Journal articles: 'Oil Immersed Transformers'

1

Lakehal, Abdelaziz, and Fouad Tachi. "Bayesian Duval Triangle Method for Fault Prediction and Assessment of Oil Immersed Transformers." Measurement and Control 50, no. 4 (May 2017): 103–9. http://dx.doi.org/10.1177/0020294017707461.

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Dissolved gas analysis of transformer insulating oil is considered the best indicator of a transformer’s overall condition and is most widely used. In this study, a Bayesian network was developed to predict failures of electrical transformers. The Duval triangle method was used to develop the Bayesian model. The proposed prediction model represents a transformer fault prediction, possible faulty behaviors produced by this transformer (symptoms), along with results of possible dissolved gas analysis. The model essentially captures how possible faults of a transformer can manifest themselves by symptoms (gas proportions). Using our model, it is possible to produce a list of the most likely faults and a list of the most informative gas analysis. Also, the proposed approach helps to eliminate the uncertainty that could exist, regarding the fault nature due to gases trapped in the transformer, or faults that result in more simultaneous gas percentages. The model accurately provides transformer fault diagnosis and prediction ability by calculating the probability of released gases. Furthermore, it predicts failures based on their relationships in the Bayesian network. Finally, we show how the approach works for five distinct electrical transformers of a power plant, by describing the advantages of having available a Bayesian network model based on the Duval triangle method for the fault prediction tasks.

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Yuchao, Ma, Mo Juan, Yu Jinshan, Li Xiang, and Zheng Zhongyuan. "Study on Sound Field Distribution Rule for Tank Structures of Large Oil-immersed Transformers." E3S Web of Conferences 233 (2021): 01021. http://dx.doi.org/10.1051/e3sconf/202123301021.

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Large oil-immersed transformers are an important part of the transmission and distribution network in power systems. Power transformers are the main noise source of substations. Because of the uneven manufacturing process, aging equipment, long-term operation, and close distance from sensitive points, the problem of transformer noise pollution has become increasingly prominent. In this paper, the transmission and analysis model is established for transformer sound waves on the interface between insulating oil and tank body according to the sound wave propagation rule in complicated medium, and the simplified acoustic simulation model is constructed for large oil-immersed transformers by simulating the vibration noise of transformer core with monopole sound source, with which, the sound field distribution rule inside and outside the transformer tank structure is obtained, and finally, the influence factors for noise distribution are given. The results of the study provide control basis for reducing transformer noise.

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Li, Li, Dianhai Zhang, Zhi Wang, Yanli Zhang, Xiaopeng Fan, and Yongyan Zhou. "Novel field-circuit assisted FEA of 110 kV power transformer for noise control and vibration reduction." International Journal of Applied Electromagnetics and Mechanics 64, no. 1-4 (December 10, 2020): 289–98. http://dx.doi.org/10.3233/jae-209333.

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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.

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KROPOTIN, V. O., S. S. GIRSHIN, V. N. GORYUNOV, E. V. PETROVA, V. M. TROTSENKO, and A. O. SHEPELEV. "SIMULATION OF STATIONARY THERMAL REGIME OF OIL TRANSFORMER USING ANSYS." Actual Issues Of Energy 3, no. 1 (2021): 037–42. http://dx.doi.org/10.25206/2686-6935-2021-3-1-37-42.

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With the expansion of the electric power system, the number of distribution plants increases, the most common in which are oil-immersed transformers. the increase in the number of transformers leads to an increase in energy losses, which depend on many factors, including the temperature of the windings. at the same time, temperature is one of the most important parameters that determine the service life of a transformer. the paper discusses a digital model of the thermal regime of an oil-immersed transformer with natural cooling based on the ansys software, focused both on the tasks of calculating energy losses and on assessing the load capacity. the simulation results are compared with the heating rates. the use of thermal regime models when calculating power losses can significantly increase the accuracy of calculations.

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Mizutani, Yoshinobu. "Deterioration Diagnosis for Oil-immersed Transformers." IEEJ Transactions on Power and Energy 136, no. 4 (2016): 351–54. http://dx.doi.org/10.1541/ieejpes.136.351.

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Mharakurwa, Edwell Tafara, and Rutendo Goboza. "Multiparameter-Based Fuzzy Logic Health Index Assessment for Oil-Immersed Power Transformers." Advances in Fuzzy Systems 2019 (December 13, 2019): 1–12. http://dx.doi.org/10.1155/2019/2647157.

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The health index scheme can be the most fundamental tool that unifies all transformer condition status information into a singular outcome, thereby enhancing the power transformer asset management and life longevity strategies. This study aims at establishing a multiple parameter-dependent transformer health index estimation model cascaded with a fuzzy logic inference system. This strategy is centered on the effect of dynamic loading regime, varying hotspot temperatures and multiple attesting results of the insulation system. Furthermore, a nonintrusive degree of polymerization (DP) model based on furans and carbon oxide ratios as DP pointers is also factored in developing the health index model. The general outcome of the health index depends on entirely considered elements, but not on any isolated attribute. Data obtained from in-service transformers were used to validate the proposed model. The outcome of the model mirrors the practical condition of the evaluated transformers. Therefore, the proposed health index model can be a vital tool to asset managers and power utilities.

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Zhang, Yiyi, Jiaxi Li, Xianhao Fan, Jiefeng Liu, and Heng Zhang. "Moisture Prediction of Transformer Oil-Immersed Polymer Insulation by Applying a Support Vector Machine Combined with a Genetic Algorithm." Polymers 12, no. 7 (July 16, 2020): 1579. http://dx.doi.org/10.3390/polym12071579.

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The support vector machine (SVM) combined with the genetic algorithm (GA) has been utilized for the fault diagnosis of transformers since its high accuracy. In addition to the fault diagnosis, the condition assessment of transformer oil-immersed insulation conveys the crucial engineering significance as well. However, the approaches for getting GA-SVM used to the moisture prediction of oil-immersed insulation have been rarely reported. In view of this issue, this paper pioneers the application of GA-SVM and frequency domain spectroscopy (FDS) to realize the moisture prediction of transformer oil-immersed insulation. In the present work, a method of constructing a GA-SVM multi-classifier for moisture diagnosis based on the fitting analysis model is firstly reported. Then, the feasibility and reliability of the reported method are proved by employing the laboratory and field test experiments. The experimental results indicate that the reported prediction model might be serviced as a potential tool for the moisture prediction of transformer oil-immersed polymer insulation.

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Liu, Wei Jia, Xin Wang, Yi Hui Zheng, Li Xue Li, and Qing Shan Xu. "The Assessment of the Overload Capacity of Transformer Based on the Temperature Reverse Extrapolation Method." Advanced Materials Research 860-863 (December 2013): 2153–56. http://dx.doi.org/10.4028/www.scientific.net/amr.860-863.2153.

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The assessment of the overload capacity of transformer has a certain practical significance. In this paper, a temperature reverse extrapolation method is proposed to assess the overload capacity of transformer. Firstly, the top oil temperature is monitored by the online monitoring system. Secondly, the temperature distribution model and the calculation methods of hot spot temperature in the PTP7 (Power Transformers. Part 7: Loading guide for oil-immersed power transformers) guide are analyzed. Then, a new method called temperature reverse extrapolation which can calculate the overload factor of transformer is composed. And based on the overload factor, two meaningful data about overload capacity are obtained. Finally, an assessment system of transformer overload capacity based on the online monitoring is developed.

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Mehrabadi, Ahmad Karimi, Asaad Shemshadi, and Hossein Shateri. "An Alternative Approach for Oil-immersed High Voltage Power Transformer Dissolved Gas Analysis Diagnostic Techniques." ELEKTRIKA- Journal of Electrical Engineering 18, no. 2 (August 31, 2019): 1–7. http://dx.doi.org/10.11113/elektrika.v18n2.123.

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This article presents alternative analyzing method of extracted dissolved gases related to insulating oil of power transformers. Analysis of soluble and free gas is one of the most commonly used troubleshooting methods for detecting and evaluating equipment damage. Although the analysis of oil-soluble gases is often complex, it should be expertly processed during maintenance operation. The destruction of the transformer oil will produce some hydrocarbon type gases. The development of this index is based on two examples of traditional evaluation algorithms along with fuzzy logic inference engine. Through simulation process, the results of the initial fractures in the transformer are obtained in two ways by the "Duval Triangle method” and "Rogers’s ratios". In continue, three digit codes containing the fault information are created based on the fuzzy logic inference engine to achieve better results and eliminate ambiguous zones in commonly used methods, especially in the “Duval Triangle method”. The proposed method is applied to 80 real transformers to diagnose the fault by analyzing the dissolved oil based on fuzzy logic. The results illustrate the proficiency of this alternative proposed algorithm. Finally, with utilization of a neural network the alternative practical inference function is derived to make the algorithm more usable in the online condition monitoring of power transformers.

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Li, Guang Hua, Hong Lei Liu, and De Jian Wang. "Heat Transfer Model and Analysis of Oil-Immersed Electrical Transformers with Heat Pipe Radiator." Advanced Materials Research 516-517 (May 2012): 312–15. http://dx.doi.org/10.4028/www.scientific.net/amr.516-517.312.

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This paper has formulated a heat transfer model for analyzing the cooling properties of a heat pipe cooling device of oil-immersed electrical transformer. Based on the model, the oil temperature field of a 30 KVA oil-immersed transformer has been numerical simulated, and experiments also had been conducted. Results showed that the numerical simulation has good agreement with experiment results. Results also showed that heat pipe radiator is feasible for oil-immersed electrical transformer cooling. The model can be used to analyze the oil temperature distribution properties in an oil-immersed electrical transformer with heat pipe cooling device, and provide theoretical guide for transformer design and improvement.

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Ling, Jeeng Min, Ming Jong Lin, and Chao Tang Yu. "Transformer Fault Diagnosis with the Duval Triangle and Heuristic Techniques." Applied Mechanics and Materials 535 (February 2014): 157–61. http://dx.doi.org/10.4028/www.scientific.net/amm.535.157.

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Dissolved gas analysis (DGA) is an effective tool for detecting incipient faults in power transformers. The ANSI/IEEE C57.104 standards, the most popular guides for the interpretation of gases generated in oil-immersed transformers, and the IEC-Duval triangle method are integrated to develop the proposed power transformer fault diagnosis method. The key dissolved gases, including H2, CH4, C2H2, C2H4, C2H6, and total combustible gases (TCG), suggested by ASTM D3612s instruction for DGA is investigated. The tested data of the transformer oil were taken from the substations of Taiwan Power Company. Diagnosis results with the text form called IEC-Duval triangle method show the validation and accuracy to detect the incipient fault in the power transformer.

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12

Guo, Zheng Wei, Xiang Li Liu, Xin Ju Guo, Cun Kai Liu, Chuan Jing, Yong Li, and Min Tang. "Research on Pressure Relief Technology of Main Transformer in 220KV Indoor Substation." Applied Mechanics and Materials 873 (November 2017): 279–84. http://dx.doi.org/10.4028/www.scientific.net/amm.873.279.

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Most indoor substations adopt oil-immersed transformers as voltage transformation device. In multiple disadvantage conditions, the flammable gases formed form transformer oil may explode in transformer room. Typical 220kV main transformer room of indoor substation was researched in this paper. An correlation of pressure relief area of the 220kV mail transformer room of indoor substation were presented based on the CFD tool FLACS modeling of the general designing schemes of the State Grid Corporation of China. This research may be used as reference of the pressure relief technology for main transformer rooms of indoor substations.

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Lu, Yun Cai, Li Wei, Wei Chao, and Wu Peng. "The New Development Trend of Distribution Transformer." Applied Mechanics and Materials 672-674 (October 2014): 831–36. http://dx.doi.org/10.4028/www.scientific.net/amm.672-674.831.

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Firstly, this paper introduces the development of new materials, new technology and new manufacture in power industry of China, energy-saving, low noise and smart distribution transformers are widely used in countryside power grid reconstruction. In this paper, application status and development trend of different types of distribution transformers were introduced and compared in terms of new material and new structure, such as oil-immersed distribution transformer, amorphous core transformer(AMT), dry-type transformer, SF6 insulated distribution transformer, composite transformer and other types of distribution transformers. The development of distribution transformer is mainly based on energy saving, miniaturization, wound core and amorphous alloy nowadays, but the class-H dry-type transformer and tridimensional toroidal-core amorphous alloy transformer are the future direction of development. The technology application of smart distribution grid, power electronics technology and dynamic reactive power compensation technique will also affect the safety and economic operation of distribution transformer.

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Cui, Qi Ming, Shu Ting Cui, Zu Yuan Guan, and Wen Tao Sun. "Expert System Based on ESTA and Guide for Condition Evaluation of Transformers." Advanced Materials Research 347-353 (October 2011): 306–9. http://dx.doi.org/10.4028/www.scientific.net/amr.347-353.306.

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Through the application research of expert system shell ESTA, We have established a transformer condition evaluation expert system(ES) based on ESTA and corporate standards of State Grid (Q/GDW 169-2008) «Guide forCondition Evaluation of Oil-immersed Power Transformers (Reactors) ». Knowledge Base consists of several sections, parameters, rules in the evaluation of state variables, components and transformers. The value of the transformer condition evaluation expert system is that it supports and enhances the operation and maintenance personnel work, guidance (less experienced) the operation and maintenance personnel to understand the basic process of transformer condition evaluation. The application show that the system is effective and practical.

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E. Gouda, Osama, Saber M. Saleh, and Salah Hamdy El-hoshy. "Power Transformer Incipient Faults Diagnosis Based on Dissolved Gas Analysis." Indonesian Journal of Electrical Engineering and Computer Science 1, no. 1 (January 1, 2016): 10. http://dx.doi.org/10.11591/ijeecs.v1.i1.pp10-16.

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Incipient fault diagnosis of a power transformer is greatly influenced by the condition assessment of its insulation system oil and/or paper insulation. Dissolved gas-in-oil analysis (DGA) is one of the most powerfull techniques for the detection of incipient fault condition within oil-immersed transformers. The transformer data has been analyzed using key gases, Doernenburg, Roger, IEC and Duval triangle techniques. This paper introduce a MATLAB program to help in unification DGA interpretation techniques to investigate the accuracy of these techniques in interpreting the transformer condition and to provide the best suggestion for the type of the fault within the transformer based on fault percentage. It proposes a proper maintenance action based on DGA results which is useful for planning an appropriate maintenance strategy to keep the power transformer in acceptable condition. The evaluation is carried out on DGA data obtained from 352 oil samples has been summarized into 46 samples that have been collected from a 38 different transformers of different rating and different life span.

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E. Gouda, Osama, Saber M. Saleh, and Salah Hamdy EL-Hoshy. "Power Transformer Incipient Faults Diagnosis Based on Dissolved Gas Analysis." TELKOMNIKA Indonesian Journal of Electrical Engineering 16, no. 3 (December 1, 2015): 409. http://dx.doi.org/10.11591/tijee.v16i3.1630.

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Incipient fault diagnosis of a power transformer is greatly influenced by the condition assessment of its insulation system oil and/or paper insulation. Dissolved gas-in-oil analysis (DGA) is one of the most powerfull techniques for the detection of incipient fault condition within oil-immersed transformers. The transformer data has been analyzed using key gases, Doernenburg, Roger, IEC and Duval triangle techniques. This paper introduce a MATLAB program to help in unification DGA interpretation techniques to investigate the accuracy of these techniques in interpreting the transformer condition and to provide the best suggestion for the type of the fault within the transformer based on fault percentage. It proposes a proper maintenance action based on DGA results which is useful for planning an appropriate maintenance strategy to keep the power transformer in acceptable condition. The evaluation is carried out on DGA data obtained from 352 oil samples has been summarized into 46 samples that have been collected from a 38 different transformers of different rating and different life span.

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17

Ohno, Takahiro, Masami Katayama, Toshitugu Ishii, and Hideo Tsukioka. "Decreasing Behavior of Acetylene in Oil-Immersed Transformers." IEEJ Transactions on Power and Energy 119, no. 1 (1999): 54–62. http://dx.doi.org/10.1541/ieejpes1990.119.1_54.

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Novković, Marko, and Zoran Radaković. "Algorithm and software for shortening heat run test of oil-immersed power transformers." Tehnika 75, no. 4 (2020): 333–40. http://dx.doi.org/10.5937/tehnika2003333n.

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One of the type tests on power transformers is heat run test, which checks that the characteristic winding and oil temperatures are below allowed values. This is necessary to prevent accelerated ageing and shortening of the transformer life if the temperatures are higher than the allowed values. In addition to the user's clear interest, manufacturers are also interested in the results of heat run tests, as they can verify and improve the accuracy of calculation methods and software [1]. Then they can increase the accuracy and reduce the safety margins of calculation methods, so they can change transformer construction and achieve temperatures that are much closer to the allowed ones. As the result of that, they can reduce material and production costs. The minimal test is defined by the IEC standard [2]. Due to the great mass and consequent great thermal capacity of the transformer parts, heat run tests take a very long time. The paper presents an algorithm, and its software implementation, which reduce the occupancy of test station, energy consumption and the total cost of performing heat run tests.

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Stanisic, Stevan, Milica Jevtic, Bhaba Das, and Zoran Radakovic. "Fem CFD analysis of air flow in kiosk substation with the oil immersed distribution transformer." Facta universitatis - series: Electronics and Energetics 31, no. 3 (2018): 411–23. http://dx.doi.org/10.2298/fuee1803411s.

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In practice of loading of oil-immersed distribution transformers, there is a need to have lumped thermal model, requiring no big computational resources and computational time. One such model is presented in international transformer loading guide (IEC 60076-7), where heat transfer inside the transformer is modeled. In case of indoor transformer operation, this model does not consider transient thermal phenomena in the room. We developed a lumped model that includes heat transfer in the transformer room. In scope of the research, we also built FEM CFD (finite element method, computational fluid dynamics) model of air flow and heat transfer. The purpose of FEM CFD was to make a better insight into air flow, i.e. to study the simplifications introduced in lumped model and suggest potential improvements. This paper presents results achieved with FEM CFD. The considered case was the transformer with natural oil and natural air flow (ONAN).

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Xu, Dapeng, Yu Mao, Xiaohui Yang, Chunxu Qin, Haikun Shang, Jun Zhao, Jianying Zhang, and Zhiwei Li. "Analysis of winding temperature field under dynamic variable load of oil-immersed transformer." Thermal Science 25, no. 4 Part B (2021): 3009–19. http://dx.doi.org/10.2298/tsci2104009x.

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The research on the temperature field of the transformer winding under dynamic variable load is of great significance for ensuring the safe operation of power systems. This paper takes an SSP-360000/500 transformer as the research object, establishes a 2-D simulation model, and uses the finite volume method to analyze the high voltage winding and low voltage winding temperature distribution of the transformer under dynamic variable load. The simulation calculation results have been fully verified by the experimental data to make the successful prediction of the overall temperature and hot spot temperature position of forced oil circulation transformers with a guided structure. The results show that the most significant temperature raise occurs at the secondary end of the winding. In the case of dynamic variable load. The temperature raise of the winding becomes larger as the load increases, but before the rated load is in the stable temperature rise range, it can run safely for a long time. However, during overload operation, the average temperature raise of the high voltage winding may exceed its limit, the insulation material is damaged.

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Zhang, Xiaojing, Lu Ren, Haichuan Yu, Yang Xu, Qingquan Lei, Xin Li, and Baojia Han. "Dual-Temperature Evaluation of a High-Temperature Insulation System for Liquid-Immersed Transformer." Energies 11, no. 8 (July 27, 2018): 1957. http://dx.doi.org/10.3390/en11081957.

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A high-temperature oil–paper insulation system offers an opportunity to improve the overloading capability of distribution transformers facing seasonal load variation. A high-temperature electrical insulation system (EIS) was chosen due to thermal calculation based on a typical loading curve on the China Southern Power Grid. In order to evaluate candidate high-temperature insulation systems, Nomex® T910 (aramid-enhanced cellulose) immersed in FR3 (natural ester) was investigated by a dual-temperature thermal aging test compared with a conventional insulation system, Kraft paper impregnated with mineral oil. Throughout the thermal aging test, mechanical, chemical, and dielectric parameters of both paper and insulating oil were investigated in each aging cycle. The thermal aging results determined that the thermal class of the FR3-T910 insulation system meets the request of overloading transformer needs.

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Fantauzzi, Maurizio, Davide Lauria, Fabio Mottola, and Daniela Proto. "Estimating Wind Farm Transformers Rating through Lifetime Characterization Based on Stochastic Modeling of Wind Power." Energies 14, no. 5 (March 9, 2021): 1498. http://dx.doi.org/10.3390/en14051498.

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This paper deals with the problem of the optimal rating of mineral-oil-immersed transformers in large wind farms. The optimal rating is derived based on the probabilistic analyses of wind power generation through the Ornstein–Uhlenbeck stochastic process and on thermal model of the transformer through the integration of stochastic differential equations. These analyses allow the stochastic characterization of lifetime reduction of the transformer and then its optimal rating through a simple closed form. The numerical application highlights the effectiveness and easy applicability of the proposed methodology. The proposed methodology allows deriving the rating of transformers which better fits the specific peculiarities of wind power generation. Compared to the conventional approaches, the proposed method can better adapt the transformer size to the intermittence and variability of the power generated by wind farms, thus overcoming the often-recognized reduced lifetime.

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Zhang, Liwei. "Fault Diagnosis of Oil-Immersed Transformers Using Self-Organization Antibody Network and Immune Operator." Mathematical Problems in Engineering 2014 (2014): 1–8. http://dx.doi.org/10.1155/2014/847623.

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There are some drawbacks when diagnosis techniques based on one intelligent method are applied to identify incipient faults in power transformers. In this paper, a hybrid immune algorithm is proposed to improve the reliability of fault diagnosis. The proposed algorithm is a hybridization of self-organization antibody network (soAbNet) and immune operator. There are two phases in immune operator. One is vaccination, and the other is immune selection. In the process of vaccination, vaccines were obtained from training dataset by using consistency-preservingK-means algorithm (K-means-CP algorithm) and were taken as the initial antibodies for soAbNet. After the soAbNet was trained, immune selection was applied to optimize the memory antibodies in the trained soAbNet. The effectiveness of the proposed algorithm is verified using benchmark classification dataset and real-world transformer fault dataset. For comparison purpose, three transformer diagnosis methods such as the IEC criteria, back propagation neural network (BPNN), and soAbNet are utilized. The experimental results indicate that the proposed approach can extract the dataset characteristics efficiently and the diagnostic accuracy is higher than that obtained with other individual methods.

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Kamata, Yuzuru, and Masaaki Maejima. "The New Insulating Technology for Oil-Immersed Large Capacity Transformers." IEEJ Transactions on Power and Energy 112, no. 4 (1992): 289–93. http://dx.doi.org/10.1541/ieejpes1990.112.4_289.

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Yorozuya, Tsuruo, Nobuo Takasu, Susumu Isaka, Toshio Suzuki, Yukihiro Hashimoto, and Yasuaki Ishioka. "Diagnosis of Oil Immersed Transformers by Colorimetry of Insulating Papers." IEEJ Transactions on Power and Energy 112, no. 5 (1992): 415–23. http://dx.doi.org/10.1541/ieejpes1990.112.5_415.

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Gouda, Osama Elsayed, Mohamed Dessoky Ali, and Ali-Hassan Ali-Hassan. "Comparasion between Oil Immersed and SF6 Gas Power Transformers Ratings." TELKOMNIKA (Telecommunication Computing Electronics and Control) 10, no. 1 (March 1, 2012): 43. http://dx.doi.org/10.12928/telkomnika.v10i1.797.

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Okabe, Shigemitsu, Masanori Kohtoh, and Tsuyoshi Amimoto. "Investigation of electrostatic charging mechanism in aged oil-immersed transformers." IEEE Transactions on Dielectrics and Electrical Insulation 17, no. 1 (February 2010): 287–93. http://dx.doi.org/10.1109/tdei.2010.5412029.

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Yorozuya, Tsuruo, Nobuo Takasu, Susumu Isaka, Toshio Suzuki, Yukihiro Hashimoto, and Yasuaki Ishioka. "Diagnosis of oil-immersed transformers by colorimetry of insulating paper." Electrical Engineering in Japan 113, no. 5 (1993): 44–58. http://dx.doi.org/10.1002/eej.4391130505.

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Chairul, Imran Sutan, Norazhar Abu Bakar, Md Nazri Othman, Sharin Ab Ghani, Mohd Shahril Ahmad Khiar, and Mohd Aizam Talib. "Potential of Used Cooking Oil as Dielectric Liquid for Oil-Immersed Power Transformers." IEEE Transactions on Dielectrics and Electrical Insulation 28, no. 4 (August 2021): 1400–1407. http://dx.doi.org/10.1109/tdei.2021.009536.

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Liu, Yongxin, Bin Song, Linong Wang, Jiachen Gao, and Rihong Xu. "Power Transformer Fault Diagnosis Based on Dissolved Gas Analysis by Correlation Coefficient-DBSCAN." Applied Sciences 10, no. 13 (June 27, 2020): 4440. http://dx.doi.org/10.3390/app10134440.

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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.

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Wang, Chen, Jie Wu, Jianzhou Wang, and Weigang Zhao. "Reliability Analysis and Overload Capability Assessment of Oil-Immersed Power Transformers." Energies 9, no. 1 (January 14, 2016): 43. http://dx.doi.org/10.3390/en9010043.

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McMillen, C. J., M. G. Comber, R. H. Hopkinson, and C. W. Schoendube. "The Development of an Oil-Immersed Surge Arrester for Distribution Transformers." IEEE Power Engineering Review PER-5, no. 9 (September 1985): 45–46. http://dx.doi.org/10.1109/mper.1985.5526454.

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Pradhan, M. K., and T. S. Ramu. "On the Estimation of Elapsed Life of Oil-Immersed Power Transformers." IEEE Transactions on Power Delivery 20, no. 3 (July 2005): 1962–69. http://dx.doi.org/10.1109/tpwrd.2005.848663.

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McMillen, C., M. Comber, R. Hopkinson, and C. Schoendube. "The Development of an Oil-Immersed Surge Arrester for Distribution Transformers." IEEE Transactions on Power Apparatus and Systems PAS-104, no. 9 (September 1985): 2481–91. http://dx.doi.org/10.1109/tpas.1985.318994.

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Kirkbas, Ali, Akif Demircali, Selim Koroglu, and Aydin Kizilkaya. "Fault diagnosis of oil-immersed power transformers using common vector approach." Electric Power Systems Research 184 (July 2020): 106346. http://dx.doi.org/10.1016/j.epsr.2020.106346.

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Lin, Chia-Hung, Chien-Hsien Wu, and Ping-Zan Huang. "Grey clustering analysis for incipient fault diagnosis in oil-immersed transformers." Expert Systems with Applications 36, no. 2 (March 2009): 1371–79. http://dx.doi.org/10.1016/j.eswa.2007.11.019.

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Xing, Yun Long, Ying Ai Jin, Xian Da Che, Jun Liu, and Qing Gao. "Research Developments of Panel Type Radiators Cooling Oil-Immersed Power Transformers Based on Energy-Saving Materials." Advanced Materials Research 700 (May 2013): 243–46. http://dx.doi.org/10.4028/www.scientific.net/amr.700.243.

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When energy loss appears around cores and copper windings, cooling down the temperature timely has immense influence on transformer life and safe operation. In this paper, a serious of methods are proposed, which enhancing heat transfer of panel type radiators placed on the surface of the oil-immersed power transformers. When adding some turbulators on the panel type radiators and choosing the location of them appropriately, heat transfer effection can get a certain degree of improvement. Heat pipe is currently accepted efficient heat transfer element. Using the perfect heat transfer effect of heat pipe, with some necessary structural transformation, capacity of panel type radiator can be improved. New types of panel shape increase the effective heat transfer area, meanwhile damage the surface bounding layers. Therefore, optimization design with theory and simulation is a significant research field, by changing many kinds of structure parameters, for instance, the panel oil passage number and non uniform size distribution, inclination angle of up oil pipe and so on. Considering from actual condition, production process is simple, by using stainless steel, and owns a high level of automation, receiving lower energy consumption.

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Sutan Chairul, Imran, Sharin Ab Ghani, Hidayat Zainuddin, Nor Hidayah Rahim, Mohd Aizam Talib, and Nor Hafiz Nor Rahman. "Exploration of the Potential of Reclaimed Waste Cooking Oil for Oil-Immersed Power Transformers." TELKOMNIKA (Telecommunication Computing Electronics and Control) 15, no. 2 (March 1, 2017): 957. http://dx.doi.org/10.12928/telkomnika.v15i1.6151.

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Sutan Chairul, Imran, Sharin Ab Ghani, Hidayat Zainuddin, Nor Hidayah Rahim, Mohd Aizam Talib, and Nor Hafiz Nor Rahman. "Exploration of the Potential of Reclaimed Waste Cooking Oil for Oil-Immersed Power Transformers." TELKOMNIKA (Telecommunication Computing Electronics and Control) 15, no. 2 (March 1, 2017): 957. http://dx.doi.org/10.12928/telkomnika.v15i2.6151.

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Jalbert, Rodriguez-Celis, Arroyo-Fernández, Duchesne, and Morin. "Methanol Marker for the Detection of Insulating Paper Degradation in Transformer Insulating Oil." Energies 12, no. 20 (October 18, 2019): 3969. http://dx.doi.org/10.3390/en12203969.

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This manuscript presents a comprehensive literature review with the aim to provide readers a reference document with up-to-date information on the field of methanol use as a chemical marker. It has been a little more than a decade since methanol was first introduced as a marker for assessing solid insulation condition in power transformers. It all started when methanol was identified in the laboratory during thermal ageing tests carried out with oil-immersed insulating papers and was subsequently also identified in transformer field samples. The first publication on the subject was released in 2007 by our research group. This review covers the methanol fundamentals such as the analytical methods for its determination in transformer oil, which is generally performed by headspace gas chromatography with mass spectrometry or flame ionization as a detector. Current standardization efforts for its determination include ASTM working group 30948 and IEC TC10. Kinetic studies have confirmed the relationship between methanol generation, the number of broken 1,4-β-glycosidic bonds of cellulose and changes in mechanical properties. Laboratory tests have confirmed its stability at different accelerated ageing temperatures. Several utilities have identified methanol during field measurements, case studies on power and some distribution transformers are presented, as well as transformer postmortem investigations. These field-testing results demonstrate its utility in monitoring cellulosic insulation degradation. Recently, a model of methanol interpretation has become available that allows for evaluation of the average degree of polymerization of core type transformer cellulose winding. Methanol has a role as an indicator of cellulosic solid insulation ageing in transformer mineral oil, and it is expected that in the future it will be in routine use by utilities.

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Gui, Yingang, Tao Li, Xin He, Zhuyu Ding, and Pingan Yang. "Pt Cluster Modified h-BN for Gas Sensing and Adsorption of Dissolved Gases in Transformer Oil: A Density Functional Theory Study." Nanomaterials 9, no. 12 (December 8, 2019): 1746. http://dx.doi.org/10.3390/nano9121746.

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Hexagonal-Boron nitride nanotubes (h-BN) decorated with transition metals have been widely studied due to their enhanced physicochemical properties. In this paper, Pt cluster-modified h-BN is proposed as a sensitive material for a novel gas sensor for the online malfunction monitoring of oil-immersed transformers. The inner oil is ultimately decomposed to various gases during the long-term use of oil-immersed transformers. Exposure to excessively high temperatures produces the alkanes CH4 and C2H6, whereas different degrees of discharge generate H2 and C2H2. Therefore, the identification of H2, CH4, and C2H2 gas efficiently measures the quality of transformers. Based on the density functional theory, the most stable h-BN doped with 1–4 Pt atoms is employed to simulate its adsorption performance and response behavior to these typical gases. The adsorption energy, charge transfer, total density of states, projected density of states, and orbital theory of these adsorption systems are analyzed and the results show high consistency. The adsorption ability for these decomposition components are ordered as follows: C2H2 > H2 > CH4. Pt cluster-modified h-BN shows good sensitivity to C2H2, H2, with decreasing conductivity in each system, but is insensitive to CH4 due to its weak physical sorption. The conductivity change of Ptn-h-BN is considerably larger upon H2 than that upon C2H2, but is negligible upon CH4. Our calculations suggest that Pt cluster modified h-BN can be employed in transformers to estimate their operation status.

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Yuan, Fating, Wentao Yang, Bo Tang, Yue Wang, Fa Jiang, Yilin Han, Li Huang, and Can Ding. "Thermal optimization research of oil-immersed transformer winding based on the support machine response surface." Thermal Science, no. 00 (2021): 264. http://dx.doi.org/10.2298/tsci210530264y.

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In this paper, the CFD (computational fluid dynamics) model is established for the low voltage winding region of an oil-immersed transformer according to the design parameters, and the detailed temperature distribution within the region is obtained by numerical simulation. On this basis, the RSM (response surface methodology) is adopted to optimize the structure parameters with the purpose of minimizing the hot spot temperature. After a sequence of designed experiments, the second-order polynomial response surface and the SVM (support vector machine) response surface are established respectively. The analysis of their errors shows that the SVM response surface can be better used to fit the approximation. Finally, the PSO (particle swarm optimization) algorithm is employed to get the optimal structure parameters of the winding based on the SVM response surface. The results show that the optimization method can significantly reduce the hot spot temperature of the winding, which provides a guiding direction for the optimal design of the winding structure of transformers.

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Zheng, Hanbo, Chuansheng Zhang, Yiyi Zhang, Jiefeng Liu, Enze Zhang, Zhen Shi, Guangqi Shao, Kuikui Shi, Jing Guo, and Chaohai Zhang. "Optimization of Ethanol Detection by Automatic Headspace Method for Cellulose Insulation Aging of Oil-immersed Transformers." Polymers 12, no. 7 (July 15, 2020): 1567. http://dx.doi.org/10.3390/polym12071567.

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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.

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Sun, Jong-Ho, Don-Ha Hwang, and Jin-Soo Kim. "Study on the Effectiveness of Continuous Water Removal in Oil Immersed Transformers." Journal of the Korean Institute of Illuminating and Electrical Installation Engineers 30, no. 2 (February 28, 2016): 20–25. http://dx.doi.org/10.5207/jieie.2016.30.2.020.

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Šerkinić, Vlatka, Marijana Majić Renjo, and Viktor Ucović. "CO2 footprint for distribution oil immersed transformers according to ISO 14067:2018." Journal of Energy - Energija 69, no. 3 (June 30, 2020): 3–9. http://dx.doi.org/10.37798/202069342.

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In the last few decades, climate change and the global warming have emerged as important environmental issues. The cause of global warming is the increase of greenhouse gas emissions (GHG). There are several greenhouse gases responsible for global warming: water vapor, carbon dioxide (CO2), methane, nitrous oxides, chlorofluorocarbons (CFCs) and others. They are mostly the result of the fossil fuels' combustion in cars, buildings, factories, and power plants. The gas responsible for the most of the global warming is carbon dioxide (CO2). This increase in the greenhouse gas emissions leads to a greater interest of the consumers, board management and stakeholders in the environmental impact of their activities, products and services.The verification of the Carbon Footprint of distribution oil immersed transformer, presented in this paper, was recognized as an opportunity for the company to understand its own environmental impact and to identify inefficiencies and opportunities within its business.Carbon Footprint of a Product (CFP) is a rather new term closely related to the greenhouse gas emissions. The CFP is considered as a total of the greenhouse emissions generated during the life cycle of a product – that is, from raw material acquisition or generation from natural resources to a final disposal. It is described within the standard ISO 14067:2018 Carbon footprint of products – Requirements and guidelines for quantification [1]. This standard belongs to the environmental series ISO 14000 and enables the organization to demonstrate its environmental responsibility.Life Cycle Assessment (LCA), as well as the Carbon Footprint of products together with environmental impact of the product, are shown in this paper in accordance with standard ISO 14067:2018. The LCA is a method for the quantification of the environmental impacts of individual products. It takes into account a complete life cycle, starting from a raw material production, until the product’s final disposal or materials’ recycling in accordance with ISO 14040 [2] and ISO 14044 [3]. Greenhouse gases are expressed in mass-based CO2 equivalents (CO2e), which is the unit of measurement in the ISO 14067:2018 standard. The functional unit in ISO 14067:2018 can be either a product or a service. In this paper, the functional unit was the product – oil immersed distribution transformer, in four product variations. The LCA scope used in the preparation of this study was "cradle to gate" – it covers the CFP from the acquisition of the raw materials ("cradle") up to dispatch from the factory ("gate").The objectives of product life cycle considerations in Končar D&ST Inc. are to reduce the use of natural resources and emissions to the environment, as well as to improve social performance at different stages of the product life cycle.By linking the economic and ecological dimension of the production, different aspects during realization of product in all phases of the life cycle come together. In this way company achieves cleaner products and processes, competitive advantage in the market and improved platform that will meet the needs of the changing business climate.Lifecycle thinking is based on the principles of reducing environmental impacts at the beginning of product creation, giving a wider picture of material and energy flow and ultimately environmental pollution prevention. These principles are organized in Končar D&ST Inc. internally by planning and introducing cleaner manufacturing processes, environmental protection management and eco-design.Incorporating ISO 14067:2018 into company business is recognized as an opportunity for transparent communication to interested parties, incorporating CO2 emissions into annual reports and as a baseline information for a first step towards managing carbon emissions.

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Sevastyanova, Olena, Bogdan Pasalskiy, and Boris Zhmud. "Copper Release Kinetics and Ageing of Insulation Paper in Oil-Immersed Transformers." Engineering 07, no. 08 (2015): 514–29. http://dx.doi.org/10.4236/eng.2015.78048.

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Bracale, Antonio, Guido Carpinelli, Mario Pagano, and Pasquale De Falco. "A Probabilistic Approach for Forecasting the Allowable Current of Oil-Immersed Transformers." IEEE Transactions on Power Delivery 33, no. 4 (August 2018): 1825–34. http://dx.doi.org/10.1109/tpwrd.2018.2791181.

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Ou, Minghui, Hua Wei, Yiyi Zhang, and Jiancheng Tan. "A Dynamic Adam Based Deep Neural Network for Fault Diagnosis of Oil-Immersed Power Transformers." Energies 12, no. 6 (March 14, 2019): 995. http://dx.doi.org/10.3390/en12060995.

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This paper presents a Dynamic Adam and dropout based deep neural network (DADDNN) for fault diagnosis of oil-immersed power transformers. To solve the problem of incomplete extraction of hidden information with data driven, the gradient first-order moment estimate and second-order moment estimate are used to calculate the different learning rates for all parameters with stable gradient scaling. Meanwhile, the learning rate is dynamically attenuated according to the optimal interval. To prevent over-fitted, we exploit dropout technique to randomly reset some neurons and strengthen the information exchange between indirectly-linked neurons. Our proposed approach was utilized on four datasets to learn the faults diagnosis of oil-immersed power transformers. Besides, four benchmark cases in other fields were also utilized to illustrate its scalability. The simulation results show that the average diagnosis accuracies on the four datasets of our proposed method were 37.9%, 25.5%, 14.6%, 18.9%, and 11.2%, higher than international electro technical commission (IEC), Duval Triangle, stacked autoencoders (SAE), deep belief networks (DBN), and grid search support vector machines (GSSVM), respectively.

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Zeng, Bing, Jiang Guo, Fangqing Zhang, Wenqiang Zhu, Zhihuai Xiao, Sixu Huang, and Peng Fan. "Prediction Model for Dissolved Gas Concentration in Transformer Oil Based on Modified Grey Wolf Optimizer and LSSVM with Grey Relational Analysis and Empirical Mode Decomposition." Energies 13, no. 2 (January 15, 2020): 422. http://dx.doi.org/10.3390/en13020422.

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Oil-immersed transformer is one of the most important components in the power system. The dissolved gas concentration prediction in oil is vital for early incipient fault detection of transformer. In this paper, a model for predicting the dissolved gas concentration in power transformer based on the modified grey wolf optimizer and least squares support vector machine (MGWO-LSSVM) with grey relational analysis (GRA) and empirical mode decomposition (EMD) is proposed, in which the influence of transformer load, oil temperature and ambient temperature on gas concentration is taken into consideration. Firstly, GRA is used to analyze the correlation between dissolved gas concentration and transformer load, oil temperature and ambient temperature, and the optimal feature set affecting gas concentration is extracted and selected as the input of the prediction model. Then, EMD is used to decompose the non-stationary series data of dissolved gas concentration into stationary subsequences with different scales. Finally, the MGWO-LSSVM is used to predict each subsequence, and the prediction values of all subsequences are combined to get the final result. DGA samples from two transformers are used to verify the proposed method, which shows high prediction accuracy, stronger generalization ability and robustness by comparing with LSSVM, particle swarm optimization (PSO)-LSSVM, GWO-LSSVM, MGWO-LSSVM, EMD-PSO-LSSVM, EMD-GWO-LSSVM, EMD-MGWO-LSSVM, GRA-EMD-PSO-LSSVM and GRA-EMD-GWO-LSSVM.

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Mechkov, Emil, Raina Tzeneva, Valentin Mateev, and Ivan Yatchev. "Electromagnetic Field Modelling Using FEM of the Active Part of Oil-Immersed Transformers." Materials Science Forum 856 (May 2016): 184–89. http://dx.doi.org/10.4028/www.scientific.net/msf.856.184.

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The electromagnetic field of the active part of oil-immersed transformers 160kVA and 630kVA has been modelled in the present paper. Finite element method and ANSYS program have been employed for the field modelling. Based on the created model, the field distribution with values of the magnetic flux density has been obtained, as well as the Joule losses have been calculated. Two variants of the model - with and without tank are studied and compared.

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Journal articles on the topic 'Oil Immersed Transformers'

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