Large 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.

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.

FEM Simulation and Test Verification of PD Ultrasonic Signal Propagation in a Power Transformer Model

Ultrasonic signals will be generated when partial discharge occurs in internal insulation faults in large oil immersed power transformers: because the ultrasonic signal has strong anti-interference ability and has no direct electromagnetic contact with the equipment, it is widely used in transformer fault detection and positioning. In this paper, the finite element method (FEM) is used to simulate the ultrasonic signal in a 35 kV power transformer. The influence of transformer case on ultrasonic signal propagation is considered, and the propagation law of the ultrasonic signal inside the transformer is obtained. Fabry–Perot (F–P) fibre acoustic sensors with a centre frequency of 28 kHz were fabricated. A partial discharge detection test was carried out in a 35 kV transformer winding model using the F–P sensors. The test results show that the ultrasonic waveform detected by the F–P sensors are in good agreement with the simulation results, and the propagation of the ultrasonic wave inside the transformer is verified. It lays a foundation for detecting and locating PDs in power transformer by F–P acoustic sensors.

Novel field-circuit assisted FEA of 110 kV power transformer for noise control and vibration reduction

The vibration and noise are serious problems for large oil-immersed power transformers, which directly affect the performance and stability of transformers. The no-load current, as the excitation source, is very important for accurate calculation of vibration and noise. This paper provides a novel approach based on the new field-circuit coupling model to calculate no-load current of large power transformers. For one 110 kV large oil-immersed power transformer, the multi-physics coupling problem including magnetic field, structural force field and acoustic field under alternating magnetic field is analyzed. Following the multi-physics coupling calculation, distributions of vibration and noise are obtained. To validate feasibility and applicability of the proposed method, the actual vibration and noise of transformer are measured experimentally. Finally, the simulation results are compared with experimental ones, which show better goodness of fit.

Dissolved Gas Analysis Principle-Based Intelligent Approaches to Fault Diagnosis and Decision Making for Large Oil-Immersed Power Transformers: A Survey

Compared with conventional methods of fault diagnosis for power transformers, which have defects such as imperfect encoding and too absolute encoding boundaries, this paper systematically discusses various intelligent approaches applied in fault diagnosis and decision making for large oil-immersed power transformers based on dissolved gas analysis (DGA), including expert system (EPS), artificial neural network (ANN), fuzzy theory, rough sets theory (RST), grey system theory (GST), swarm intelligence (SI) algorithms, data mining technology, machine learning (ML), and other intelligent diagnosis tools, and summarizes existing problems and solutions. From this survey, it is found that a single intelligent approach for fault diagnosis can only reflect operation status of the transformer in one particular aspect, causing various degrees of shortcomings that cannot be resolved effectively. Combined with the current research status in this field, the problems that must be addressed in DGA-based transformer fault diagnosis are identified, and the prospects for future development trends and research directions are outlined. This contribution presents a detailed and systematic survey on various intelligent approaches to faults diagnosing and decisions making of the power transformer, in which their merits and demerits are thoroughly investigated, as well as their improvement schemes and future development trends are proposed. Moreover, this paper concludes that a variety of intelligent algorithms should be combined for mutual complementation to form a hybrid fault diagnosis network, such that avoiding these algorithms falling into a local optimum. Moreover, it is necessary to improve the detection instruments so as to acquire reasonable characteristic gas data samples. The research summary, empirical generalization and analysis of predicament in this paper provide some thoughts and suggestions for the research of complex power grid in the new environment, as well as references and guidance for researchers to choose optimal approach to achieve DGA-based fault diagnosis and decision of the large oil-immersed power transformers in preventive electrical tests.

Hot Spot Temperature and Grey Target Theory-Based Dynamic Modelling for Reliability Assessment of Transformer Oil-Paper Insulation Systems: A Practical Case Study

This paper develops a novel dynamic correction method for the reliability assessment of large oil-immersed power transformers. First, with the transformer oil-paper insulation system (TOPIS) as the target of evaluation and the winding hot spot temperature (HST) as the core point, an HST-based static ageing failure model is built according to the Weibull distribution and Arrhenius reaction law, in order to describe the transformer ageing process and calculate the winding HST for obtaining the failure rate and life expectancy of TOPIS. A grey target theory based dynamic correction model is then developed, combined with the data of Dissolved Gas Analysis (DGA) in power transformer oil, in order to dynamically modify the life expectancy calculated by the built static model, such that the corresponding relationship between the state grade and life expectancy correction coefficient of TOPIS can be built. Furthermore, the life expectancy loss recovery factor is introduced to correct the life expectancy of TOPIS again. Lastly, a practical case study of an operating transformer has been undertaken, in which the failure rate curve after introducing dynamic corrections can be obtained for the reliability assessment of this transformer. The curve shows a better ability of tracking the actual reliability level of transformer, thus verifying the validity of the proposed method and providing a new way for transformer reliability assessment. This contribution presents a novel model for the reliability assessment of TOPIS, in which the DGA data, as a source of information for the dynamic correction, is processed based on the grey target theory, thus the internal faults of power transformer can be diagnosed accurately as well as its life expectancy updated in time, ensuring that the dynamic assessment values can commendably track and reflect the actual operation state of the power transformers.