Oil-immersed Transformer Research Articles

Mechanism and numerical model of bubble effect in oil-paper insulation based on microtubule model

In this paper, a numerical model of bubble evolution is established by studying the microstructure of pressboard and the physical process of bubble generation. In the model, the porous structure of fibers is equivalent to microtubules, the gases in the bubbles are classified into two categories, and the ideal gas state equation is used to describe the bubble state. Then the pressure conditions at the bubble boundary and the evaporation rate of water are taken as solution conditions to solve the physical quantities in the state equation. The numerical model provides a new way to calculate the initial temperature of bubble escape (ITBE). And the bubble evolution inside the pressboard is obtained which is hard to be observed in experiment. After that, the influences of moisture content of pressboard and cellulose aging on ITBE in the numerical model are discussed. The model results show that the expansion rate of bubble is not uniform. Bubble growth is slow in the early stage of temperature rise, when the temperature reaches a certain threshold the growth rate of bubble radius increases significantly, which is of great significance to the temperature limit on oil-immersed power transformers.

Numerical Study of Electromagnetic Loss and Heat Transfer in an Oil-Immersed Transformer

Transformer is one of the most important pieces of equipment in power system. The insulation aging and lifespan of transformer are significantly affected by hot spot distributions of internal components inside. In the present paper, the electromagnetic losses of different components and heat transfer process in a three-phase forced oil circulation transformer (400 kVA-15 kV/400 V) are numerically studied with finite element method. The leakage magnetic flux and eddy current loss density for metal components and oil tank are carefully analyzed, and the effect of metal components’ electromagnetic loss on hot spot temperature of different components and oil flow in transformer is also studied. It is found that the surface current of metal components is generated by leakage magnetic flux, and surface current density is large when leakage magnetic flux concentrates. The effect caused by relative magnetic permeability of metal components is remarkable on electromagnetic loss of metal components and oil tank, while the effect caused by relative magnetic permeability of transformer tank is relatively small. Due to the mixing of metal components on oil flow, the heat transfer of core is enhanced, its hot spot temperature is lowered, and the hot spot locations of coil and core also change. These results are meaningful for further understanding of heat transfer process in transformer and important for the optimal design of transformer.

Measurements and performance evaluations of natural ester and mineral oil-immersed identical transformers

The use of natural esters as an insulating liquid has become popular since the early 2000s. Studies in the literature emphasize the superiority of natural esters as compared to mineral oils. In these studies, the prominent properties of natural esters are presented as being environmentally friendly, safe, and delaying the aging of cellulosic insulation material. Although there is no doubt in terms of their characteristics, such as being environmentally and safe, exaggerated comments are made about the delay of aging of the paper material used in the insulation of transformer windings. This is because comparative experimental applications on mineral oil and natural esters are carried out under artificial and equal conditions provided in the laboratory environment. Whereas, performing these experiments on identical transformers will give more realistic results. In this study, two identical transformers were designed and equipped with precision fiber optic temperature sensors. Temperature differences that vary depending on the load between high voltage and low voltage windings were measured with a high degree of accuracy for both identical transformers. Besides these measurements, physical and chemical changes were analyzed on samples taken from insulation liquids. It was observed that the physical and chemical properties of the insulation liquids directly affect the operating performance of the transformers. Thanks to the differences in viscosity of insulating liquids, the heat dissipation due to losses in the windings were different for both transformers. Both heat distribution and temperature differences in the windings varied depending on the loading factors. Considering all these factors together, comprehensively evaluations were made on the aging rate of cellulosic insulation and the service life of the transformers.

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.

Experimental and Numerical Investigation of the Internal Temperature of an Oil-Immersed Power Transformer with DOFS

To accurately detect and monitor the internal temperature of an operating power transformer, the distributed optical fiber sensor (DOFS) was creatively applied inside an oil-immersed 35 kV transformer through high integration with the winding wire. On the former basis, the power transformer prototype with a completely global internal temperature sensing capability was successfully developed and it was also qualified for power grid operation through the ex-factory type tests. The internal spatially continuous temperature distribution of the operating transformer was then revealed through a heat-run test and the numerical simulation was also applied for further analysis. Hotspots of windings were continuously located and monitored (emerging at about 89%/90% height of low/high voltage winding), which were furtherly compared with the IEC calculation results. This new nondestructive internal sensing method shows a broad application prospect in the electrical equipment field. Also, the revelation of transformer internal distributed temperature can offer a solid reference for both researchers and field operation staff.

Experimental study of vibration characteristics of 110 kV oil-immersed transformer

As the voltage level and capacity of power transformer continue to rise, this results in its increasingly prominent vibration and noise problems. In this article, we study the vibration characteristics of the 110-kV oil-immersed transformer by means of experiments. Under different no-load voltages and short-circuit currents, the vibration signals in different positions and directions of the transformer are monitored respectively, and the vibration laws are analyzed. First, the vibration law of transformers is closely related to the operating conditions of transformers, which directly affects the frequency components of transformer vibration and the amplitude corresponding to different frequencies; second, it is found that the vibration law of transformers facing different directions will change under different operating conditions; and third, it summarizes the vibration of transformers in different positions under different operating conditions. These results can not only be used as the experimental basis for the theoretical research on large-scale transformer noise but also provides experimental support for the research on vibration reduction and noise reduction methods of large-scale transformer in engineering.

Indoor distribution transformers oil temperature prediction using new electro‐thermal resistance model and normal cyclic overloading strategy: an experimental case study

In the energy distribution networks, the most important and valuable equipment is oil-immersed distribution transformers. Besides, due to the key role of these transformers and their multiplicity, their lifetime monitoring is inevitable. The life of a transformer depends on the weakest solid insulation material (i.e. paper insulation). On the other hand, monitoring the transformer insulation status requires accurate information to be available about the oil temperature at every moment. Therefore, it is important to control and predict the oil temperature rise in the transformer. In this study, a new model based on fundamental heat transfer theory is proposed for thermal behaviour prediction of top oil of indoor distribution transformers using the concept of thermal resistance, namely electro-thermal resistance model (E-TRM). In E-TRM, the thermal resistance network is formed by following three-dimensional heat transfer path and assigning thermal resistance to each path. To evaluate the proposed E-TRM, the results of this model are verified with experimental results. Moreover, the E-TRM is used to predict the thermal behaviour of the indoor transformer in the overloading condition. At the end, the transformer loss of life is estimated based on the oil temperature and a normal cyclic overloading strategy is presented for overloading management.

Study of winding vibration and transmission characteristics of three-phase oil-immersed distribution transformer based on FEA

In this paper, the magnetic-structure-acoustic multi-physical field model of S13-M400/10 distribution transformer is established through the FEA software. The harmonic response analysis method is used to study the winding vibration and transmission characteristics of the transformer. The simulation results show that the flux leakage distribution of windings on each core column is mainly determined by the current flowing through these windings. The radial and axial acceleration distributions of the windings are U shape and M shape respectively along the winding height. The acceleration of B phase windings is smaller than that of A and C phase windings. The vibration of the oil tank surface is concentrated in its middle and lower part. The tank side vibration is mainly affected by the vibration of the nearest phase windings, while the tank front vibration is affected by the vibration of the three-phase windings together. This study is of great significance for further studies of the vibration characteristics of the transformer under unbalanced load and proposing the vibration reduction and noise reduction measures for the distribution transformer.

Multi-Physics Coupling Simulation of Heat Transfer in Transformer Winding

In the electric power industry, the requirements for capacity, voltage level and compactness of transformers are getting higher and higher, which lead to significant problems, such as increased loss and more serious hot spots phenomena. Based on the three-dimensional finite element method, this paper establishes the bidirectional coupling between electromagnetic field, flow field and temperature field to calculate the temperature rise of 400 kVA oil-immersed transformer at full load and compare it with the results obtained by the heat-flow unidirectional coupling method (constant loss). The results show, due to the interaction between electromagnetic field and temperature field, the coil loss density increases, but the core loss density does not change. The hot spot temperature of coil increases and the location of coil hot spot changes. The temperature and position of the core hot spot do not change. The loss density of coil decreases as the inlet velocity increases, the hot spot temperature of coil drops faster. When the inlet velocity exceeds 0.5 m/s, increasing the inlet velocity can no longer effectively promote heat exchange. With the increase of the inlet velocity, the interaction between electromagnetic field and temperature field is weakened, but it still has a great impact on the loss density of coil. The multi-physics coupling method is of great significance to improve the accuracy of transformer temperature rise calculation.

Optimization of Ethanol Detection by Automatic Headspace Method for Cellulose Insulation Aging of Oil-immersed Transformers.

The method using ethanol to evaluate the cellulose insulation aging condition of oil-immersed transformers has been proposed. At present, the dominating method for detecting ethanol in insulating oil is to use headspace–gas-chromatography–mass-spectrometry (HS-GC-MS). However, the problem of quantitative inaccuracy will be sometimes encountered in the actual detection process due to improper instrument parameter setting and improper manual operation. In this study, as an aging marker, ethanol in transformer insulating oil was separated by using VF-624 ms capillary column. The effects of gas-chromatography–mass-spectrometry (GC-MS) optimization conditions, headspace equilibrium temperature, headspace equilibrium time and standard solution preparation method on the determination of ethanol content in oil were discussed, and optimized measures were proposed. The experimental results showed that the measurement can be more accurate under the headspace temperature of 80 °C and the headspace time of 40 min, and relative standard deviation percentage (RSD%) could reach to 4.62% under this condition. It was also pointed out that, for the preparation of standard solution, the method which controlled the sampling volume of anhydrous ethanol by microliter syringe could make the peak area of ethanol chromatogram have a better linear relationship with the standard curve. Under the similar linear range, the goodness of fitting curve without diluting process could be as high as 0.9993, while the method of preparing the stock solution and diluting stepwise to obtain the fitting curve only had a goodness of 0.9910. The method was validated by standard addition recovery test, and the recovery values obtained were between 90.3% and 95.8%. The optimized method is of great significance for the measurement of ethanol dissolved in insulating oil.

Research on the temperature rise characteristics of power transformers based on fluid-solid-thermal coupling

The problems of overheating and insulation are the main influencing factors to the operation status of power transformers. Based on the physical model of a 50MVA/110kV oil-immersed self-cooling transformer, the electromagnetic, temperature and fluid field are introduced to establish a three-dimensional temperature rise model of the transformer in this thesis. The loss obtained from the electromagnetic field calculation model is taken as a heat source and input into the transformer temperature rise model. The results show that the hot spot temperature occurs in the core column and increases with the maximum load current when the single-phase ground fault occurs, and the hot spot temperature in the transformer exceeds the limit value. The temperature distribution trends of the core and high and low voltage windings under rated conditions and overload conditions are similar. In addition, the average temperature rise of the windings under overload conditions exceeds the limit value.

Power Transformer Fault Diagnosis Based on Dissolved Gas Analysis by Correlation Coefficient-DBSCAN

The transformers work in a complex environment, which makes them prone to failure. Dissolved gas analysis (DGA) is one of the most important methods for oil-immersed transformers’ internal insulation fault diagnosis. In view of the high correlation of the same fault data of transformers, this paper proposes a new method for transformers’ fault diagnosis based on correlation coefficient density clustering, which uses density clustering to extrapolate the correlation coefficient of DGA data. Firstly, we calculated the correlation coefficient of dissolved gas content in the fault transformers oil and enlarged the correlation of the same fault category by introducing the amplification coefficient, and finally we used the density clustering method to cluster diagnosis. The experimental results show that the accuracy of clustering is improved by 32.7% compared with the direct clustering judgment without using correlation coefficient, which can effectively cluster different types of transformers fault modes. This method provides a new idea for transformers fault identification, and has practical application value.

Effect of melamine modified cellulose nanocrystals on the performance of oil-immersed transformer insulation paper

It is generally believed that the service life of transformer oil-paper insulation is determined by the aging condition of transformer insulation paper. In order to further improve the anti-aging performance of transformer insulation paper, the cellulose nanocrystals (CNCs) modified by citric acid crosslinking and melamine amidation, were used to prepare the transformer insulation paper. The insulation paper containing modified CNCs was characterized with Fourier Transform Infrared Spectroscopy, X-ray diffractometry and Scanning Electron Microscopy. When the dosage of modified CNCs was 3.0 wt% at the beating degree of pulp of 35 °SR, compared with the reference insulation paper, the tensile index of insulation paper containing modified CNCs was increased by 17.30%, the AC breakdown strength in air and oil were increased by 12.95% and 40.28%, respectively. And the degree of polymerization of insulation paper containing modified CNCs was 50.50% higher than that of reference insulation paper at the aging time of 28 days. The melamine modified CNCs could improve the thermal aging performance of transformer insulation paper.

Trajectory Planning of Transformer Cleaning System Based on IRB4600 Robotic Arm

Clean with power for oil-immersed transformer by robotic arm is investigated. We establish kinematic model of IRB4600 Robotic Arm, coordinate transfer is analyzed. Space near inline and outline of transformer is treated as obstacle, surface to be sprayed is departed into several areas, and RRT algorithm is modified in form of point to multi-points to search optimal path for cleansing. Simulation and test by virtual robot platform show that scheme proposed in this paper is valid.

Combustion behavior of mineral insulating oil with addition of flame retardants

Mineral oil is the most commonly used liquid insulating material in oil-immersed transformer. Improving the fire performance of mineral oil can not only greatly reduce the probability of fire in transformer system, but also reduce the risk of fire. In this paper, the flash point tester, cone calorimeter and thermogravimetric analyzer were used to study and analyze the mineral oil and modified samples. The results show that the fire resistance and thermal performance of the modified insulating oil are improved.

Experimental Study on the Vibration Characteristics of Oil Tank Model of Oil-Immersed Transformer

The problem of vibration and noise of transformers in urban substations is more and more concerning to society. With a 110kV transformer substation as the research object, the oil tank model of the oil-immersed transformer is designed according to the principle of similitude. Through the modal test of the oil tank model, the vibration characteristics of a 110kV oil-immersed transformer model are obtained, which will help avoid the resonance frequency in the structure design of transformer oil tanks and provide measured data for the vibration and noise control design of transformers. Finally, the finite element model of transformer oil tanks is established, and the vibration characteristics of the oil tank are simulated, and consistent analysis results are obtained.

Application of WO3 Hierarchical Structures for the Detection of Dissolved Gases in Transformer Oil: A Mini Review.

Oil-immersed power transformers are considered to be one of the most crucial and expensive devices used in power systems. Hence, high-performance gas sensors have been extensively explored and are widely used for detecting fault characteristic gases dissolved in transformer oil which can be used to evaluate the working state of transformers and thus ensure the reliable operation of power grids. Hitherto, as a typical n-type metal-oxide semiconductor, tungsten trioxide (WO3) has received considerable attention due to its unique structure. Also, the requirements for high quality gas detectors were given. Based on this, considerable efforts have been made to design and fabricate more prominent WO3 based sensors with higher responses and more outstanding properties. Lots of research has focused on the synthesis of WO3 nanomaterials with different effective and controllable strategies. Meanwhile, the various morphologies of currently synthesized nanostructures from 0-D to 3-D are discussed, along with their respective beneficial characteristics. Additionally, this paper focused on the gas sensing properties and mechanisms of the WO3 based sensors, especially for the detection of fault characteristic gases. In all, the detailed analysis has contributed some beneficial guidance to the exploration on the surface morphology and special hierarchical structure of WO3 for highly sensitive detection of fault characteristic gases in oil-immersed transformers.

Fault diagnosis of oil-immersed power transformers using common vector approach

This paper considers the problem of classifying power transformer faults in the incipient stage by using dissolved gas analysis (DGA) data. To solve this problem with high accuracy, we propose to use the common vector approach (CVA) that is a successful classifier when the number of data is insufficient. The feature vector required for the training and testing phases of the CVA is established by using both raw dissolved gas analysis data and some characteristics extracted from this data. The performance of the proposed method is evaluated over DGA data sets supplied from the Turkish Electricity Transmission Company and is compared with some conventional and intelligent methods in terms of classification accuracy and training/testing duration. The achieved results show that the proposed method exhibits superior performance than that of the other methods compared in the meaning of both diagnosis accuracy and computational time. Analysis performed on the physical faults, where the transformers fault types are verified with the electrical test methods, confirms the validity and reliability of the proposed method, as well. Being free from parameter settings is another advantage of this method for using it in online oil-gas analysis applications.

HST calculation of a 10 kV oil‐immersed transformer with 3D coupled‐field method

Transformer winding hot-spot temperature (HST) is one of the important factors affecting transformer oil–paper insulation deterioration. This study presents a three-dimensional coupled electromagnetic-fluid-thermal analysis method for HST calculation in a 10 kV oil-immersed distribution transformer, the influence of the transformer internal metal structure parts on the HST of the winding is considered in the simulation. Combining electromagnetic-field calculation with no-load test and load test of the transformer provides a more accurate method to determine internal losses of the transformer. Taking those power losses as heat sources, the transformer fluid-thermal field analysis is conducted with the finite volume method. The variation of physical parameters of transformer oil with temperature is considered in the simulation. On the basis of the equivalent thermal resistance theory, the equivalent thermal conductivities of transformer windings are obtained. The simulation results deduced from the proposed method agree well with the experimental ones, which are obtained with fibre optic temperature sensors during the transformer temperature rise test, the maximum temperature difference is <3°C. The results validated the validity and accuracy of the proposed transformer HST calculation method.

SWCNTs-based MEMS gas sensor array and its pattern recognition based on deep belief networks of gases detection in oil-immersed transformers

MEMS gas sensor arrays and specially designed pattern recognition systems are the main research directions in the field of modern sensing technology in the engineering, especially in the smart sensing and monitoring of faults in large power equipment such as oil-immersed transformers. In this paper, the MEMS sensor array composed by eight SWCNTs-based (pure, OH functionalized, COOH functionalized, NH2 functionalized by ethylenediamine, NH2 functionalized by aniline, Ni-coated, Pd-doped, ZnO-doped) sensing units was palced in the fault characteristic gases (H2, CO, and C2H2) of oil-immersed transformers, and their gas-sensing characteristics were tested in single and mixed gas atmosphere. Combined with the DBN-DNN pattern recognition method, the qualitative identification and quantitative analysis of the sensor array in a mixed gas atmosphere was realized, and the accuracy and reliability of the results are higher than the traditional BPNN model.