) and acetylene (C2H2) were the analytes for the FT-IR spectrometer while propane (C3H8), propylene (C3H6), propyne (C3H4), n-butane (n-C4H10) and iso-butane (iso-C4H10) were the interferents, which might exist in the dissolved gas but are not currently used as analytes for detecting an internal fault. The instrument parameters and analysis approach are first introduced. Specifically, an absorption spectra reading approach by switching two cone-type gas cells into separate light-paths was presented for reducing the effects of gas in the gaps between gas cells and spectrometers, scanning the background spectrum without clearing the sample cell, and increasing the dynamics. Then, the instrument was tested with a standard gas mixture that was extracted from insulation oil in a power transformer. The testing results show that the detection limit of every analyte component is lower than 0.1 μL/L, and the detection limits of all analytes meet the detection requirements of oil-dissolved gas analysis, which means that the FT-IR spectrometer may be an ideal instrument due to its benefits, such as being maintenance-free and having a high stability.

A Power Transformers Fault Diagnosis Model Based on Three DGA Ratios and PSO Optimization SVM

In order to make up for the shortcomings of existing transformer fault diagnosis methods in dissolved gas-in-oil analysis (DGA) feature selection and parameter optimization, a transformer fault diagnosis model based on the three DGA ratios and particle swarm optimization (PSO) optimize support vector machine (SVM) is proposed. Using transforming support vector machine to the nonlinear and multi-classification SVM, establishing the particle swarm optimization to optimize the SVM multi classification model, and conducting transformer fault diagnosis combined with the cross validation principle. The fault diagnosis results show that the average accuracy of test method is better than the standard support vector machine and genetic algorithm support vector machine, and the proposed method can effectively improve the accuracy of transformer fault diagnosis is proved.

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.

The Analysis on Temperature Raise of EHV Transformer Winding Based on the oil dissolved gas analysis Technology

The Analysis on Temperature Raise of EHV Transformer Winding Based on the oil dissolved gas analysis Technology

Power Transformer Incipient Faults Diagnosis Based on Dissolved Gas Analysis

Incipient fault diagnosis of a power transformer is greatly influenced by the condition assessment of its insulation system oil and/or paper insulation. Dissolved gas-in-oil analysis (DGA) is one of the most powerfull techniques for the detection of incipient fault condition within oil-immersed transformers. The transformer data has been analyzed using key gases, Doernenburg, Roger, IEC and Duval triangle techniques. This paper introduce a MATLAB program to help in unification DGA interpretation techniques to investigate the accuracy of these techniques in interpreting the transformer condition and to provide the best suggestion for the type of the fault within the transformer based on fault percentage. It proposes a proper maintenance action based on DGA results which is useful for planning an appropriate maintenance strategy to keep the power transformer in acceptable condition. The evaluation is carried out on DGA data obtained from 352 oil samples has been summarized into 46 samples that have been collected from a 38 different transformers of different rating and different life span.

DGA Method-Based ANFIS Expert System for Diagnosing Faults and Assessing Quality of Power Transformer Insulation Oil

<p class="zhengwen"><span lang="EN-GB">Accurate fault diagnostics and assessment of electrical power transformer insulation oil for lifelong endurance are the key issues addressed in this research. The durability of a transformer is significantly determined by the quality of its insulation oil, which deteriorates over time due to temperature fluctuations and moisture content. Protecting transformers from potential failure through early and precise diagnosis of faults and through efficient assessment of oil quality during the actual conduct of the operation can avoid sizeable economic losses. The ANFIS Expert System that uses intelligent software plays an important role in this regard. The dissolved gas analysis (DGA) in oil is a reliable method for diagnosing faults and assessing insulation oil quality in transformers. The safeguarding teams of transformer power stations often suffer from the occurrence of sudden faults, which result in severe damages and heavy monetary loss. The oil in transformers must be appropriately treated to circumvent such failures. In this research, an ANFIS Expert System was used to diagnose faults and to assess the status and quality of insulation oil in power transformers. A suitable treatment was identified using the Rogers ratio method depending on the DGA in oil. The graphical user interface from the MATLAB environment was used and proven effective for fault diagnosis and oil quality evaluation. The training algorithm is capable of assessing oil quality according to the specifications of the IEEE standard C57-104 and the IEC standard 60599.</span></p>

Power Transformer Incipient Faults Diagnosis Based on Dissolved Gas Analysis

Incipient fault diagnosis of a power transformer is greatly influenced by the condition assessment of its insulation system oil and/or paper insulation. Dissolved gas-in-oil analysis (DGA) is one of the most powerfull techniques for the detection of incipient fault condition within oil-immersed transformers. The transformer data has been analyzed using key gases, Doernenburg, Roger, IEC and Duval triangle techniques. This paper introduce a MATLAB program to help in unification DGA interpretation techniques to investigate the accuracy of these techniques in interpreting the transformer condition and to provide the best suggestion for the type of the fault within the transformer based on fault percentage. It proposes a proper maintenance action based on DGA results which is useful for planning an appropriate maintenance strategy to keep the power transformer in acceptable condition. The evaluation is carried out on DGA data obtained from 352 oil samples has been summarized into 46 samples that have been collected from a 38 different transformers of different rating and different life span.

Design and evaluation of a hybrid system for detection and prediction of faults in electrical transformers

Transformers are the vital parts of an electrical grid system. A faulty transformer can destabilize the electrical supply along with the other devices of the transmission system. Due to its significant role in the system, a transformer has to be free from faults and irregularities. Dissolved Gas-in-oil Analysis (DGA) is a method that helps in diagnosing the faults present in an electrical transformer. This paper proposes a hybrid system based on Genetic Neural Computing (GNC) for analyzing and interpreting the data derived from the concentration of the dissolved gases. It is further analyzed and clustered into four subsets according to the standard C57.104 defined by IEEE using genetic algorithm (GA). The clustered data is fed to the neural network that is used to predict the different types of faults present in the transformers. The hybrid system generates the necessary decision rules to assist the system’s operator in identifying the exact fault in the transformer and its fault status. This analysis would then be helpful in performing the required maintenance check and plan for repairs.

Creeping discharge developing on vegetable-based oil / pressboard interface under AC voltage

The behavior of creeping discharges developing on the pressboard surface was investigated experimentally in palm fatty acid ester oil, natural rapeseed oil and commercial mineral oil under variable ac voltages with 60 Hz, respectively. The oilimpregnated pressboard was used as the solid dielectric and it was immersed completely into the same oil. The tungsten needle electrode was installed in the one side of pressboard surface without the counter electrode as a high voltage electrode and a grounded fine copper rod was adhered tightly in the other side of pressboard as a back side electrode. The discharge shape, streamer extension, streamer velocity and tracking at the oil/pressboard interface were observed as a function of the time and voltage by using a still camera equipped with a night viewer. Simultaneously, the discharge current and dissipated energy were measured. The gas components dissolved in oils by the creeping discharge were also examined by the dissolved gas-in-oil analysis (DGA). In this paper, it is presented that there are distinctive events due to the occurrence of creeping discharges.

Dissolved gas analysis (DGA) of natural ester insulating fluids with different chemical compositions

This study presents experimental results of the application of the dissolved gas-inoil analysis (DGA) technique to natural ester insulating fluids. The investigations covered two common types of transformer faults, discharge and overheating, and four different commercial insulating oils, one mineral and three natural esters: Biovolt A (from corn oil), Biovolt HW (from sunflower oil with high oleic acid) and Envirotemp FR3 (from soya oil). The natural esters insulating fluids (NEIFs) analyzed had different chemical compositions. Experiments were performed in the laboratory with the test oils at three different moisture levels (low, medium and high). The purpose was to see the effect of moisture content and oil composition on the quantities of fault gases dissolved in the different oil types. The overheating tests were performed by heating the oils in an oven at 150 °C. Discharges were generated using a flat plate-plate electrode arrangement immersed in oil. Five key dissolved hydrocarbon gases - hydrogen (H2), methane (CH4), ethane (C2H6), ethylene (C2H4) and acetylene (C2H2) - generated by the two fault types in NEIFs were analyzed and compared with mineral oil (MIO) fault gases. The classic Duval triangle 1, modified Duval triangle 3, Roger's ratio, Dornenburg and IEC 60599 methods were applied to predict the fault type. The findings confirmed that the direct application of classic DGA fault interpretation methods, developed for mineral oils, to non-mineral oils needs some modifications. IR and RMN spectroscopy analyses were performed to observe differences in triacylglycerol group compositions before and after heating tests.

IMPLEMENTATION OF SVM USING SEQUENTIAL MINIMAL OPTIMIZATION FOR POWER TRANSFORMER FAULT ANALYSIS USING DGA

Reliable operations of power transformers are necessary for effective transmission and distribution of power supply. During normal functions of the power transformer, distinct types of faults occurs due to insulation failure, oil aging products, overheating of windings, etc., affect the continuity of power supply thus leading to serious economic losses. To avoid interruptions in the power supply, various software fault diagnosis approaches are developed to detect faults in the power transformer and eliminate the impacts. SVM and SVM-SMO are the software fault diagnostic techniques developed in this paper for the continuous monitoring and analysis of faults in the power transformer. The SVM algorithm is faster, conceptually simple and easy to implement with better scaling properties for few training samples. The performances of SVM for large training samples are complex, subtle and difficult to implement. In order to obtain better fault diagnosis of large training data, SVM is optimized with SMO technique to achieve high interpretation accuracy in fault analysis of power transformer. The proposed methods use Dissolved Gas-in-oil Analysis (DGA) data set obtained from 500 KV main transformers of Pingguo Substation in South China Electric Power Company. DGA is an important tool for diagnosis and detection of incipient faults in the power transformers. The Gas Chromatograph (GC) is one of the traditional methods of DGA, utilized to choose the most appropriate gas signatures dissolved in transformer oil to detect types of faults in the transformer. The simulations are carried out in MATLAB software with an Intel core 3 processor with speed of 3 GHZ and 2 GB RAM PC. The results obtained by optimized SVM and SVM-SMO are compared with the existing SVM classification techniques. The test results indicate that the SVM-SMO approach significantly improve the classification accuracy and computational time for power transformer fault classification.