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1
Ivanov, Georgi, Anelia Spasova, Valentin Mateev i Iliana Marinova. "Applied Complex Diagnostics and Monitoring of Special Power Transformers". Energies 16, nr 5 (22.02.2023): 2142. http://dx.doi.org/10.3390/en16052142.
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As a major component in electric power systems, power transformers are one of the most expensive and important pieces of electrical equipment. The trouble-free operation of power transformers is an important criterion for safety and stability in a power system. Technical diagnostics of electrical equipment are a mandatory part of preventing accidents and ensuring the continuity of the power supply. In this study, a complex diagnostic methodology was proposed and applied for special power transformers’ risk estimation. Twenty special power transformers were scored with the proposed risk estimation methodology. For each transformer, dissolved gas analysis (DGA) tests, transformer oil quality analysis, visual inspections of all current equipment on-site and historical data for the operation of each electrical research were conducted. All data were collected and analyzed under historical records of malfunctioning events. Statistical data for expected fault risk, based on long-term records, with such types of transformers were used to make more precise estimations of the current state of each machine and expected operational resource. The calculated degree of insulation polymerization was made via an ANN-assisted predictive method. Assessment of the collected data was applied to allow detailed information of the state of the power transformer to be rated. A method for risk assessment and reliability estimation was proposed and applied, based on the health index (HI) for each transformer.
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Fiennes, J., i C. R. de Souza. "The Complex Transformer as a Network-Model Element". International Journal of Electrical Engineering & Education 40, nr 1 (styczeń 2003): 27–35. http://dx.doi.org/10.7227/ijeee.40.1.3.
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Jamali, Ali, i Masoud Mahdianpari. "Swin Transformer for Complex Coastal Wetland Classification Using the Integration of Sentinel-1 and Sentinel-2 Imagery". Water 14, nr 2 (10.01.2022): 178. http://dx.doi.org/10.3390/w14020178.
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The emergence of deep learning techniques has revolutionized the use of machine learning algorithms to classify complicated environments, notably in remote sensing. Convolutional Neural Networks (CNNs) have shown considerable promise in classifying challenging high-dimensional remote sensing data, particularly in the classification of wetlands. State-of-the-art Natural Language Processing (NLP) algorithms, on the other hand, are transformers. Despite the fact that transformers have been utilized for a few remote sensing applications, they have not been compared to other well-known CNN networks in complex wetland classification. As such, for the classification of complex coastal wetlands in the study area of Saint John city, located in New Brunswick, Canada, we modified and employed the Swin Transformer algorithm. Moreover, the developed transformer classifier results were compared with two well-known deep CNNs of AlexNet and VGG-16. In terms of average accuracy, the proposed Swin Transformer algorithm outperformed the AlexNet and VGG-16 techniques by 14.3% and 44.28%, respectively. The proposed Swin Transformer classifier obtained F-1 scores of 0.65, 0.71, 0.73, 0.78, 0.82, 0.84, and 0.84 for the recognition of coastal marsh, shrub, bog, fen, aquatic bed, forested wetland, and freshwater marsh, respectively. The results achieved in this study suggest the high capability of transformers over very deep CNN networks for the classification of complex landscapes in remote sensing.
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Sun, Yuanyuan, Gongde Xu, Na Li, Kejun Li, Yongliang Liang, Hui Zhong, Lina Zhang i Ping Liu. "Hotspot Temperature Prediction of Dry-Type Transformers Based on Particle Filter Optimization with Support Vector Regression". Symmetry 13, nr 8 (22.07.2021): 1320. http://dx.doi.org/10.3390/sym13081320.
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Both poor cooling methods and complex heat dissipation lead to prominent asymmetry in transformer temperature distribution. Both the operating life and load capacity of a power transformer are closely related to the winding hotspot temperature. Realizing accurate prediction of the hotspot temperature of transformer windings is the key to effectively preventing thermal faults in transformers, thus ensuring the reliable operation of transformers and accurately predicting transformer operating lifetimes. In this paper, a hot spot temperature prediction method is proposed based on the transformer operating parameters through the particle filter optimization support vector regression model. Based on the monitored transformer temperature, load rate, transformer cooling type, and ambient temperature, the hotspot temperature of a dry-type transformer can be predicted by a support vector regression method. The hyperparameters of the support vector regression are dynamically optimized here according to the particle filter to improve the optimization accuracy. The validity and accuracy of the proposed method are verified by comparing the proposed method with a traditional support vector regression method based on the real operating data of a 35 kV dry-type transformer.
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Xu, Honghua, Yong Li, Lei Zhu i Ziqiang Xu. "Condition assessment of transformers in wind farm based on modified one-dim residual neural network". Journal of Physics: Conference Series 2378, nr 1 (1.12.2022): 012078. http://dx.doi.org/10.1088/1742-6596/2378/1/012078.
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Abstract The working environment of transformers in the wind farm is more complex than others, which brings the difference in condition assessment. Moreover, many condition assessment methods based on characteristics or machine learning have difficulty in recognition in cases of multiple transformers, conditions and measuring points. To assess conditions, this paper establishes a condition classification model of the transformer with a modified one-dim residual neural network and uses vibration signal, current and voltage as inputs. The built network mode has faster convergence speed and classification accuracy in transformer condition assessment and is more suitable for transformer condition assessment than the original one.
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Andjelic, Zoran, Ramsis Girgis, Asim Fazlagic, Alessandro Regalino, Claude J. Lambert, A. Seidel i M. Ries. "Structural-mechanics analysis of the 10g transformer impact". Journal of Energy - Energija 63, nr 1-4 (2.07.2022): 104–13. http://dx.doi.org/10.37798/2014631-4168.
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Transportation of the power transformers from the site of production to the site of the exploitation is very complex and sensible task. During transportation, the transformer can be subjected to a variety of different impacts registered either during railway transportation or during on/off loading. The transformer should be designed to sustain the high accelerations appearing often during transportation. Transformer are usually equipped with the impact recorder to registry the acceleration behavior during the transport. In the current paper, we give an overview on the structural-mechanics analysis of 10g impact on the power transformer registered during the railway transportation of the transformer from Canada to US.
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Azmi Murad Abd Aziz, Mohd Aizam Talib, Ahmad Farid Abidin i Syed Abdul Mutalib Al Junid. "Development of Power Transformer Health Index Assessment Using Feedforward Neural Network". Journal of Advanced Research in Applied Sciences and Engineering Technology 30, nr 3 (15.05.2023): 276–89. http://dx.doi.org/10.37934/araset.30.3.276289.
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The role of a power transformer is to convert the electrical power level and send it to the consumer, making it an essential component of a power system. In addition, transformer asset management is essential for monitoring the functioning of transformers in the system to prevent failure and anticipating the health state of transformers, using a technique known as the health index (HI). However, the calculation and computation to determine the transformer HI based on a scoring and ranking technique is complex and required expert validation. Therefore, this paper presents a transformer HI prediction using a feedforward neural network (FFNN) to improve the existing complex scoring and ranking technique. Levenberg–Marquardt (LM), Bayesian Regularized (BR), and Scaled Conjugate Gradient (SCG) are the FFNN training techniques presented in this study to forecast the transformer HI. To validate the techniques, the HI values generated by different FFNN techniques were compared to the scoring and ranking system. Then, the performance of the proposed ANN was evaluated using the correlation coefficient and mean square error (MSE). As a result, the transformer HI was successfully predicted by employing three FFNN techniques, namely the LM, BR, and SCG techniques, which were able to determine whether the transformer's condition is very good, good, fair, or poor. In conclusion, the ANN suggested in this study has also been validated with the ranking and scoring approach, which provides high similarity score in comparison to the transformer health index.
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Stosic, Biljana. "Wave digital models of ideal and real transformers". Facta universitatis - series: Electronics and Energetics 29, nr 2 (2016): 219–31. http://dx.doi.org/10.2298/fuee1602219s.
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In this paper, the wave digital filter (WDF) theory is applied for development of the wave digital models of ideal and real transformers which can be used for modeling of more complex structures. The transformers wave digital networks are described and developed here based on scattering variables and two-port and three-port networks of parallel and series adaptors. WDF-based model of a real transformer includes parasitic resistors and inductors, which are usual in low-frequency transformer equivalent circuit.
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Lin, Yuyin, Yuzi Lin, Qingyu Wu, Xinhai Wu, Jiayi Tu, Weisheng Ren, Yu Lu, Yaoyin Zhang, Engang Cheng i Xiangyu Guan. "Anti-interference detection and operation state identification of transformer acoustic characteristics based on Conv-Tas-ResNet". Journal of Physics: Conference Series 2399, nr 1 (1.12.2022): 012039. http://dx.doi.org/10.1088/1742-6596/2399/1/012039.
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Abstract There is a limitation in the process of acoustic signal detection, which lies in serious background noise interference and the complex correlation between acoustic signal characteristics and operation states. Integrating the denoising model and feature classification model, a method of transformer acoustic signal anti-interference detection and operation state detection based on deep learning is proposed in this paper. Through tests in anechoic rooms, acoustic signals of transformers in the normal state or under harmonic load are acquired. Combining these signals with the background noise, a dataset containing 12000 samples of acoustic signals is constructed. To implement anti-interference detection, Conv-TasNet is utilized to get the transformer acoustic signal and environmental noise separated; then, ResNet is utilized to classify the operation states of the transformer accurately. Results show that compared with the blind source separation method through RNN and FastICA, the denoising model established in this paper improves Si-SDRi parameters by 37.4dB and 17.53dB respectively, and the transformer operation state classification model established in this paper classifies the test dataset with an accuracy of 97.7%, thus providing an effective method for the extraction of transformer acoustic signal and diagnosis of transformer operation states in complex environments.
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Li, Zhi Bin, i Qi Ben Li. "Multi-Level Fault Diagnosis of Power Transformer Based on Fusion Technology". Advanced Materials Research 860-863 (grudzień 2013): 1925–28. http://dx.doi.org/10.4028/www.scientific.net/amr.860-863.1925.
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Traditional transformer fault diagnosis based on single source of information has significant limitation in identification of transformer fault type because of power transformers complex structure and changeable operating environment. So fusion technology is introduced into the fault diagnosis of power transformer. This method divides the progress of transformer fault diagnosis into two fusion levels. The first level is to ascertain whether it is overheated or discharged by content of gases dissolved in transformer oil. The second level is to ascertain the location or cause of the fault by electric data. The intelligence algorithms which are used in these two levels are both the improved BP neural network algorithm. Finally, the effectiveness is validated by the result of practical fault diagnosis examples.
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Alyunov, Alexander, Olga Vyatkina i Alexander Nemirovskiy. "On efficiency of digital system of power transformer proactive diagnostics". Proceedings of Irkutsk State Technical University 24, nr 5 (październik 2020): 966–76. http://dx.doi.org/10.21285/1814-3520-2020-5-966-976.
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The purpose of the paper is to diagnose the state of a power transformer by determining its equivalent circuit parameters by means of synchronized vector measurements of currents and voltages in the transformer normal operation mode without shutdowns, which makes it possible to increase the reliability of relay protection operation. A cost - effective method for proactive diagnostics of power transformers is proposed. Through monitoring of additional parameters (short circuit resistance, active and inductive resistance of the positive sequence, active and inductive resistance of the negative sequence) it can increase the speed and accuracy of detecting possible internal short circuits arising due to winding damage or high-voltage transformer bushings without diagnosed transformer disconnection from the network. The method allows to estimate the transformer health index and serviceability by the difference between the calculated parameters of the equivalent circuit and the passport values of the parameters. Having conducted the damage causedependent analysis of the number of power transformer damages, the authors determined total economic losses that include the losses caused by equipment damage and losses caused by the interruptions in consumer power supply. The total economic losses for a power transformer with a rated power of 63 MVA amounted to 10687402 rubles. It is shown that the diagnostic system expands the possibilities of analyzing the transformer state in operating modes, allows to pr event the approaching of the damage moment and occurrence of sudden accidents as well as minimizes the expected damage from shutdowns and equipment failure. A hardware and software complex is proposed for the diagnostics of power transformer internal damage. The given main characteristics of the proposed hardware and software complex include the number of measuring channels, accuracy class, sampling frequency, and others. The results of the work expand the possibilities of analyzing the transformer state in the operating mode and can be used in the world practice of creating various monitoring systems designed to identify the defects developing in transformers caused by winding deformation.
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Li, Xiaopeng, i Shuqin Li. "Transformer Help CNN See Better: A Lightweight Hybrid Apple Disease Identification Model Based on Transformers". Agriculture 12, nr 6 (19.06.2022): 884. http://dx.doi.org/10.3390/agriculture12060884.
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The complex backgrounds of crop disease images and the small contrast between the disease area and the background can easily cause confusion, which seriously affects the robustness and accuracy of apple disease- identification models. To solve the above problems, this paper proposes a Vision Transformer-based lightweight apple leaf disease- identification model, ConvViT, to extract effective features of crop disease spots to identify crop diseases. Our ConvViT includes convolutional structures and Transformer structures; the convolutional structure is used to extract the global features of the image, and the Transformer structure is used to obtain the local features of the disease region to help the CNN see better. The patch embedding method is improved to retain more edge information of the image and promote the information exchange between patches in the Transformer. The parameters and FLOPs (Floating Point Operations) of the model are significantly reduced by using depthwise separable convolution and linear-complexity multi-head attention operations. Experimental results on a complex background of a self-built apple leaf disease dataset show that ConvViT achieves comparable identification results (96.85%) with the current performance of the state-of-the-art Swin-Tiny. The parameters and FLOPs are only 32.7% and 21.7% of Swin-Tiny, and significantly ahead of MobilenetV3, Efficientnet-b0, and other models, which indicates that the proposed model is indeed an effective disease-identification model with practical application value.
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Vinogradov, A. V., A. V. Vinogradova, V. E. Bolshev, M. O. Ward, N. V. Makhiyanova i L. V. Dolomaniuk. "Justification for creating a mobile complex to assess electric energy loss in power transformers during the operation process". E3S Web of Conferences 124 (2019): 02009. http://dx.doi.org/10.1051/e3sconf/201912402009.
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The paper analyzes the statistical data on the transformers of the Kromsky branch of Oreloblenergo OJSC including the average lifetime of the transformers and no-load losses measured in accordance with the requirements of Russian standard GOST 3484.1. The analysis indicates that the declared passport data differ from the measured data. There is also the analysis of technical solutions to improve the power transformer design including to development of new types of electrical steel used in transformer cores, superconducting materials for winding. The article gives an understanding of the advantages and disadvantages of using these technologies as well as the possible reduction of electrical losses. In conclusion there is the justification for creating the mobile measuring complex to assess electric energy loss in power transformers during operation without disconnecting the load. The paper describes the result achieved using such a mobile measuring complex along with the economic effect of creating the project.
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Agarkov, N. E. "FEATURES OF WIDEBAND DEVELOPMENT OF RESISTANCE TRANSFORMERS IN HF RANGE, LOADED ON LOW-IMPEDANCE ABSORBERS". RADIO COMMUNICATION TECHNOLOGY, nr 49 (15.07.2021): 53–66. http://dx.doi.org/10.33286/2075-8693-2021-49-53-66.
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A device based on a resistance transformer for matching a low-resistance load, including an electrically small short-wave antenna, with a transceiver equipment is described. The results of prototyping and modeling of broadband matching transformers with ferrite cores with transformation ratios 4:1, 9:1, and 25:1 are presented. A model of a matching transformer operating on a low-resistance absorber is proposed. The results of measurements and modeling of the effect of frequency dependences of the complex resistance of low-resistance resistors of various types used as loads on the measured characteristics of transformers are presented. An increase in the influence of parasitic parameters of the load, winding terminals, compensating reactivity of elements and the transformer itself on its characteristics with a decrease in the load resistance is shown.
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Perrier, Anne-Laure, Jean-Marc Duchamp, Olivier Exshaw, Robert Harrison i Philippe Ferrari. "A compact semi-lumped tunable complex-impedance transformer". International Journal of Microwave and Wireless Technologies 1, nr 5 (8.09.2009): 403–13. http://dx.doi.org/10.1017/s175907870999050x.
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This article describes the design and performance of a compact tunable impedance transformer. The structure is based on a transmission line loaded by varactor diodes. Using only two pairs of diodes, the circuit is very small with a total length of only λ/10. Both the frequency range and the load impedance can be tuned by varying the varactor bias voltages. Our design provides a tunable operating frequency range of ±40% and an impedance match ranging from 20 to 90 Ω at 0.8 GHz and from 30 to 170 Ω at 1.5 GHz. In addition, a new approach that considers losses for the simulation and measurement of this impedance transformer was investigated. The measured performance of a 1 GHz prototype design confirmed the validity of this new approach.
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Zheng, Jianhan, Shengqing Gui i Haomin Zhang. "Transformer Vibration Analysis Based on Double Branch Convolutional Neural Network". Journal of Physics: Conference Series 2503, nr 1 (1.05.2023): 012092. http://dx.doi.org/10.1088/1742-6596/2503/1/012092.
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Abstract The power transformer is one of the important pieces of equipment in the power grid system, and its normal operation is related to the safety and reliability of the whole power system. There are many factors influencing transformer vibration in operation, and its characteristics are complex, so it is difficult to be directly used for transformer state analysis. This paper proposes a method for vibration signal analysis based on a continuous wavelet time-frequency graph. The segmented samples of transformer vibration signals are selected by the time-domain sample segmentation method, and the segmented time sequence samples are transformed by continuous wavelet transform to obtain a two-dimensional time-frequency graph. The time-frequency graph is input into the two-branch convolutional neural network, and the transformer state classification is given based on the features extracted from the network. The simulation analysis on transformer vibration data measured by multiple measuring points shows that the proposed method has an average recognition accuracy of 98.3%. The work in this paper can provide a reference for the vibration analysis of the transformer.
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Zhu, Xiaoning, Yannan Jia, Sun Jian, Lize Gu i Zhang Pu. "ViTT: Vision Transformer Tracker". Sensors 21, nr 16 (20.08.2021): 5608. http://dx.doi.org/10.3390/s21165608.
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This paper presents a new model for multi-object tracking (MOT) with a transformer. MOT is a spatiotemporal correlation task among interest objects and one of the crucial technologies of multi-unmanned aerial vehicles (Multi-UAV). The transformer is a self-attentional codec architecture that has been successfully used in natural language processing and is emerging in computer vision. This study proposes the Vision Transformer Tracker (ViTT), which uses a transformer encoder as the backbone and takes images directly as input. Compared with convolution networks, it can model global context at every encoder layer from the beginning, which addresses the challenges of occlusion and complex scenarios. The model simultaneously outputs object locations and corresponding appearance embeddings in a shared network through multi-task learning. Our work demonstrates the superiority and effectiveness of transformer-based networks in complex computer vision tasks and paves the way for applying the pure transformer in MOT. We evaluated the proposed model on the MOT16 dataset, achieving 65.7% MOTA, and obtained a competitive result compared with other typical multi-object trackers.
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Yao, Degui, Longfei Li, Songyang Zhang, Dianhai Zhang i Dezhi Chen. "The Vibroacoustic Characteristics Analysis of Transformer Core Faults Based on Multi-Physical Field Coupling". Symmetry 14, nr 3 (7.03.2022): 544. http://dx.doi.org/10.3390/sym14030544.
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Power transformers play an important role in the safe and reliable operation of the whole power grid. Once a fault occurs, it will endanger the normal operation of the transformer, and even result in power grid accidents. Accurate and practical methods of transformer fault monitoring and type identification have attracted extensive attention in the field of electrical engineering. However, it is difficult to obtain a large number of measurement data for different fault types on a large power transformer. The vibroacoustic characteristics of transformer faults have significant asymmetry. For the power transformer in service, it is complex and uneconomic to obtain the vibroacoustic signals under different fault conditions. To handle this problem, this paper proposes simulation methods of several common transformer core faults, based on multi-physical field coupling, and then analyzes the vibroacoustic signals generated by the operating transformer. Finally, it verifies the results of acoustic and vibration signals under several faults, through physical experiments. The results show that the transformer fault simulation method is reasonable and accurate. Furthermore, a change in the transformer core state will cause a change in the transformer vibroacoustic characteristics, and different types of core faults can be distinguished by the analysis of vibroacoustic characteristics.
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Xiang, Shen, Xu Fei, Xu Long i Cai Jidong. "Research on Transformer Voiceprint Feature Extraction Oriented to Complex Noise Environment". International Journal of Acoustics and Vibration 28, nr 2 (16.06.2023): 193–99. http://dx.doi.org/10.20855/ijav.2023.28.21933.
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Transformer fault diagnosis based on acoustic characteristics is a new non-contact and non-destructive monitoring method. It has the advantages that the acoustic signal detection is not disturbed by electric and magnetic fields, and the monitoring process does not affect the normal operation of the transformer. Aiming at the difficulty of extracting transformer voiceprint features in complex noise environment, a transformer voiceprint feature extraction method based on Variable Mode Extraction (VME) is proposed. In this method, the center frequency of the Intrinsic Mode Function(IMF) is set according to the generation mechanism of transformer radiated noise, thus the uncertainty of decomposition results caused by random distribution and other frequency search methods is eliminated; Then, taking the frequency-domain energy aggregation of IMF and the minimum center frequency energy of residual signal as the optimization objectives, the cyclic iterative decomposition is used to identify and extract the transformer voiceprint features, so as to reduce the impact of environmental noise and other equipment noise. The analysis results of simulation signals and field signals show that this method can effectively reduce the impact of environmental noise and extract more clear and accurate transformer voiceprint features.
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Kovacevic, Dragan, Slobodan Skundric i Jelena Lukic. "Monitoring and diagnostics of power transformer insulation". Thermal Science 10, nr 4 (2006): 43–54. http://dx.doi.org/10.2298/tsci0604043k.
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Liberalization of the energy market drives utilities to a more cost-effective power system. Power transformers are the most complex, important, and critical components of the transition and distribution power systems. Insulation system is the key component of life extension, better availability and higher reliability of a transformer. In order to achieve both decreasing operational cost and reliable service condition-based maintenance is needed. Monitoring and diagnostics methods and techniques, for insulation condition assessment of power transformers, are described. Date base and knowledge rules diagnostics management system, in internet oriented environment, is outlined. .
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Trappey, Amy J. C., Charles V. Trappey, Min-Hua Chao, Nan-Jun Hong i Chun-Ting Wu. "A VR-Enabled Chatbot Supporting Design and Manufacturing of Large and Complex Power Transformers". Electronics 11, nr 1 (28.12.2021): 87. http://dx.doi.org/10.3390/electronics11010087.
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Virtual reality (VR) immersive technology allows users to experience enhanced reality using human–computer interfaces (HCI). Many systems have implemented VR with improved HCI to provide strategic market advantages for industry and engineering applications. An intelligent chatbot is a conversational system capable of natural language communication allowing users to ask questions and receive answers online to enhance customer services. This research develops and implements a system framework for a VR-enabled large industrial power transformer mass-customization chatbot. The research collected 1272 frequently asked questions (FAQs) from a power transformer manufacturers’ knowledge base that is used for question matching and answer retrieval. More than 1.2 million Wikipedia engineering pages were used to train a word-embedding model for natural language understanding of question intent. The complex engineering questions and answers are integrated with an immersive VR computer human interface. The system enables users to ask questions and receive explicit and detailed answers combined with 3D immersive images of industrial sized power transformer assemblies. The user interfaces can be projected into the VR headwear or computer screen and manipulated with a controller. The unique immersive VR consultation chatbot system is to support real-time design consultation for the design and manufacturing of complex power transformers.
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IASINSKII, IGOR' F., i DENIS V. EGOROV. "DESIGN AUTOMATION OF INTERNAL ISOLATION OF HIGH VOLTAGE TRANSFORMERS INPUT USING PARALLEL COMPUTING". Cherepovets State University Bulletin 4, nr 97 (2020): 89–101. http://dx.doi.org/10.23859/1994-0637-2020-4-97-8.
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The design process of a transformer is characterized by conflicting requirements for its qualities. This circumstance is due to the fact that the transformer includes a large number of elements and is manufactured on individually tuned equipment. Moreover, the production of transformers is associated with the cost of such valuable materials as copper, aluminum, steel, etc. Thus, the transformer is a complex system, the high-quality design of which is difficult without automation. It is proposed to speed up the process of calculating the internal isolation of a transformer using a parallel computing environment and the use of fast algorithms. Methods of mathematical modeling of physical objects, methods of applied mathematics, principles of computer modeling of physical objects, algorithms for parallelizing the computing process in interfaces with shared memory, analysis of the results of computational experiments are applied. A software system has been developed for the automated design of the internal isolation of the high-voltage input of the transformer...
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Zhao, ZG, SQ Zhang, HC Cheng, ZY Xu i RJ Tan. "Research on Short-Circuit Impedance Analysis Method of Auto-transformer for Side Column Voltage Regulation". Journal of Physics: Conference Series 2476, nr 1 (1.04.2023): 012066. http://dx.doi.org/10.1088/1742-6596/2476/1/012066.
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Abstract In order to realize the accurate analysis of the impedance of transformers with complex connection relations, this paper derives the impedance calculation method of low-voltage excitation side column linear voltage regulating autotransformer by using the power method. The transformer network with different taps is analyzed, and the calculation formulas of transformer HV-MV impedance, HV-LV impedance and MV-LV impedance are derived. The analysis is simplified to the solution of concentric column coil impedance and magnetic potential of each coil. By comparing the calculated data of actual products with the measured data, the calculation deviation of this method is controlled within 2.03%; By using the finite element method, the impedance of the transformer with the measures to control the magnetic shielding is analyzed and calculated. By comparing the impedance of the transformer with different measures, it can be confirmed that the magnetic leakage measures have an impact on the impedance of the transformer.
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Jurišić, Bruno, Ivo Uglešić, Alain Xemard i Françoise Paladian. "Case study on transformer models for calculation of high frequency transmitted overvoltages". Journal of Energy - Energija 63, nr 1-4 (4.07.2022): 262–72. http://dx.doi.org/10.37798/2014631-4186.
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Events such as lightning, switching of vacuum circuit breaker or switching operations in gas insulated substation (GIS) generate high frequency overvoltages. An equipment in a transmission or a distribution system has to be protected against such phenomena. Unfortunately, the traditional transformer models available in Electromagnetic transient simulations program (EMTP-like) software packages are not capable of representing transformer behavior in a transient state, which includes high frequencies. Moreover, high frequency transformer models are often too complex or require confidential information on transformer geometry. However, in the design stage of insulation coordination it is particularly important to accurately calculate transmitted overvoltages through transformers. In the scope of this paper two different transformer models for high frequency, are developed in an EMTP-type software program. The first model named “Black box” derives solely from the values measured on the transformer terminals and does not require any knowledge of the transformer inner geometry. The second model named “Grey box”, is based on a lumped RLC parameters network, whose values are derived from the simple geometry of the transformer window and from the nameplate data. Furthermore, the models’ capabilities to characterize a transformer at high frequencies are analyzed. The case study is done on a distribution transformer which is to be located inside a power plant. The transmitted overvoltages calculated with the models in the EMTP-type software program are compared with measurements.
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Tikhonov, A. I., A. A. Karzhevin, A. V. Stulov, D. M. Tikhomirov i V. E. Rozin. "Technology for simulation models of power transformers with arbitrary design of active part". Vestnik IGEU, nr 4 (31.08.2023): 28–35. http://dx.doi.org/10.17588/2072-2672.2023.4.028-035.
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Existing simulation models of power transformers that operate, for example, in the MATLAM Simulink SimPowerSystem environment, are based on an ideal transformer model. It allows using this model as an autonomous link in a complex electrical circuit. The main disadvantage of such models is the difficulty to consider non-standard versions of the magnetic circuit and special schemes for connecting winding elements when modeling. Particularly significant problems arise when modeling special transformers of the certain classes. At the same time, traditionally, when modeling the transient modes of transformers, a different approach is used. It is based on the use of inductance matrices which allows considering all the design features of the active part of the transformer. The disadvantage of this approach is the need to describe an external electrical circuit in addition to the transformer. Therefore, the problem to develop a modern technology for simulation models of transformers with an arbitrary design of the active part is topical. This model meets the requirement for the autonomy of the transformer model from the external circuit model. The authors have used the methods of modeling electrical and magnetic circuits based on the theory of ordinary differential equations, and simulation method using the MatLab Simulink SimPowerSystems package. A technology has been developed for simulation models of transformers with an arbitrary design of the active part based on the use of typical subsystems. An algorithm for matrix of inductances based on the main magnetic field of the transformer with the existing equivalent circuit of the magnetic circuit is given. The authors have presented a diagram of a three-phase transformer model developed using the proposed technology, as well as the results of comparing the current curves in the primary and secondary windings of the transformer when it is turned on at idle and with a resistive load. The results are obtained using the existing and new models. The results of the study can be used to design general industrial and special transformers in design companies and in manufacturing environment. The developed technology can give a special effect when it is used at the R&D stage to study the operating modes of transformers in case the enterprise has no experience to design and manufacture them.
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Nazarychev, Alexandr, Dmitriy Andreev, Alexey Tadjibaev, Svetlana Vysogorets i Ilia Sulynenkov. "Methods for calculation of the marginal exploitation lifespan of power transformers 35 rV and higher based on the state index". E3S Web of Conferences 58 (2018): 02006. http://dx.doi.org/10.1051/e3sconf/20185802006.
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A methods for determining the marginal exploitation lifespan of a transformer based on a complex diagnostic survey was developed. The integral evaluation of the technical state of the transformers is performed according to the value of the condition index. An example of the marginal exploitation lifespan calculation of the transformer according to the real initial data is given. The application of the methods will allow to proceed to the planning of technical re-equipment and reconstruction of energy facilities taking into account the technical condition of the equipment.
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Macdonald-Ross, Michael, i Robert Waller. "The transformer revisited". Information Design Journal 9, nr 2-3 (1.01.2000): 177–93. http://dx.doi.org/10.1075/idj.9.2-3.06mac.
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Written in 1974 while the authors were with the Open University, this paper first appeared in the 1976 Penrose Annual. The original abstract, written by the Penrose editor, read: Break down the barriers in the interests of the reader. Take responsibility for the success or failure of the communication. Do not accept a label or a slot on a production line. Be a complete human being with moral and intellectual integrity and thoroughgoing technical competence. This is the message of this article by two highly professional communicators at the Institute of Educational Technology of the Open University, Milton Keynes. It examines the range of complex problems involved in putting the expert's message in a form the ordinary person can best understand and use. It is reprinted here with minor changes that mostly reflect the current un acceptability of the pronoun 'he' used generi-cally. The authors have also added a 2000 Postscript.
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Gomathy, V., i Dr S. Sumathi. "IMPLEMENTATION OF SVM USING SEQUENTIAL MINIMAL OPTIMIZATION FOR POWER TRANSFORMER FAULT ANALYSIS USING DGA". INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 10, nr 5 (20.08.2013): 1687–99. http://dx.doi.org/10.24297/ijct.v10i5.4153.
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Reliable operations of power transformers are necessary for effective transmission and distribution of power supply. During normal functions of the power transformer, distinct types of faults occurs due to insulation failure, oil aging products, overheating of windings, etc., affect the continuity of power supply thus leading to serious economic losses. To avoid interruptions in the power supply, various software fault diagnosis approaches are developed to detect faults in the power transformer and eliminate the impacts. SVM and SVM-SMO are the software fault diagnostic techniques developed in this paper for the continuous monitoring and analysis of faults in the power transformer. The SVM algorithm is faster, conceptually simple and easy to implement with better scaling properties for few training samples. The performances of SVM for large training samples are complex, subtle and difficult to implement. In order to obtain better fault diagnosis of large training data, SVM is optimized with SMO technique to achieve high interpretation accuracy in fault analysis of power transformer. The proposed methods use Dissolved Gas-in-oil Analysis (DGA) data set obtained from 500 KV main transformers of Pingguo Substation in South China Electric Power Company. DGA is an important tool for diagnosis and detection of incipient faults in the power transformers. The Gas Chromatograph (GC) is one of the traditional methods of DGA, utilized to choose the most appropriate gas signatures dissolved in transformer oil to detect types of faults in the transformer. The simulations are carried out in MATLAB software with an Intel core 3 processor with speed of 3 GHZ and 2 GB RAM PC. The results obtained by optimized SVM and SVM-SMO are compared with the existing SVM classification techniques. The test results indicate that the SVM-SMO approach significantly improve the classification accuracy and computational time for power transformer fault classification.
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Hütten, Nils, Richard Meyes i Tobias Meisen. "Vision Transformer in Industrial Visual Inspection". Applied Sciences 12, nr 23 (23.11.2022): 11981. http://dx.doi.org/10.3390/app122311981.
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Artificial intelligence as an approach to visual inspection in industrial applications has been considered for decades. Recent successes, driven by advances in deep learning, present a potential paradigm shift and have the potential to facilitate an automated visual inspection, even under complex environmental conditions. Thereby, convolutional neural networks (CNN) have been the de facto standard in deep-learning-based computer vision (CV) for the last 10 years. Recently, attention-based vision transformer architectures emerged and surpassed the performance of CNNs on benchmark datasets, regarding regular CV tasks, such as image classification, object detection, or segmentation. Nevertheless, despite their outstanding results, the application of vision transformers to real world visual inspection is sparse. We suspect that this is likely due to the assumption that they require enormous amounts of data to be effective. In this study, we evaluate this assumption. For this, we perform a systematic comparison of seven widely-used state-of-the-art CNN and transformer based architectures trained in three different use cases in the domain of visual damage assessment for railway freight car maintenance. We show that vision transformer models achieve at least equivalent performance to CNNs in industrial applications with sparse data available, and significantly surpass them in increasingly complex tasks.
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Sun, Yi Yun, Xin Wang, Yi Hui Zheng, Li Xue Li i Qing Shan Xu. "Power Transformer Life Analysis Based on Risk Assessment". Applied Mechanics and Materials 672-674 (październik 2014): 1151–54. http://dx.doi.org/10.4028/www.scientific.net/amm.672-674.1151.
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In order to grasp the state of full life cycle of the power transformer, A new method for power transformer life analysis based on risk assessment is proposed in this paper. Firstly power transformer risk assessment is conducted and fault tree is established accordingly to make the complex transformer faults system subdivide into various kinds of basic types directly; Secondly, the Fuzzy Analytical Hierarchy Process (FAHP) is designed to analyze the fault tree, so the complex system of transformer can be quantitatively described and the reliability of transformer can be obtained; Finally, transformer aging model is established to research the change rules of the transformer runnung state and determine the duration that the transformer stays in each stages of life. In addition, the application result shows the feasibility and effectiveness of the method above.
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Li, Xiaopeng, Xiaoyu Chen, Jialin Yang i Shuqin Li. "Transformer helps identify kiwifruit diseases in complex natural environments". Computers and Electronics in Agriculture 200 (wrzesień 2022): 107258. http://dx.doi.org/10.1016/j.compag.2022.107258.
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Jiao, Lingxiao, Yongle Wu, Zheng Zhuang, Mingxing Li i Yuanan Liu. "Multiband DC‐block impedance transformer for extreme complex impedances". Electronics Letters 54, nr 2 (styczeń 2018): 105–7. http://dx.doi.org/10.1049/el.2017.3698.
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Tan, Kaijun, Wenyu Mao, Xiaozhou Guo, Huaxiang Lu, Chi Zhang, Zhanzhong Cao i Xingang Wang. "CST: Complex Sparse Transformer for Low-SNR Speech Enhancement". Sensors 23, nr 5 (21.02.2023): 2376. http://dx.doi.org/10.3390/s23052376.
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Speech enhancement tasks for audio with a low SNR are challenging. Existing speech enhancement methods are mainly designed for high SNR audio, and they usually use RNNs to model audio sequence features, which causes the model to be unable to learn long-distance dependencies, thus limiting its performance in low-SNR speech enhancement tasks. We design a complex transformer module with sparse attention to overcome this problem. Different from the traditional transformer model, this model is extended to effectively model complex domain sequences, using the sparse attention mask balance model’s attention to long-distance and nearby relations, introducing the pre-layer positional embedding module to enhance the model’s perception of position information, adding the channel attention module to enable the model to dynamically adjust the weight distribution between channels according to the input audio. The experimental results show that, in the low-SNR speech enhancement tests, our models have noticeable performance improvements in speech quality and intelligibility, respectively.
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Kastawan, I. Made Wiwit, Ewin Yusuf i Afif Fadhilah. "Design of Phase-Shifting Transformer Based on Simulink Matlab Simulation". Current Journal: International Journal Applied Technology Research 1, nr 2 (1.10.2020): 148–62. http://dx.doi.org/10.35313/ijatr.v1i2.30.
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Using variable speed drive for controlling speed of three-phase induction motor in industrial sector gives an advantage of reducing consumption of electrical energy; on the other hand, it also causes a disadvantage of source current harmonic. To solve the problem of source current harmonic, a method of using phase-shifting transformer is applied. This method may be applied in a system with two VSDs or more connected to a three-phase power supply. The application of this phase-shifting transformer method could be as simple as using of two transformer with Y-y (wye-wye) and Y-d (wye-delta) three-phase winding connections to give a phase-shifting of 30 or more complex as it uses two transformer with Y-y and Y-z (wye-zigzag) three-phase winding connections to give a phase-shifting less than 30. This paper proposes design of five different phase-shifting transformer configurations to produce 30, 20, 15, 12 and 10 phase-shifting. Simulation on a computer-based software, Simulink Matlab, then confirmed that the proposed phase-shifting transformer design gives a very accurate result regarding to phase-shifting and magnitudes of input and output voltage of the phase-shifting transformers.
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Mwaniki, Fredrick, i Ahmed A. Sayyid. "Characterizing power transformer frequency responses using bipolar pseudo-random current impulses". International Journal of Power Electronics and Drive Systems (IJPEDS) 12, nr 4 (1.12.2021): 2423. http://dx.doi.org/10.11591/ijpeds.v12.i4.pp2423-2434.
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The behaviour of a power transformer is complex and difficult to predict during transient conditions or during operation at frequencies below or above its nominal frequency, a phenomenon common in renewable energy plants due to harmonic distortion. Furthermore, the accuracy of a power system simulation depends on the models of critical subsystems such as the power transformers. This paper presents the use of a unique excitation waveform comprising of pseudo-random current impulses to accurately identify the wideband characteristics of a power transformer. By injecting the excitation waveform to the relevant transformer terminals, frequency responses are determined by cross-correlation of the perturbation signal, and the measured response. Compared to the traditional transformer identification methods, the pseudo-random current impulses offer a wideband excitation with a higher degree of controllability such that its spectral energy can be focused in the frequency band of interest. The proposed method was investigated on a 16 kVA, 22 kV/240 V single-phase transformer. The obtained wideband frequency responses provide useful information in harmonic penetration and over-voltage studies and are also used to estimate, with a high degree of accuracy, the lumped parameters of the equivalent transformer broadband circuit model.
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Ricci, Stefano, i Dario Russo. "Linear Ultrasound Transmitter Based on Transformer with Improved Saturation Performance". Electronics 10, nr 2 (7.01.2021): 107. http://dx.doi.org/10.3390/electronics10020107.
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Ultrasound methods are currently employed in a wide range of applications. They are integrated in complex electronics systems, like clinical echographs, but also in small and compact boards, like industrial sensors, embedded systems, and portable devices. Ultrasound waves are typically generated by energizing a piezoelectric transducer through a high-voltage sequence of small sinusoidal bursts. Moreover, in several applications, the ultrasound board should work in a wide frequency range. This makes the transmitter, i.e., the electronics that drives the transducer, a key part of the circuit. The use of a small transformer simplifies the electronics and reduces the need of high-voltage power sources. Unfortunately, the transformer magnetic core, when subjected to the sequence of bursts employed in ultrasound, is particularly prone to saturation. This phenomenon limits the maximum voltage and/or the minimum frequency the transformer can be employed for. In this work, a transmitter based on a transformer is proposed. Inspired by the technique currently employed in the power network transformers, we added a prefluxing circuit, which improves the saturation performance 2-fold. The proposed transmitter was implemented in a test board and experimented with two commercial transformers at 80 Vpp. Measurements show that the proposed prefluxing circuit moves down the minimum usable frequency 2-fold: from 400 to 200 kHz for one of the two transformers, and from 2.4 to 1.2 MHz for the other.
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Ricci, Stefano, i Dario Russo. "Linear Ultrasound Transmitter Based on Transformer with Improved Saturation Performance". Electronics 10, nr 2 (7.01.2021): 107. http://dx.doi.org/10.3390/electronics10020107.
Pełny tekst źródłaStreszczenie:
Ultrasound methods are currently employed in a wide range of applications. They are integrated in complex electronics systems, like clinical echographs, but also in small and compact boards, like industrial sensors, embedded systems, and portable devices. Ultrasound waves are typically generated by energizing a piezoelectric transducer through a high-voltage sequence of small sinusoidal bursts. Moreover, in several applications, the ultrasound board should work in a wide frequency range. This makes the transmitter, i.e., the electronics that drives the transducer, a key part of the circuit. The use of a small transformer simplifies the electronics and reduces the need of high-voltage power sources. Unfortunately, the transformer magnetic core, when subjected to the sequence of bursts employed in ultrasound, is particularly prone to saturation. This phenomenon limits the maximum voltage and/or the minimum frequency the transformer can be employed for. In this work, a transmitter based on a transformer is proposed. Inspired by the technique currently employed in the power network transformers, we added a prefluxing circuit, which improves the saturation performance 2-fold. The proposed transmitter was implemented in a test board and experimented with two commercial transformers at 80 Vpp. Measurements show that the proposed prefluxing circuit moves down the minimum usable frequency 2-fold: from 400 to 200 kHz for one of the two transformers, and from 2.4 to 1.2 MHz for the other.
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Behjat, Vahid, Reza Emadifar, Mehrdad Pourhossein, U. Mohan Rao, Issouf Fofana i Reza Najjar. "Improved Monitoring and Diagnosis of Transformer Solid Insulation Using Pertinent Chemical Indicators". Energies 14, nr 13 (2.07.2021): 3977. http://dx.doi.org/10.3390/en14133977.
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Transformers are generally considered to be the costliest assets in a power network. The lifetime of a transformer is mainly attributable to the condition of its solid insulation, which in turn is measured and described according to the degree of polymerization (DP) of the cellulose. Since the determination of the DP index is complex and time-consuming and requires the transformer to be taken out of service, utilities prefer indirect and non-invasive methods of determining the DP based on the byproduct of cellulose aging. This paper analyzes solid insulation degradation by measuring the furan concentration, recently introduced methanol, and dissolved gases like carbon oxides and hydrogen, in the insulating oil. A group of service-aged distribution transformers were selected for practical investigation based on oil samples and different kinds of tests. Based on the maintenance and planning strategy of the power utility and a weighted combination of measured chemical indicators, a neural network was also developed to categorize the state of the transformer in certain classes. The method proved to be able to improve the diagnostic capability of chemical indicators, thus providing power utilities with more reliable maintenance tools and avoiding catastrophic failure of transformers.
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Wu, Bo, Chao Fang, Zhenjie Yao, Yanhui Tu i Yixin Chen. "Decompose Auto-Transformer Time Series Anomaly Detection for Network Management". Electronics 12, nr 2 (10.01.2023): 354. http://dx.doi.org/10.3390/electronics12020354.
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Time series anomaly detection through unsupervised methods has been an active research area in recent years due to its enormous potential for networks management. The representation and reconstruction of time series have made extraordinary progress in existing works. However, time series is known to be complex in terms of their temporal dependency and stochasticity, which makes anomaly detection difficult. To this end, we propose a novel approach based on a decomposition auto-transformer networks(DATN) for time series anomaly detection. The time series is decomposed into seasonal and trend components, and renovated as a basic inner block deep model. With this design, transformers can decompose complex time series in a progressive manner. We also design an auto-transfomer block that determines dependencies and representation aggregation at the sub-series level based on series seasonal and trend components. Moreover, the complex transformer decoder is replaced by a simple linear decoder, which makes the model more efficient. Extensive experiments on various public benchmarks demonstrate that our method has achieved state-of-the-art performance.
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Gracia-Velásquez, David Gilberto, Andrés Steven Morales-Rodríguez i Oscar Danilo Montoya. "Application of the Crow Search Algorithm to the Problem of the Parametric Estimation in Transformers Considering Voltage and Current Measures". Computers 11, nr 1 (6.01.2022): 9. http://dx.doi.org/10.3390/computers11010009.
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The problem of the electrical characterization of single-phase transformers is addressed in this research through the application of the crow search algorithm (CSA). A nonlinear programming model to determine the series and parallel impedances of the transformer is formulated using the mean square error (MSE) between the voltages and currents measured and calculated as the objective function. The CSA is selected as a solution technique since it is efficient in dealing with complex nonlinear programming models using penalty factors to explore and exploit the solution space with minimum computational effort. Numerical results in three single-phase transformers with nominal sizes of 20 kVA, 45 kVA, 112.5 kVA, and 167 kVA demonstrate the efficiency of the proposed approach to define the transformer parameters when compared with the large-scale nonlinear solver fmincon in the MATLAB programming environment. Regarding the final objective function value, the CSA reaches objective functions lower than 2.75×10−11 for all the simulation cases, which confirms their effectiveness in minimizing the MSE between real (measured) and expected (calculated) voltage and current variables in the transformer.
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Li, Chao, Jie Chen, Cheng Yang, Jingjian Yang, Zhigang Liu i Pooya Davari. "Convolutional Neural Network-Based Transformer Fault Diagnosis Using Vibration Signals". Sensors 23, nr 10 (16.05.2023): 4781. http://dx.doi.org/10.3390/s23104781.
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Fast and accurate fault diagnosis is crucial to transformer safety and cost-effectiveness. Recently, vibration analysis for transformer fault diagnosis is attracting increasing attention due to its ease of implementation and low cost, while the complex operating environment and loads of transformers also pose challenges. This study proposed a novel deep-learning-enabled method for fault diagnosis of dry-type transformers using vibration signals. An experimental setup is designed to simulate different faults and collect the corresponding vibration signals. To find out the fault information hidden in the vibration signals, the continuous wavelet transform (CWT) is applied for feature extraction, which can convert vibration signals to red-green-blue (RGB) images with the time–frequency relationship. Then, an improved convolutional neural network (CNN) model is proposed to complete the image recognition task of transformer fault diagnosis. Finally, the proposed CNN model is trained and tested with the collected data, and its optimal structure and hyperparameters are determined. The results show that the proposed intelligent diagnosis method achieves an overall accuracy of 99.95%, which is superior to other compared machine learning methods.
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G. Deakin, Anthony, Duncan H. Smith, Joseph W. Spencer, Darren Jones i Nigel Johnson. "Chromatic acoustic condition monitoring of transformers". Sensor Review 34, nr 3 (10.06.2014): 291–96. http://dx.doi.org/10.1108/sr-04-2013-663.
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Purpose – The purpose of this paper is to present an approach for continuous acoustic condition monitoring of transformers based on chromatic principles for abstracting information on individual acoustic events as well as secondary trends in the behaviour of the events. Design/methodology/approach – The potential benefits of condition monitoring of high-value transformer equipment are explored, and an approach based on chromatic information abstraction is illustrated and discussed. Findings – Tracking of large numbers of complex and variable individual acoustic events over time using a chromatic approach appears to offer a means for remote operators to evaluate mechanical transformer tap changer condition in a traceable manner. Originality/value – The condition monitoring is retrofittable and non-intrusive, and the approach may be applied generically for combining condition indicators for overall health-checking. A complex system behaviour may be operationally simplified without discarding the complexity.
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Sarraf, Saman, Arman Sarraf, Danielle D. DeSouza, John A. E. Anderson i Milton Kabia. "OViTAD: Optimized Vision Transformer to Predict Various Stages of Alzheimer’s Disease Using Resting-State fMRI and Structural MRI Data". Brain Sciences 13, nr 2 (3.02.2023): 260. http://dx.doi.org/10.3390/brainsci13020260.
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Advances in applied machine learning techniques for neuroimaging have encouraged scientists to implement models to diagnose brain disorders such as Alzheimer’s disease at early stages. Predicting the exact stage of Alzheimer’s disease is challenging; however, complex deep learning techniques can precisely manage this. While successful, these complex architectures are difficult to interrogate and computationally expensive. Therefore, using novel, simpler architectures with more efficient pattern extraction capabilities, such as transformers, is of interest to neuroscientists. This study introduced an optimized vision transformer architecture to predict the group membership by separating healthy adults, mild cognitive impairment, and Alzheimer’s brains within the same age group (>75 years) using resting-state functional (rs-fMRI) and structural magnetic resonance imaging (sMRI) data aggressively preprocessed by our pipeline. Our optimized architecture, known as OViTAD is currently the sole vision transformer-based end-to-end pipeline and outperformed the existing transformer models and most state-of-the-art solutions. Our model achieved F1-scores of 97%±0.0 and 99.55%±0.39 from the testing sets for the rs-fMRI and sMRI modalities in the triple-class prediction experiments. Furthermore, our model reached these performances using 30% fewer parameters than a vanilla transformer. Furthermore, the model was robust and repeatable, producing similar estimates across three runs with random data splits (we reported the averaged evaluation metrics). Finally, to challenge the model, we observed how it handled increasing noise levels by inserting varying numbers of healthy brains into the two dementia groups. Our findings suggest that optimized vision transformers are a promising and exciting new approach for neuroimaging applications, especially for Alzheimer’s disease prediction.
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Manusov, V. Z., i D. M. Ivanov. "Study of Electromagnetic and Thermal Transients in a High-temperature Superconducting Transformer during a Short Circuit". Problems of the Regional Energetics, nr 2(58) (maj 2023): 1–12. http://dx.doi.org/10.52254/1857-0070.2023.2-58.01.
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Today, high-temperature superconducting (HTS) current limiters and transformers allow to limit the surge short circuit current during failure without negatively affecting on the power grid complex at the normal operation mode. However, the transition of a superconductor to a resistive state at the moment of current limitation can cause significant heat generation, which can destroy the transformer windings. The research goal is to provide optimal technical characteristics of the HTS transformer to achieve effective short circuit current limitation and prevent thermal breakdown of its windings. To achieve this goal, a mathematical model of a HTS transformer was developed. The presented method considers the material type and geometry of the superconducting tape, the critical parameters of the superconductor (current and temperature), the parameters of the cryogenic liquid, dependence of the resistance and heat capacity of the HTS tape layers on temperature. The simulation model was created in the Matlab/Simulink software. The most important result is the possibility of obtaining optimal electrical and thermal parameters of the HTS transformer windings during the short circuit current limitation, as well as ensuring the thermal stability of the superconducting tape at the quench moment. The obtained results are significant in the design and operation of HTS transformers. For efficient and safe operation in the current-limiting mode, it is necessary to take into account heat generation on the transformer windings. It is important for the superconductor returning to the superconducting state without causing significant overheating of the windings.
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Sun, Yi Yun, Xin Wang, Yi Hui Zheng, Li Xue Li i Qing Shan Xu. "Application of FTA and Improved FAHP in Power Transformer Risk Assessment". Advanced Materials Research 860-863 (grudzień 2013): 2157–60. http://dx.doi.org/10.4028/www.scientific.net/amr.860-863.2157.
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For fast and accurately assessing the risk of the transformer failure and getting the reliability of transformer components, it is proposed a new method based on Fault Tree Analysis (FTA) and Improved Fuzzy Analytic Hierarchy Process (IFAHP) in this paper. Firstly power transformer fault tree is established accordingly to make the complex transformer faults system subdivide into various kinds of basic types directly; Secondly, the IFAHP is designed to analyze the fault tree, so the complex system of transformer can be quantitatively described and the fuzzy judgment matrix can be established; Finally, the fuzzy consistent qualification is changed into a mathematical programming problem and Genetic Algorithms (GA) is adopted to get the solution so as to obtain the reliability of transformer components. In addition, the application result shows the effectiveness of the method above.
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Ibrahim, Khalid H., Nourhan R. Korany i Saber M. Saleh. "Effects of VA Rating on the Fault Diagnosis of Power Transformer Using SFRA Test". European Journal of Electrical Engineering 23, nr 5 (31.10.2021): 381–89. http://dx.doi.org/10.18280/ejee.230504.
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The electric power transformer is an essential part of an electrical power system since it is used to step up or down voltage levels to maintain the system performance as well as possible. Frequency response analysis (FRA) is one of the most widely used techniques for detecting various types of mechanical damage in transformers. The equivalent circuit of the transformer will be represented by a complex network of R, L, and C elements in the FRA technique. For transformer faults diagnosis, various calculation techniques and diagnostic techniques may be used, such as acoustic emission analysis, thermal images of electromagnetic radiation, transformer temperature, and humidity analysis. SFRA test is one of these techniques that could be used to determine the fault type based on its response over a wide frequency range. The main challenge of the SFRA test is that the functional interpretation requirement for this test is not universally accepted Also statistical features are defined for this SFRA response to be used in fault detection and classification. In this paper, the effect of the transformer rating on the fault diagnosis techniques using SFRA is tested. Also, the effect of the transformer VA rating on the statistical parameters and the classification rules of fault diagnosis is discussed. Finally, the features used in fault diagnosis are ranked according to its independence of the transformer rating resulting in a more accurate matching fault diagnosis technique.
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Malygin, Dmitry Vladimirovich, Maksim Vladimirovich Borodin, Roman Pavlovich Belikov, Yulia Lyusievna Mikhaylova i Zumeyra Munirovna Shakurova. "The Development of A Universal Transformer Housing of the Unit Transformer Substation 6-10 / 0.4 KV". E3S Web of Conferences 288 (2021): 01097. http://dx.doi.org/10.1051/e3sconf/202128801097.
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The company group of the PJSC “ROSSETI” pays great attention to reducing occupational hazard at the facilities of the power grid complex. The analysis of the accidents in the branch of PJSC “IGDC of the Center”– “Orelenergo” revealed that transformer housings installed at the mast-type transformer substations 6-10 / 0.4 kV can’t fully provide the required safety level as they can be slightly raised even without using a special tool, and therefore one can stick his hands or some objects to the current-carrying parts of the transformer substation. According to the statistics of the damages at the mast-type transformer substations 6-10 / 0.4 kV various small animals and birds can enter the electrical installation through small slits between the housing and the transformer, which will lead to different emergency situations. To prevent the aforementioned negative consequences, a universal transformer housing was developed for a mast-type unit transformer substation (UTS) 6-10 / 0.4 kV. The offered design of the housing is universal, since it can be used for the transformers of various capacities; for its manufacture tools and materials with different characteristics can be used. At the same time, the installation of the developed housing will allow power grid companies to reduce occupational hazard, reduce the undersupply of electricity and increase the reliability of power supply to the agricultural consumers. The technical solution presented in the article can be applied for the mast-type UTS 6-10 / 0.4 kV in the post-Soviet countries.
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Chistyakov, Yury S., Elena V. Kholodova, Alexey S. Minin, Hans-Georg Zimmermann i Alois Knoll. "Modeling of Electric Power Transformer Using Complex-Valued Neural Networks". Energy Procedia 12 (2011): 638–47. http://dx.doi.org/10.1016/j.egypro.2011.10.087.
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Dawei, Huang. "Novel realization of hilbert transformer using complex wave digital filters". Journal of Electronics (China) 3, nr 3 (lipiec 1986): 180–88. http://dx.doi.org/10.1007/bf02778879.
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Ortiz-Zambrano, Jenny A., César Espin-Riofrio i Arturo Montejo-Ráez. "Combining Transformer Embeddings with Linguistic Features for Complex Word Identification". Electronics 12, nr 1 (27.12.2022): 120. http://dx.doi.org/10.3390/electronics12010120.
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Identifying which words present in a text may be difficult to understand by common readers is a well-known subtask in text complexity analysis. The advent of deep language models has also established the new state-of-the-art in this task by means of end-to-end semi-supervised (pre-trained) and downstream training of, mainly, transformer-based neural networks. Nevertheless, the usefulness of traditional linguistic features in combination with neural encodings is worth exploring, as the computational cost needed for training and running such networks is becoming more and more relevant with energy-saving constraints. This study explores lexical complexity prediction (LCP) by combining pre-trained and adjusted transformer networks with different types of traditional linguistic features. We apply these features over classical machine learning classifiers. Our best results are obtained by applying Support Vector Machines on an English corpus in an LCP task solved as a regression problem. The results show that linguistic features can be useful in LCP tasks and may improve the performance of deep learning systems.