Oil-immersed Power Transformers Research Articles

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.

Simulation Analysis of Metallic Particles Movement in Typical Structure of Oil-immersed Power Transformer

The insulation performance of oil-immersed power transformers is closely involved with the movement of metal particle impurities, and the movement characteristics of metal particles are not only influenced by the electric field, but also by the oil flow. In this paper, a solid-liquid two-phase flow simulation model was constructed, and the effect of electric field on the motion of metal particles was considered. Firstly, the validity of the simulation model was verified by using the experimentally observed motion of metal particles in the horizontal oil channel between parallel flat plate electrodes, based on which the motion of metal particles in the typical oil channels of a 500kV power transformer was studied. It was found that when the metal particles passed through the oil flow inlet, the number of metal particles settling increased with the increase of particle size. All the metal particles with 1mm particle size sank in the oil flow inlet during the oil migration process, it was almost impossible to enter the low, medium and high voltage winding parts. Through the middle or upper end of the high voltage winding, the smaller particles (50μm, 150μm) would easily migrate with the oil flow and move faster; the larger particles (500μm) would move slower or be more easily deposited. This research holds significant practical implications in the engineering study of metal particle motion distribution within transformers.

Mn-doped covalent organic framework (COF), graphene, and their nanocomposite (Mn@GP/COF) as sensors for oil-dissolved gases in transformer: A computational study

All things being equal! As gas pollutants continue to pose threat to life so is the emergence of sensor devices that detect and capture these gases. Herein, two of the oil-decomposed gases (C2H4 and CO) in an oil-immersed power transformer were adsorbed to examine the sensor performances of Mn-doped covalent organic framework (Mn@COF) and that of graphene (Mn@GP), as well as their nanocomposite (Mn@GP/COF). This investigation was carried out using the first principle within density functional theory (DFT), with the ωB97XD/6–311 G(d,p)/LanL2DZ method. The most negative adsorption energies are featured in the adsorption of C2H4 and CO gas molecules on Mn@GP/COF nanocomposite, showing that the gases are best adsorbed on Mn@GP/COF as compared to Mn@COF and Mn@GP surfaces. Due to the least energy gap of 0.272 eV, the CO_Mn@GP/COF complex is the most stable. Relatively small energy gap of 0.77 eV was observed in C2H4_Mn@GP/COF, indicating stability during C2H4 gas adsorption. In most cases, non-covalent nature of interactions was observed, coupled with a van der Waals interaction. In all, greater tendency to move toward, interact, and keep to the adsorbent was ascertained by the Molecular dynamic simulation. Hence, the inherent sensing ability of Mn@GP/COF nanocomposite revealed in this research would be beneficial to experimental researcher in choosing the surface as a promising candidate in engineering gas sensor devices.

Comparative study of moisture treatment techniques for mineral insulating oil

The presence of moisture is one of the factors that promote degradation of transformer insulating oils and deterioration of cellulose insulation materials in oil-immersed power transformers, which affect the lifespan of the transformers. Realizing the importance of moisture in transformer insulating oils, this study compares the effectiveness of three moisture treatment techniques nitrogen bubbling technique (NBT), molecular sieve technique (MST), and vacuum oven technique (VOT)) for mineral oil (MO). The moisture content and AC breakdown voltage of the MO samples before and after moisture treatment were measured using Karl Fischer coulometric titrator and portable oil tester, respectively, in accordance with the American Society for Testing and Materials (ASTM) D1533 and ASTM D1816 standards. The results showed that NBT is the best moisture treatment technique for the MO, where the NBT reduced 80.79% of moisture present in the oil, followed by MST and VOT, which reduced 72.87 and 42.28% of moisture, respectively. The results also showed that the AC breakdown voltage of the MO samples after moisture treatment was improved owing to the reduction in moisture content.

Research on Online Gas Volume Monitoring Technology of Intelligent Gas Relay

The gas relay is the main non-electrical protection device of the transformer, which plays an important role. Traditional detection methods can not reflect the gas relay level, and there will be gas relay false alarms in operation and maintenance. Firstly, the working principle of the gas relay was analyzed, and a non-conductive capacitance sensor was proposed to realize the real-time monitoring of gas volume in the gas relay. Then, a new online monitoring scheme for intelligent gas relays was constructed through the selection analysis and optimization design of the gas protection module and liquid-level measurement module. Based on the changing state of gas volume in the gas relay, a more accurate and timely fault diagnosis of the power transformer was realized, and the prototype processing was completed. Finally, the feasibility of the volumetric testing method was verified by comparing the two conditions of different liquid-level heights and different ambient temperatures. The proposed method is of great significance for discovering the latent faults of transformers and realizing the condition monitoring of oil-immersed power transformers, which ensures the safety of power grid equipment and personnel.

Investigating Aging Characteristics of Oil-Immersed Power Transformers' Insulation in Electrical-Thermal-Mechanical Combined Conditions.

The condition and health of large oil-immersed power transformers' insulation have a direct impact on the safety and stability of the power grid. Therefore, it is crucial to investigate the aging characteristics of oil-paper insulation in power transformers. In this study, we developed a computational model for reclosing current calculation and multiphysics coupling models for magnetic-circuit-force, electrostatic field, and temperature field simulations. The calculated aging resulted in a mechanical stress of 8.71 MPa, an electric field strength of 2.26 × 106 V/m, and a temperature of 113.7 °C. We conducted combined electrical-thermal-mechanical aging tests on the oil-paper insulation and measured various insulating paper performance parameters at different aging stages. Our study revealed that both the mechanical and electrical properties of the insulating paper deteriorated in both aging groups. However, the changes were more pronounced in the electrical-thermal-mechanical aging group compared to the electrical-thermal aging group, indicating that mechanical stress accelerated the aging process of the insulating paper. In the early stages of aging, the rate of performance changes in the electrical-thermal aging group was similar to that in the electrical-thermal-mechanical aging group. However, as the aging time increased, the degradation of performance induced by mechanical aging became more significant. This suggests that the insulating paper's resistance to mechanical damage, specifically short-circuit resistance, noticeably decreased after prolonged aging.

A Comparative Study of Machine Learning Classifiers in Oil-Immersed Power Transformer Fault Diagnosis

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.

A fast computational method for internal temperature field in Oil-Immersed power transformers

In order to improve the computational efficiency of the steady-state temperature rise of oil-immersed power transformer windings, this paper proposes a temperature field reduced order model(ROM) based on proper orthogonal decomposition (POD) and response surface method(RSM). The method takes the operating conditions of the transformer as input and uses the POD method as a carrier, and establishes a fast calculation model of the steady-state temperature field through RSM, thus achieving fast calculation from transformer operating data to the entire temperature field. The paper first analyzes the reduction characteristics of the POD method, and then introduces the RSM to establish the correlation between the POD modal coefficients and the transformer conditions. Compared with traditional surrogate models, this method can obtain high-precision surrogate relationships with a small number of sample points, and the time cost of establishing is relatively low. The method aims to quickly obtain the POD modal coefficients through the winding conditions, thereby skipping the complex nonlinear calculation and efficiently reconstructing the winding temperature field with the reduced modal. The related examples show that the method has good computational accuracy and efficiency. In a 2D forced oil circulation cooling model, out of 100 test conditions, the error is not more than 2.0 K compared with the full-order calculation, and the total calculation time is only 3.13 s. Finally, based on a 35 kV natural oil circulation cooling transformer, a temperature rise test platform is constructed to verify the effectiveness of the method. The experimental results show that the calculated error of the ROM is not more than 5 K compared with the experimental results, and the single-step calculation time is only 0.73 s, which has greatly improved the computational efficiency compared with full-order calculation of the same scale. The proposed method in this study achieves a second-level calculation efficiency for the transformer winding temperature, providing a feasible solution for digital online monitoring of transformer temperatures.

Quantitative analysis and evaluation of the optimization method of transformer partial discharge ultrasonic sensor monitoring

Abstract Accurate partial discharge (PD) measurement is critical to ensure the stable operation of transformers. The ultrasonic method is a low-cost, safe, and reliable technology that is widely available and provides real-time monitoring capability. The PD ultrasonic signals propagation is complex and severely attenuated in the transformer, which greatly affects the measurement accuracy of the sensor. In order to improve the accurate monitoring of PD in complicated transformer environments, an optimization monitoring method based on sub-scene detection and quantitative analysis and evaluation is proposed in this paper. Firstly, to address this concern, a sub-scene monitoring method is designed and explores the optimal monitoring points separately. In addition, establish the partition model of an oil-immersed power transformer, and compare the ultrasonic wave propagation characteristics and sound pressure attenuation characteristics of different monitoring points. Then, analyzed by wavelet transform algorithm and Pearson correlation coefficient to determine the best monitoring point location for each scene. Finally, we further tested the proposed method through extensive experiments based on simulations, testbed, and trial deployment. The experimental results have demonstrated the feasibility and accuracy of the proposed method in transformer PD monitoring under complicated environments.

Data augmentation for fault diagnosis of oil-immersed power transformer

110 kV oil immersed transformer is a key part of the power transmission and transformation system, which determines the power quality and transmission efficiency. Its fault diagnosis can greatly reduce the maintenance cost and improve the economy. At present, the methods of transformer fault diagnosis have a strong dependence on the original data, and the size of the original data directly affects the effect of fault diagnosis. In order to change this situation and achieve higher accuracy of transformer fault diagnosis, this paper firstly uses the Conditional Variational Automatic Encoder (CVAE) composed of full connection layers to expand the original samples under each fault category. After data augmentation, the convolutional neural network (CNN) with strong feature extraction ability is selected as the classifier. Finally, the CVAE-CNN model is validated using public dataset and the result is compared to other machine learning algorithms.

Analysis of temperature characteristics of power transformer withstand short circuit shock for two seconds

The occurrence of a short-circuit fault in a transformer results in a rapid increase in the short-circuit current, and the temperature of the transformer winding rises rapidly. At present, many scholars use analytical formulas to solve for the average temperature of transformer short-circuit for two seconds, but the transient change of the temperature of transformer short-circuit for two seconds is less considered. In order to counter this phenomenon, this paper establishes a two-dimensional planar power transformer model, simulates its fluid-temperature coupling field, and obtains its temperature distribution results. In this thesis, the steady-state fluid-temperature field of 40000 kVA/110 kV oil-immersed power transformers under rated working condition is analyzed. Secondly, on the basis of static analysis, the temperature characteristics after two seconds of short circuit is analyzed numerically. Finally, by comparing the simulated value with the formula calculation value, the calculation results were verified.

Alternate and Effective Dissolved Gas Interpretation to Understand the Transformer Incipient Faults

Various techniques of fault diagnosis in oil immersed power transformers have been utilized for the last two decades and are well reported in the literature. The traditional and widely accepted dissolved gas analysis methods include IEC gas ratio code, IEEE Key gas, Doernenburg ratios and the Duval triangle. Amongst these methods, Duval triangle method is reported to be the most prominent. A new fault gas interpretation approach has been proposed in the present work. This method combines three Duval triangles with triangular membership functions evaluated empirically for diagnosing the incipient faults. Like the existing Duval triangles, five gases, namely hydrogen, methane, ethylene, ethane, and acetylene, are considered for the present study. Initially, the relevant gas percentages for the three Duval triangles are calculated and later converted into triangular fuzzy membership functions. Finally, an expert rule-based method synthesizes the outcomes into a specific main fault type or sub-fault type. Test data of 139 transformers are used to compare the proposed method with the conventional Duval triangle method. The proposed approach is simple and can potentially determine the main faults and sub-faults simultaneously based on numerical indices in a single fuzzy logic system. In addition, the proposed method has overcome the ambiguity in overlapping faults in traditional methods.

Planning and Optimizing the Replacement Strategies of Power Transformers: Literature Review

The power transformer is one of the most critical assets in power systems; therefore, planning and optimizing the economic investment for its replacement is crucial for the financial efficiency of the utility. A compilation of the main approaches reported in the literature for the replacement of oil-immersed power transformers is presented in this article. A chronological description of procedures presented in the literature for the determination of risk index, useful life evaluation, and transformer replacements is provided. Methodologies that use the theoretical basis of the degree of polymerization of the solid insulation of the units through the oxidation aging process to estimate their condition bring together the best tools currently available to achieve this objective. However, it is important and pertinent to complement these methodologies by considering the aging processes by pyrolysis and hydrolysis together and by incorporating economic analyses for appropriate replacement and management of these aged units.

An intelligence expert system for quality health monitoring of power transformers using testing attributes

Power transformers are very vital components in electrical power network. Deterioration of transformer’s insulation has significant impacts on its health condition. Hence, detailed analysis of insulation testing attributes is very important in diagnosing the transformer. It is possible with an effective decision-making system. In the present paper, a new grey relational-based fuzzy logic expert system is proposed for continuous health monitoring of power transformers. In this approach, ranking-based qualitative relational analysis is initially carried out in order to reduce large number of input attributes into three input health grades. These input grades are further integrated with a technically designed expert fuzzy rule base. Outputs of the proposed model are validated with the outputs of multi-criterion based fuzzy logic model presented in [Mharakurwa E T and Goboza R 2019 Multiparameter-based fuzzy logic health index assessment for oil-immersed power transformers Adv. Fuzzy Syst. 2019]. Testing data of 200 in-service transformers are utilised in the validation of both the models. It is observed from the comparison that similarity index of the proposed grey-fuzzy system is 92 percent. Several issues exist in regular transformer health assessment models such as the large number of rules, uncertainties, requirement of large training data, complexity issues are overcome by integrating the grey relational analysis with the fuzzy logic system. The proposed model can be easily implemented by all utilities and diagnostic experts of transformers.

State-of-the-art review on asset management methodologies for oil-immersed power transformers

Owing to their vital function and high cost, oil-immersed power transformers represent key links in electricity grids. While extensive effort has been invested by industry in developing various condition monitoring and fault diagnosis apparatuses, it is not economically feasible to apply rigorous inspection and extensive testing to all power transformer fleets in the network. The fundamental purpose of asset management is to balance cost and reliability of the system. Maintaining the reliability of power transformers involves regular condition monitoring, fault diagnosis and overall health assessment. In addition, estimating the remnant life of power transformers can help asset managers plan capital investments. By reviewing the methods used for power transformer asset management, assets integrity team can decide the most suitable maintenance strategy for each transformer in the network. This paper is aimed to present state-of-the-art review for power transformer asset management, health assessment and remnant life methodologies. While many review papers on transformer fault diagnosis have been published in the literature, not much attention was given to present a state-of-the-art review on the three modules of the transformer asset managements that include fault diagnosis, reliability assessment and life management modules. These three modules are comprehensively presented in this paper that also presents future challenges and improvement in this field of research.

Fault diagnosis of oil-immersed transformer based on MGTO-BSCN

In order to improve the accuracy and reliability of fault diagnosis of oil-immersed power transformers, a fault diagnosis method based on the Modified Artificial Gorilla Troops Optimizer (MGTO) and the Stochastic Configuration Networks with Block Increments (BSCN) is proposed. First, the original artificial gorilla troop optimization algorithm is improved, which effectively improves the convergence speed and optimization accuracy of the algorithm. Secondly, the conventional Stochastic Configuration Networks (SCN) learning methodology is modified when the fault diagnosis model is constructed. The original SCN adopts point incremental approach to gradually add hidden nodes, while BSCN adopts block increment approach to learn features. It significantly accelerates training. MGTO algorithm is used to jointly optimize regularization parameter and scale factor in BSCN model, and the fault diagnosis model with the highest accuracy is constructed. The experimental results show that the accuracy of MGTO-BSCN for transformer fault diagnosis reaches 95.9%, which is 3.5%, 9.9% and 11.7% higher than BSCN fault diagnosis models optimized by GTO, Grey Wolf Optimizer (GWO) and Particle Swarm Optimization (PSO) respectively, reflecting the superiority of MGTO algorithm. Meanwhile, the comparison with the traditional model shows that the proposed method has obvious advantages in diagnostic effect.

Thermal Distribution Characteristics Related to the Transformer Fine Structure Predicted by CFD-Based Spline Interpolation

Insulation overheating is a major cause of transformer failures, which makes it important to quickly and accurately predict the thermal distribution characteristics related to the transformer’s fine structure. In this work, a hybrid model of computational fluid dynamics (CFD)-based spline interpolation (CFD-SI model) is proposed for the real-time prediction of transformer thermal characteristics. Taking a 750-kV oil-immersed power transformer as an example, a CFD model is established from a complete conjugate heat transfer model and validated simultaneously by 24-h experiments and the empirical formulation. A group of 30 CFD simulations is employed to establish a hybrid model. Validation results of eight randomly selected cases demonstrate that this hybrid model can accurately describe heat flow distributions and predict hot-spot locations with a computational speed of 5570 times faster than the CFD model. Furthermore, a series of 42 predictions of hot spot temperatures under various scenarios are performed using this hybrid model to characterize insulation aging due to overheating, which enabled continuous management over its lifetime.

Conventional methods of dissolved gas analysis using oil-immersed power transformer for fault diagnosis: A review

Conventional methods of dissolved gas analysis using oil-immersed power transformer for fault diagnosis: A review

A review on a novel method for aging evaluation of transformer insulating paper based on methanol

AbstractThe insulation paper is a crucial factor for evaluating the insulation status of transformers. The traditional evaluation methods for insulation paper are dissolved gas component content analysis (CO and CO2) and furfural (2‐FAL) content analysis in oil. The detection principle of the former leads to its low accuracy, while the field application effect of the latter is not ideal due to the easy oxidation of furfural and low content. Methanol, result of its good stability and high production compared with other marker products (CO, CO2, 2‐FAL) has been investigated as a novel marker for aging evaluation of transformer insulating paper in recent years. The results of the investigation indicated that there is a good correlation between the content of methanol in oil and the degree of polymerization under laboratory conditions, which reflected the insulation aging state of the transformer insulating paper more accurately. This paper summarizes the current research status of methanol formation mechanism, detection methods and aging evaluation in insulating oil, and points out the key problems to be solved and development prospects, hoping to provide relevant reference for aging evaluation of oil immersed power transformer.

Effects of Thiophene Degradation on the Corrosiveness of Oil and the Properties of Oil–Paper Insulation in the Oil-Immersed Transformers

As a non-corrosive inactive sulfur, thiophene is one of the most common sulfides in mineral oil, which are retained in oil and used for improving the oxidation stability of the oil. However, thiophene may be decomposed and form low-molecular sulfide with strong corrosiveness because of rapid energy accumulation under operating conditions of the oil-immersed power transformer. Thus, this study explored the effect of thiophene degradation on the corrosiveness and properties of insulating oil under thermal and electrical aging conditions, including a case investigation of sulfur corrosion of a 500-kV electric reactor and the accelerated aging experiments of oil–paper insulation. The results showed that the degradation and pyrolysis of thiophene depend on energy accumulation induced by thermal and electric fields in the oil-immersed transformers. Under the premise of thiophene without degradation and transformation, thiophene can still delay the deterioration of insulating oil due to its high oxidation resistance. The continuous energy accumulation induced by thermal and electric fields may cause the molecular chains of thiophene molecules to break and form some corrosive low-molecular sulfides, thereby resulting in the increase of the corrosiveness of oil, the decrease of the properties of oil–paper, and posing a huge corrosion threat to the oil-immersed transformer.