Artificial Bee Colony Algorithm Based Interpretation Of Dissolved Gas Analysis

This study discusses the application of four Dissolved Gas Analysis (DGA) methods in identifying failure indicators in power transformers. Transformer failures can occur in the form of thermal and electrical failures, producing hazardous gases known as fault gases. The insulating oil used in power transformers plays a role in cooling and dissolving these dangerous gases to prevent unrestricted circulation. Determining the type and concentration of gases dissolved in the oil can provide information about transformer failure indicators. Therefore, DGA analysis is conducted to determine effective methods for identifying these failure signs. Oil samples were taken from transformers and analyzed using four different DGA methods. The DGA analysis results revealed varying levels of gas concentration. Based on the analysis results using the Key Gas Method, Roger's Ratio Method, Doernenburg's Ratio Method, and Duval's Triangle Method, it was found that the transformers experienced thermal damage. The Duval's Triangle Method analysis concluded that two transformers had overheated. Thus, the Duval's Triangle Method analysis can be considered an effective method for identifying disturbances in power transformers due to its simple yet effective calculations

DGA is the best technique which is used to identify the incipient fault of the power transformer. The recently most advanced technology used for online fault diagnosis of the transformer is dissolved gas analysis (DGA) and it is technically acceptable. There are some methods on DGA based which are used to solve this analysis are Key-Gas method, Dornenburg method, International Electro technical Commission (IEC) ratio codes, Duval triangle and Rogers's ratio method. Also, the most well-known strategies are key gas and Roger's ratio. Artificial Neural Network (ANN), Fuzzy logic (FL), Neuro-Fuzzy, Support vector machine etc are the techniques which are used to solve dissolved gas analysis. Taking the concentrations of key gases(CO, CO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> , H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> , C <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">6</sub> , C <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</sub> , C <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> & CH <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</sub> ) of different fault conditions for the same sample unit, we can be identified the fault by using various techniques. Because of the assortment of gas data and operational natures, sometimes it is difficult to identify fault by the traditional method and hence fails to diagnose. And because of that it is important to build up a preventive maintenance technique for detection of an incipient fault of transformer automatically through dissolved gas analysis in transformer insulating oil. In this paper, we focus on two methods, Roger's ratio and IEC for the fault diagnosis of transformer insulating oil with Fuzzy interface system (FIS). This diagnosis is totally based on the ratio of concentration gases generated from single or numerous faults however with same predominant nature. At the point when gases generated from two or more faults then the relation between different gases becomes too complicated and unable to match the predefined ratio codes and fails to diagnose. Hence, to avoid such kind of issues we briefly focused on fuzzy logic and carried out diagnosis using Roger's ratio and IEC ratio codes.

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

This paper investigates the accuracy and reliability of each DGA method via dissolved gases. Key Gas Method, Doernenburg Ratio Method, Rogers Ratio Method, IEC Ratio Method, and Duval Triangle Method are used to test the 100 sample units. The results obtained are Doernenburg and IEC 94% accuracy each, Duval Triangle 80% accuracy, Rogers 79% accuracy, and Key Gas 75% accuracy. The said individual ratio methods are combined to proceed with newly developed hybrid testing methods that could probably improve the existing DGA methods. The first hybrid method is a combination between Doernenburg and Rogers which produced a slight improvement of 86% accuracy. Then a combination between Doernenburg and IEC delivered a stable prediction result of 94%. It seems that Doernenburg and IEC are the most accurate DGA methods. Although both methods are combined, they still produced a constant accuracy result. While the other methods, such as Key Gas, Rogers, and Duval Triangle, do not achieve satisfactory results.

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

Early detection of the generated dissolved gases due to different electrical and thermal stresses in transformer oil will prevent the malfunction of the transformer and hence maintain the continuity of the power system network operation. Dissolved gas analysis (DGA) is widely spread technique that is very useful in detecting the incipient faults in the oil filled power transformers. There are many interpretation techniques based on DGA such as Dornenburg method, Key-Gas method, IEC Standard Code, Duval triangle and Rogers methods. The diagnostic accuracy of these techniques is very limited and a significant instability is occurring with data uncertainty. In this paper a modification will be made on the IEC code and Rogers four ratios method based on the case study dataset (320 samples) to improve the accuracy of them. The data is collected from the central chemical laboratory of Egyptian electricity utility and from literatures. The results point out the accuracy improvement using the proposed form of IEC Code and Rogers four ratios methods.

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

Power transformers are one of the most crucial as well as costly equipment in power grids. Nowadays, a set of factors like the growing load, expanding power consumption, and market liberation affect on the transformer reliability and operational lifetime. At present, numerous approaches are on hand to diagnosis and evaluate their operational conditions. Dissolved gas analysis (DGA) in the insulation oil is an efficient diagnostic scheme which can offer an essential source for transformer failure diagnosis. In this paper, five existing approaches of utilizing dissolved gas data to identify fault kind in power transformers oil are employed. The considered methods are: key gas technique, IEC 60599 technique, Doernenburg ratio scheme, Duval triangle framework, as well as Rogers ratio scheme. These fault diagnosis methods are used to interpret and validate the dissolved gas analysis (DGA) results for four case studies. The experiments are performed on four 15.75 kV/400 kV, 3-Phase, 250 MVA power transformers in a thermal power plant. The obtained DGA results reveal that all considered DGA methods have effectively detected the fault type for each considered case except Duval triangle method which has failed to discover the fault type for two cases.

Severity is the intrinsic property of transformer faults. DGA of gaseous species evolving from power transformers due to electric and thermal stresses distinguishes between types of faults. No information about severity is provided by the DGA based techniques. For the severity study of incipient faults, thermodynamics involved in their formation is considered. Energy weighted DGA (EWDGA) which uses energy differences between fault gases is used for severity prediction. In this paper, severity of faults detected by Duval triangle method has been evaluated. Prediction of fault severity by Duval triangle method limits the concept of EWDGA to EWMEA (Energy Weighted Methane Ethylene and Acetylene). EWMEA requires subset comprising of three of primary fault gases MEA(methane, ethylene and acetylene) to be energy weighed. Duval triangle also classifies the faults using the same set of gases besides showing least percentage of inaccurate results. Henceforth, EWDGA is applied to Duval triangle to further enhance its fault diagnosis capabilities. The paper also compares the unweighted and weighed counterparts of MEA gas combinations for various fault categories of Duval triangle method. Results show the higher sensitivity of these gases for high-energy faults. The concept of energy weighing has been implemented for Duval triangle by using fuzzy inference system (FIS). The FIS uses both unweighted and weighed inputs for detection of faults and their respective severities simultaneously.

In this work, Dissolved gas Analysis (DGA) has been implemented using soft computing models namely fuzzy logic and Adaptive Neuro fuzzy inference system (ANFIS). DGA has developed as an effective tool for the identification of transformer incipient faults. A number of standards and procedures have evolved over the years making DGA more reliable and user friendly. A comparative study of the two models has been developed based on their ability to circumvent the limitations of the IEC 599 standard, Rogers ratio method and Doernenburg's method. The models have been tested using a reported fault database for their diagnostic capability. Results presented in this paper clearly indicate the superiority of the ANFIS model over the fuzzy system. The ANFIS model presents a reliable system with relative high degree accuracy. ANFIS model being very simple to develop can obviate the limitations of conventional methods of transformer fault diagnosis using DGA.

Transformer condition monitoring and assessment is crucial as it can be applied to evaluate its maintenance needs. The current trend is to leave critical power transformers in service uninterrupted unless the monitored parameters indicate otherwise. This ensures optimal use of the facility and uninterrupted power supply to consumers. This paper presents the evaluation of the health status of a transformer using the dissolved gas analysis (DGA), oil testing and evidential reasoning criterion. In the analysis, the dissolved gases and oil testing parameters are first normalized and then transformed into fuzzy variables using trapezoid membership function. Then from fuzzy variables, the condition assessment is transformed into a multiple-attribute decision making (MADM) problem under an evidential reasoning (ER) framework. Two case studies are evaluated using actual field data to verify the methodology. Both demonstrate the applicability of the proposed methodology. This emerges as a good logical and systematic way of assessing the maintenance needs of a power transformer using operational data.

Fault diagnosing power transformers based on dissolved gas analysis (DGA) has become an important and a significant tool. There are a number of techniques developed for DGA that are represented extensively in literature. In practical application of DGA, there is a degree of measurement errors in obtained data. These errors are produced from inaccuracy in measurement system, environmental impact, and human errors. In the present study, it is aimed to investigate the sensitivity of different DGA techniques against measurement errors. The considered DGA techniques are IEC Ratio, Duval Triangle, and Pentagon shape. A total number of 380 actual samples were obtained from the Egyptian Electricity Network as well as published reports. Measurement errors are modeled with various levels as a percentage of original data using a random function. The three different DGA techniques are applied for error data and the corresponding diagnostic accuracy is evaluated. The techniques are compared on two stages. The first stage includes the comparison for only main faults, thermal, arcing and partial discharge. The second stage compares the diagnostic accuracy for detailed faults, such as low thermal, medium thermal, high thermal, and so on. Based on the results, the most robust technique is adopted.

A Review of Dissolved Gas Analysis in Power Transformers

In any power system or grid network transformer device use for power from one circuit to another circuit without changing the frequency with high efficiency levels. Due to number of usages Transformer protection is very important now a days for electric supply which is fault free, efficiency and to increase the transformer life cycle. This paper is a brief discussion about the concentration of different gases like CO, CO2, H2, C2H6, C2H4, C2H2 and CH4 related faults which is known as DGA analysis with the help of various classical techniques gives different conditions for the same sample unit. This paper presents MATLAB simulation of ANN and Machine learning based high accuracy design techniques for DGA analysis and the results are compared with the classical techniques like Key Gas Method, IEC Ratio method, Duval triangle Method and Rogers Ratio Method, but in this paper we have done only Duval triangle Method for Comparison using Matlab. The Simulation result of the proposed methods shows that overall DGA analysis using Machine learning algorithm is better than conventional Duval triangle method Performance.

In any power system or grid network transformer device use for power from one circuit to another circuit without changing the frequency with high efficiency levels. Due to number of usages Transformer protection is very important now a days for electric supply which is fault free, efficiency and to increase the transformer life cycle. This paper is a brief discussion about the concentration of different gases like CO, CO2, H2, C2H6, C2H4, C2H2 and CH4 related faults which is known as DGA analysis with the help of various classical techniques gives different conditions for the same sample unit. This paper presents MATLAB simulation of ANN and Machine learning based high accuracy design techniques for DGA analysis and the results are compared with the classical techniques like Key Gas Method, IEC Ratio method, Duval triangle Method and Rogers Ratio Method, but in this paper we have done only Duval triangle Method for Comparison using Matlab. The Simulation result of the proposed methods shows that overall DGA analysis using Machine learning algorithm is better than conventional Duval triangle method Performance.