Faults In Oil-immersed Transformers Research Articles

DFT study of Pd-doped ZrCl2: a promising solution for dissolved gas molecule analysis in transformer oil.

The application of 2D materials for detecting dissolved gas molecules is essential for identifying faults in oil-immersed transformers. This study investigates the adsorption properties of ZrCl2 monolayer (ML) and Pd-doped ZrCl2 ML with six gas molecules (CO, CO2, CH4, C2H2, C2H4, C2H6) in transformer oil using the density functional approach. The adsorption behaviour was analysed by calculating and comparing the structures, charge transfer and adsorption energies. Additionally, the chemical interaction and electronic properties of gas molecules on ZrCl2/Pd-ZrCl2 ML are examined through PDOS (projected density of states), band structure, recovery time and work function. The pristine ZrCl2 ML has a weak interaction for all six gas molecules, whereas Pd doping on ZrCl2 ML has considerably increased the adsorption strength for C2H4, CO and C2H2 gases. The results presented in this paper offer a theoretical framework for utilizing Pd-ZrCl2 ML in monitoring transformer performance and gas detection.

Study on Thermal Characteristics of Winding under Inter-Turn Fault of Oil-Immersed Transformer

The inter-turn fault is a common fault of oil-immersed transformers. Due to the deterioration of inter-turn insulation, its resistance decreases, which leads to an increase in the winding coil current and loss, causing the winding temperature to rise. Therefore, analyzing the electrothermal characteristics of inter-turn short circuit faults in oil-immersed transformers can provide a theoretical basis for the diagnosis of inter-turn faults in transformers. This paper studies the equivalent circuit under inter-turn faults and establishes a field–circuit coupling model to calculate the current characteristics under different inter-turn short circuit faults of transformers. The distribution of coil losses under faults is calculated as thermal source excitations, and a thermal–fluid-coupled calculation model for oil-immersed transformers is established. After the experimental verification, it is found that both the errors between the winding and tank surface temperatures do not exceed the acceptable limits, validating the effectiveness of this model. Using this model to calculate the temperature distribution for various inter-turn short circuit faults in transformers reveals their hot-spot characteristics. The results indicate that under inter-turn fault conditions, the temperature at faulty turns will gradually increase towards a limit, eventually leading to coil damage. This research provides theoretical support for enhancing transformer detection and diagnostic capabilities.

Adsorption sensitivity and sensing performance of Au-doped Ti3C2O2 gas sensor for fault characteristic gases in power transformer

By analyzing the compositions and concentration of dissolved gases in power transformer oil, it is possible to effectively identify and predict the types and degrees of internal faults in oil-immersed transformers. This technology is currently an important method for fault diagnosis and prevention of large oil-immersed power transformers. This study established two different adsorption systems, namely Ti3C2O2 and Ti3C2O2 doped with Au atom, for the detection of four fault characteristic gases (CO, CO2, C2H4, CH4) in power transformer oil. The research results indicate that Ti3C2O2 exhibits inadequate adsorption capabilities for the above gases. The maximum adsorption energy is only −0.440 eV, which belongs to physical adsorption. After doping with Au atoms, the adsorption sensitivity of Ti3C2O2 for the above gases was significantly improved, with a significant increase in adsorption energy and charge transfer. The average adsorption energy has increased by 435%, and the minimum adsorption energy is also −0.660 eV. The adsorption process changed from physical adsorption to chemical adsorption. These research results provide a theoretical basis for Au-doped Ti3C2O2 gas sensor in detecting fault characteristic gases in power transformer.

A Novel Fault Diagnosis Method for a Power Transformer Based on Multi-Scale Approximate Entropy and Optimized Convolutional Networks.

Dissolved gas analysis (DGA) in transformer oil, which analyzes its gas content, is valuable for promptly detecting potential faults in oil-immersed transformers. Given the limitations of traditional transformer fault diagnostic methods, such as insufficient gas characteristic components and a high misjudgment rate for transformer faults, this study proposes a transformer fault diagnosis model based on multi-scale approximate entropy and optimized convolutional neural networks (CNNs). This study introduces an improved sparrow search algorithm (ISSA) for optimizing CNN parameters, establishing the ISSA-CNN transformer fault diagnosis model. The dissolved gas components in the transformer oil are analyzed, and the multi-scale approximate entropy of the gas content under different fault modes is calculated. The computed entropy values are then used as feature parameters for the ISSA-CNN model to derive diagnostic results. Experimental data analysis demonstrates that multi-scale approximate entropy effectively characterizes the dissolved gas components in the transformer oil, significantly improving the diagnostic efficiency. Comparative analysis with BPNN, ELM, and CNNs validates the effectiveness and superiority of the proposed ISSA-CNN diagnostic model across various evaluation metrics.

Adsorption Mechanisms of TM3 (TM = Mo, Ru, Au)-Decorated Tin Sulfide Monolayers for the Decomposition of Gas Components under Fault Conditions in Oil-Immersed Transformers.

Oil-immersed transformers play a pivotal role owing to their environmentally friendly characteristics, compact footprint, and cost-effectiveness. Ensuring the online monitoring of oil-immersed transformers is a fundamental measure to ensure the secure and stable operation of modern power systems. In this paper, metal particle cluster-doped SnS is firstly used in the adsorption and sensing of decomposition components (CO, C2H2) under fault conditions in oil-immersed transformers. The study comprehensively analyzed band structure, differential charge density, density of states, and molecular orbital theory to unveil the adsorption and sensing mechanisms of target gases. The findings suggest that the modification of metal particle clusters can enhance the surface electronic properties of single-layer SnS. In the regions of metal particle clusters and the gas-surface reaction area, electronic activity is significantly heightened, primarily attributed to the contribution of d-orbital electrons of the metal cluster structures. The modified SnS exhibits adsorption capacity in the following order: Ru3-SnS > Mo3-SnS > Au3-SnS. Additionally, the modified material demonstrates increased competitiveness for C2H2, with adsorption types falling under physical chemistry adsorption. Different metal elements exert diverse effects on the electronic distribution of the entire system, providing a theoretical foundation for the preparation of corresponding sensors. The findings in this work offer numerical insights for the further preparation and development of SnS nanosensors, concurrently shedding light on the online monitoring of faults in oil-immersed transformers.

Discernment of transformer oil stray gassing anomalies using machine learning classification techniques

This work examines the application of machine learning (ML) algorithms to evaluate dissolved gas analysis (DGA) data to quickly identify incipient faults in oil-immersed transformers (OITs). Transformers are pivotal equipment in the transmission and distribution of electrical power. The failure of a particular unit during service may interrupt a massive number of consumers and disrupt commercial activities in that area. Therefore, several monitoring techniques are proposed to ensure that the unit maintains an adequate level of functionality in addition to an extended useful lifespan. DGA is a technique commonly employed for monitoring the state of OITs. The understanding of DGA samples is conversely unsatisfactory from the perspective of evaluating incipient faults and relies mainly on the proficiency of test engineers. In the current work, a multi-classification model that is centered on ML algorithms is demonstrated to have a logical, precise, and perfect understanding of DGA. The proposed model is used to analyze 138 transformer oil (TO) samples that exhibited different stray gassing characteristics in various South African substations. The proposed model combines the design of four ML classifiers and enhances diagnosis accuracy and trust between the transformer manufacturer and power utility. Furthermore, case reports on transformer failure analysis using the proposed model, IEC 60599:2022, and Eskom (Specification—Ref: 240-75661431) standards are presented. In addition, a comparison analysis is conducted in this work against the conventional DGA approaches to validate the proposed model. The proposed model demonstrates the highest degree of accuracy of 87.7%, which was produced by Bagged Trees, followed by Fine KNN with 86.2%, and the third in rank is Quadratic SVM with 84.1%.

Simulation study on oil pressure problems caused by internal faults in oil-immersed transformers

When internal faults occur in an oil-immersed transformer, the transformer oil inside the cabinet is subjected to thermal decomposition at high temperatures or arcing and produces a mixture of hydrocarbon gases with a risk of vapor explosion. Three-dimensional models of AC 220 kV, AC 500 kV, and DC ± 800 kV oil-immersed transformers were developed for numerical simulations. For these three transformers, the pressure distribution clouds inside the tank and the pressure variation curves at different positions discharge after the occurrence of a short-circuit arc fault between winding turns and at the connection between the tank body and the bushing. It was found that within 50 ms after the fault, the transformer oil vapor expanded rapidly and created a pressure difference at the oil-gas interface. As the oil vapor pressure continued to increase, the pressure inside the tank rose suddenly and transient pressure peaks of varying degrees (up to 0.65 MPa) appeared successively at different locations inside the tank. When the transformer tank pressure limit was exceeded, the tank cracked or exploded easily under uncontrolled conditions.

Classification Method in Fault Diagnosis of Oil-Immersed Power Transformers by Considering Dissolved Gas Analysis

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.

Power transformers fault diagnosis using graph neural networks based on dissolved gas data

The Dissolved Gas Analysis (DGA) is an important way and is now extensively used to detect incipient faults in oil-immersed transformers. Some conventional methods for DGA, such as the Three-Ratio Method, usually misunderstood the gas data and failed to accurately detect multiple fault combinations. Recently, most researchers investigating DGA have utilized machine learning algorithms to improve the accuracy of fault diagnosis. The dissolved gas data are usually treated as tabular data by using machine learning algorithms to achieve better results compared to conventional methods. However, those machine learning methods ignore the internal connection of the dissolved gas data in the time dimension. Therefore, this paper adopted a new DGA method for transformer fault diagnosis based on Graph Neural Network (GNN) and Multivariate Time Series (MTS) model. The results finally showed that the model outperforms the existing machine learning baseline methods and conventional methods on the dataset.

Density Functional Theory Study of Oil-Immersed Transformers Characteristic Decomposition Products Adsorption on n ZrO2 (n = 1~2) Modified GaN Nanotubes

Oil-immersed transformers, with safe reliability and high utilization, plays an irreplaceable role in power transmission and transformation. Unavoidable insulation defects can occur that affect the safety of the power system and socio-economic, thus timely online monitoring becomes a focus of attention in the power sector. In this study, n ZrO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> (n = 1-2) modified GaN nanotubes are firstly proposed as a novel gas-sensing material for estimating the discharge faults in oil-immersed transformers through dissolved gas analysis. Density functional theory calculations are carried out to deeply explore the characteristic dissolved gases adsorption and sensing mechanisms. The density of states, charge density difference, band structures, and theoretical recovery time are analyzed to evaluate the feasibility and applicability of the gas sensor. The data shows that ZrO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> dopant provides active sites for gas adsorption. n ZrO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> -GaNNTs (n = 1-2) has limited gas sensitivity to H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> and CH <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</sub> but demonstrates considerable ability to capture 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> . Moreover, all adsorption has excellent gas-sensitive recovery properties, aiding in the rapid recycling of sensors. Meanwhile, the verification is carried out by experiments. Results in this work can be employed for practical production of n ZrO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> (n = 1-2) modified GaN nanotubes gas sensors, ultimately realizing online monitoring of oil-immersed transformer.

A novel two‐stage Dissolved Gas Analysis fault diagnosis system based semi‐supervised learning

Dissolved Gas Analysis (DGA) is an important method for oil-immersed transformer fault diagnosis. However, collecting labelled DGA data is difficult because the determination of the transformer fault is time-consuming and expensive in the transformer substation, but DGA data without labels is easier to obtain. Therefore, the paper proposed a semi-supervised two-stage diagnostic system based DGA by using less labelled samples. The two-stage system includes a novel semi-supervised feature selection based Genetic Algorithm (GA) and Support Vector Machine (SVM) model (SSL-FS-GASVM) for selecting optimal features and a novel semi-supervised transformer fault diagnosis model based improved Artificial Fish Swarm Algorithm (AFSA) and SVM (SSL-IAFSA-SVM) for optimising the SVM parameter. Finally, the performances of SSL-FS-GASVM and SSL-IAFSA-SVM models are tested and compared with traditional supervised diagnostic models combined with other optimisation methods, respectively. The results show that the proposed two-stage system works in optimising features and parameters and has strong robustness in solving small sample classification problems.

Adsorption and gas-sensing properties of C2H4, CH4, H2, H2O on metal oxides (CuO, NiO) modified SnS2 monolayer: A DFT study

Detection of oil dissolved gases is of great significance to diagnose the diverse insulation faults in oil-immersed transformers. Therefore, the Metal oxides (CuO, NiO) doped SnS2 monolayer were exploited to reveal their gas-sensing properties to the typical oil dissolved gases (C2H4, CH4, H2) and H2O. Based on density functional theory calculations, the most stable modified substrate and adsorption structures were selected according to adsorption energy and geometry optimization. In addition, the gas-sensing mechanisms were obtained through the analysis of the adsorption structure, molecular orbit, charge transfer, total density of states and projected density of states. For gases adsorption on CuO-SnS2 the adsorption capacity is ranked as: C2H4 > H2O > H2 > CH4. For NiO-SnS2 the adsorption capacity is ranked as: H2O > C2H4 > H2 > CH4. By contrast, CuO-SnS2 shows higher superiority towards C2H4 adsorption, while the adsorption energy of CH4 ,H2 and H2O on NiO-SnS2 is more prominent than that of CuO-SnS2. Considering the ameliorative electronic properties and appropriate adsorption energy, NiO-SnS2 can be a potential candidate sensor for C2H4.

Portable ppb-level acetylene photoacoustic sensor for transformer on-field measurement

Acetylene (C2H2) is considered as one of the typical decomposition gas of transformer oil to measure the severity of oil-immersed transformer fault. Quantitative measurement of trace C2H2, especially on-field measurement, has long been the center of fault diagnosis field. In this work, a ppb-level photoacoustic sensor for C2H2 was developed for transformer insulation status detection. Firstly, combined degassing method was employed to extract dissolved gas from oil. According to known Ostwald coefficient at certain temperature, the concentration of dissolved C2H2 in oil can be calculated by measuring the concentration of extracted C2H2. Then, a quantitative detection system for C2H2 based on laser photoacoustic spectroscopy was established in this work. The concentration of the extracted C2H2 can be reversed by obtained photoacoustic signal. Experimental results indicate that the detection limit of proposed system can reach ~3.4 ppb. Besides, compared with the quantitative detection results of GC, the relative error of proposed sensor could meet relevant requirements. Experimental fruitions testify the feasibility of proposed sensor for field application, and the results of this work may serve as a guide of insulation fault diagnosis for oil-immersed transformer.

Detection of Dissolved Acetylene in Power Transformer Oil Based on Photonic Crystal Fiber

Dissolved gas analysis (DGA) is an important online monitoring technique for power transformers. When there are some faults in oil-immersed transformers, insulation materials will decompose into various small-molecule gases and continually dissolve in transformer oil. In order to figure out the composition and the concentration of dissolved gases, an optical gas sensing system based on photonic crystal fiber was developed. Hollow core photonic crystal fiber (HC-PCF) used in the system played the role of an absorption gas cell. The gas diffusion time was studied theoretically and experimentally, and the result showed that by using a 1.0 m length long HC-PCF, the theoretical diffusion time for acetylene was about 3.47 hours. Various acetylene gas samples got detected. Through Allan variance analysis, the sensitivity of the proposed sensing system could reach the best with the highest signal-to-noise ratio (SNR) when the average time was 29 s, and a minimum detection limit of 4.5 ppm could be achieved. The proposed sensing system showed tremendous advantages, such as low detection limit, no cross interference, high sensitivity. This gas sensing technique has a good application prospect in DGA for power transformers.

A Refined Classification Method for Transformer Fault Diagnosis

Dissolved gas analysis is one of the most effective methods for diagnosing transformer faults. The traditional method for oil-immersed transformer fault diagnosis can only recognize several types of defects and has a low accuracy rate. In order to improve the classification effectiveness, a refined classification method for transformer fault diagnosis is proposed. It can detect more types of faults with a higher accuracy rate. The proposed method is based on the probability-output relevance vector machine, and a three-layer four-classifier model is constructed to analyse the different diagnostic results of different kinds of input data. In this model, a binomial tree is used to transfer the multi-classification problem to four binary classification problems; each classifier is a binary classifier used to distinguish the transformer type between two types of error. The proposed method is employed for analysis of 100 DGA samples consisting of characteristic gas content. The experimental result shows that this method has a high diagnostic rate and can diagnose 11 kinds of operation state.

Oil immersed transformer fault diagnosis based on cross entropy algorithm optimized support vector machine

With the increase of the voltage level of the power grid and the increase of its capacity, the probability of transformer failure is getting higher and higher. In order to discover the early latency faults of transformers, a cross-entropy algorithm was proposed to optimize the support vector machines. This method established a support vector machine classification model and used the cross-entropy algorithm to optimize the penalty factor and kernel function parameters. The transformer fault data is used to verify the classification model and compared with the test results of other algorithms. The results show that the accuracy of this algorithm for oil-immersed transformer fault diagnosis has reached 86.7%, which is higher than that of genetic algorithm and particle swarm algorithm, and iterating over 6 times of the fitness curve tends to be smooth and takes less time. After many tests, the forecast results are stable.

The behavior of different transformer oils relating to the generation of fault gases after electrical flashovers

Dissolved gas analysis is a common technique in the supervision and maintenance of transformers. It can give hints on defects and faults in oil-immersed transformers by determination and quantification of gases arising in consequence of thermal or electrical stress. Different algorithms for the assessment of transformers, which employ data obtained from dissolved gas analysis, have been published so far. However, only few investigations, dealing with the influence of the oil’s composition towards the oil’s tendency of releasing gases, exist. This research study presents a comparison of three commercially available transformer oils of different composition regarding that aspect. The results indicate that an oil with high percentages of paraffinic hydrocarbons forms gases to a lesser extent than naphthenic oils when it is exposed to electrical stress. This phenomenon can be explained either by a variable solubility of gases in the medium oil, or by the presumption that cyclic hydrocarbons are the main producers of gases.

Dissolved gases forecast to enhance oil-immersed transformer fault diagnosis with grey prediction-clustering analysis

: 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.