A New Six-Gas-Based Fault Prediction Graphical Technique in Dissolved Gas Analysis for Oil-Immersed Power Transformer

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.

A Review of Dissolved Gas Analysis in Power Transformers

With the advent of smart grids, a significant amount of data has become available about the electric infrastructure. Much of research focus has been on exploiting newly available data sources such as smart meters and phasor measurement units. This paper proposes a new class of predictive analytics that can be built to manage existing infrastructure by combining new and old data sources together. Power transformers, one of the most critical assets in the grid, are perhaps frontrunners among ‘smarter’ set of assets which have significant instrumentation already installed to monitor their operating conditions such as load, voltage, and internal oil temperature. While such advanced instrumentation enables detailed operating condition monitoring, manual measurement of dissolved gas concentration has been the primary fault diagnostic method to identify their fault modes. Dissolved gas analysis (DGA) offers great potential to diagnose fault modes in such oil-immersed transformers. This manual routine DGA, however, is costly and not free from error. Fortunately, it is understood that the loading conditions of transformers are major drivers of fault modes in oil-immersed transformers. In this paper, a predictive model is proposed to predict accumulation of dissolved gas concentration in sealed substation transformers based on its historical loading conditions. A multi-dimensional regression approach is used to predict the concentration level of each gas in real-time. Measurements from historical dissolved gas analyses are used to solve the regression problem with a robust optimization framework. The simulation results show that the forecasting of each dissolved gas based on loading characteristics is possible with high regression accuracy ranging from 84% to 97%. Thus this method can be used to optimize DGA inspection schedules as well as to provide “virtual DGA instrumentation” without the associated high cost.

Mineral oil is the most commonly used fluid for the insulation of power transformers and has excellent physicochemical and electrical properties. Dissolved gas analysis (DGA) is commonly used in transformers as a fault identification method to monitor the operating condition of the transformer and to detect faults. However, in oil-immersed power transformers, metal materials such as copper, alloys and silicon steel are used in parts such as windings and cores, which can have an impact on dissolved gases in mineral oil. The effect of the different metals present in oil-immersed transformers on the dissolved gases in the insulating oil was explored. The results show that alloys and silicon steel contribute to the production of H <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> and copper contributes to the production of CO and C <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> H <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</inf> . When the tests were carried out to 70 days, the C <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> H <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</inf> gas content in the mineral oil samples containing copper strips at 120 °C was 22.19 times that at 90 °C. This paper provides the data basis and experimental proof for investigating the effect of metals on the gas production characteristics of transformer insulating oil.

Dissolved gas analysis (DGA) is a common technology used in the on-site maintenance of oil-immersed power transformers in the power industry at present. However, when the content of dissolved gas in the oil reaches the attention value DGA method can effectively diagnose the operating state of the transformer. Due to the lack of gas production data of free gas which was detected when the faults occur, DGA method cannot timely diagnose the transformer status. To solve the above problem, an experimental platform is built for studying the free gas generation law in oil-immersed transformers under discharge faults, and the characteristic free gas information under discharge fault of transformer is obtained through the experiment. It is found that the existing DGA method cannot accurately analyze the types and severity of sudden serious insulation faults. When high-energy partial discharge fault occurred in the equipment, CO, CO2, CH4, and H2 will be collected in large quantities on the oil surface. These four gases can be used as the basis for characterizing high-energy partial discharge faults. When spark discharge occurred in the equipment, C2H6, C2H4, and C2H2 also be collected on the oil surface which can be used as a diagnostic basis for spark discharge. Moreover, it is found that the existing three-ratio method cannot be used for accurate analysis of oil free characteristic gas, so it is necessary to explore new diagnostic methods. The aim of this study is to explore the pattern of free gas production law by experiments when discharge faults occur and to provide data for a new diagnostic method.

: A method is proposed for dissolved gases forecast and fault diagnosis in oil-immersed transformers using grey prediction–clustering analysis. Incipient faults can produce hydrocarbon molecules and carbon oxides due to the thermal decomposition of mineral oil, cellulose and other solid insulation. Dissolved gas analysis is employed to detect and monitor abnormal conditions in oil-immersed power transformers. However, the procedure takes a long time to decompose overall key gases and monitor conditions. The grey prediction GM(1, 2) model uses the variant information of hydrogen to forecast the further trends of both combustible and non-combustible gases. Grey clustering analysis is applied to diagnose internal faults including thermal faults, electrical faults and faults involving cellulose degradation. Numerical tests with field gas records were conducted to show the effectiveness of the proposed model, and are easy to implement with the help of portable devices.

Fault detection in the incipient stage is necessary to avoid hazardous operating conditions and reduce outage rates in transformers. Fault-detected dissolved gas analysis is widely used to detect incipient faults in oil-immersed transformers. This paper proposes fault diagnosis transformers using an artificial neural network based on classification techniques. Data on the condition of transformer oil is assessed for dissolved gas analysis to measure the dissolved gas concentration in the transformer oil. This type of disturbance can affect the gas concentration in the transformer oil. Fault diagnosis is implemented, and fault reference is provided. The result of the NN method is more accurate than the Tree and Random Forest method, with CA and AUC values 0.800 and 0.913. This classification approach is expected to help fault diagnostics in power transformers.

This research presents the study and analysis of faults in the oil-immersed transformer. Abnormalities can be analyzed from the Dissolved Gas Analysis (DGA) test. The oil has a variety of compounds such as hydrocarbons, oxygen, nitrogen and hydrogen. The amount of gas in oil has distinctive characteristics that can indicate abnormalities in different types. This program is developed based on according to standard IEEE Std C57.104TM - 2019. This program is designed to detect for defects in oil-immersed transformers by using artificial neural networks (Artificial Neural Network: ANN). The program using MATLAB programs is prepared for maintenance planning and supports applications from basic learning to industrial sector.

Oil-immersed power transformer is the key equipment to maintain the safe and stable operation of the whole power system, it is very important to improve the accuracy of the condition assessment results to keep the power system running safely and stably. Aiming at the problems that the traditional condition evaluation model of oil immersed transformer is difficult to evaluate and the evaluation result is inaccurate. A condition assessment model of oil immersed power transformer based on fuzzy theory and combination weighting of least squares is proposed in this paper. The model combines subjective weightings with objective weightings, and uses the least square method to determine the subjective and objective weight coefficients, which reduces the error impact caused by artificial subjective determination. The final transformer condition assessment results are determined by the fuzzy theory method. Using the oil-immersed transformer test data provided by a power supply company as a sample for experimental verification and analysis. The results show that the method proposed in this paper can accurately evaluate the state of oil-immersed power transformers, which has certain practical significance in engineering.

This paper deals with the partial discharge (PD) phenomenon and its use in the detection of small bubbles in oil and application to insulation diagnosis for oil-immersed power transformers. Bubbles in the oil strongly affect the reliability of insulation of oil-immersed power transformers as well as foreign particles. As the first step of the diagnosis of discharges in oil-immersed transformers, we conducted experiments to study the PD phenomena by measuring the PD characteristics and by observing the behavior of bubbles in oil. The experimental results, show that the /spl Phi/-q patterns strongly depends on the size of bubble.

A validated online algorithm for detection of fan failures in oil-immersed power transformers

Condition assessment for critical infrastructure is a key factor for the wellbeing of the modern human. Especially for the electricity network, specific components such as oil-immersed power transformers need to be monitored for their operating condition. Classic approaches for the condition assessment of oil-immersed power transformers have been proposed in the past, such as the dissolved gases analysis and their respective concentration measurements for insulating oils. However, these approaches cannot always correctly (and in many cases not at all) classify the problems in power transformers. In the last two decades, novel approaches are implemented so as to address this problem, including artificial intelligence with neural networks being one form of algorithm. This paper focuses on the implementation of an adaptive number of layers and neural networks, aiming to increase the accuracy of the operating condition of oil-immersed power transformers. This paper also compares the use of various activation functions and different transfer functions other than the neural network implemented. The comparison incorporates the accuracy and total structure size of the neural network.

The most common fault diagnosis method for oil-immersed power transformers is dissolved gas analysis (DGA). Doernenburg ratios, Rogers ratios, IEC (International Electrotechnical Commission) ratios, and Duval's triangle are conventional DGA techniques for insulating oil in power transformers. In this study, Scikit-learn known as a popular open-source free machine learning tool for Python programming language has been used to develop different machine learning (ML) classifiers to effectively detect defects in oil-immersed power transformers. These classifiers are Decision Trees, Support Vector Machines, Gaussian Naive Bayes, k-Nearest Neighbours, Random Forests, and Multi-Layer Perceptron. The input vector of each classifier has been formed by Doernenburg ratios, Rogers ratios, IEC ratios, and CSUS (California State University Sacramento) method. After these classifiers are completely trained, unseen DGA data sets are then used to evaluate their performances. Based on a statistical analysis, the study can indicate the most effective type of the input vector and ML classifier for precisely detecting faults in power transformers.

Instabilities in energy supply caused by equipment failures, particularly in power transformers, can significantly impact efficiency and lead to shutdowns, which can affect the population. To address this, researchers have developed fault diagnosis strategies for oil-immersed power transformers using dissolved gas analysis (DGA) to enhance reliability and environmental responsibility. However, the fault diagnosis of oil-immersed power transformers has not been exhaustively investigated. There are gaps related to real scenarios with imbalanced datasets, such as the reliability and robustness of fault diagnosis modules. Strategies with more robust models increase the overall performance of the entire system. To address this issue, we propose a novel approach based on Kolmogorov-Arnold Network (KAN) for the fault diagnosis of power transformers. Our work is the first to employ a dedicated KAN in an imbalanced data real-world scenario, named KANDiag, while also applying the synthetic minority based on probabilistic distribution (SyMProD) technique for balancing the data in the fault diagnosis. Our findings reveal that this pioneering employment of KANDiag achieved the minimal value of Hamming loss-0.0323-which minimized the classification error, guaranteeing enhanced reliability for the whole system. This ground-breaking implementation of KANDiag achieved the highest value of weighted average F1-Score-96.8455%-ensuring the solidity of the approach in the real imbalanced data scenario. In addition, KANDiag gave the highest value for accuracy-96.7728%-demonstrating the robustness of the entire system. Some key outcomes revealed gains of 68.61 percentage points for KANDiag in the fault diagnosis. These advancements emphasize the efficiency and robustness of the proposed system.

The breakdown of insulation caused by partial discharge inside the transformer is an important cause of transformer failure. Ultrasonic detection is a common method for detecting and positioning partial discharge in transformers. According to the needs of rationally arranging ultrasonic sensors and optimizing the positioning algorithm, this paper carries out a simulation analysis of the propagation pattern of partial discharge ultrasonic signals inside the oil-immersed transformer. Based on the finite element simulation software, an oil-immersed transformer model was built, and different geometric structures and material parameters were designed for the transformer’s box, iron core, winding, and oil. The propagation mode of the partial discharge occurring in the oil passage between the high and low voltage windings, the high voltage winding and the outside of the winding in the transformer is analyzed, and the attenuation of the partial discharge ultrasonic signal in the transformer by the winding is studied. This article provides a certain reference for the layout and optimization of built-in ultrasonic sensors in oil-immersed power transformers.