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S., S., Thulasi Bikku, P. Muthukumar, K. Sandeep, Jampani Chandra Sekhar, and V. Krishna Pratap. "Enhanced Intrusion Detection Using Stacked FT-Transformer Architecture." Journal of Cybersecurity and Information Management 8, no. 2 (2024): 19–29. http://dx.doi.org/10.54216/jcim.130202.
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The function of network intrusion detection systems (NIDS) in protecting networks from cyberattacks is crucial. Many of the more conventional techniques rely on signature-based approaches, which have a hard time distinguishing between various types of assaults. Using stacked FT-Transformer architecture, this research suggests a new way to identify intrusions in networks. When it comes to dealing with complicated tabular data, FT-Transformers—a variant of the Transformer model—have shown outstanding performance. Because of the inherent tabular nature of network traffic data, FT-Transformers are an attractive option for intrusion detection jobs. In this area, our study looks at how FT-Transformers outperform more conventional machine learning (ML) methods. Our working hypothesis is that, in comparison to single-layered ML models, FT-Transformers will achieve better detection accuracy due to their intrinsic capacity to grasp long-range correlations in network traffic data. We also test the FT-Transformer model on several network traffic datasets that include various protocols and attack kinds to see how well it performs and how generalizable it is. The purpose of this research is to shed light on how well and how versatile FT-Transformers perform for detecting intrusions in networks. We aim to prove that FT-Transformers can secure networks from ever-changing cyber threats by comparing their performance to that of classic ML models and by testing their generalizability.
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Krupa, Tadeusz. "Elements of Theory of the Correct Operations of Logistics Transforming Networks." Foundations of Management 9, no. 1 (December 20, 2017): 347–60. http://dx.doi.org/10.1515/fman-2017-0026.
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Abstract In this paper, transformer logistics networks are treated as flow models of discrete manufacturing systems (FMDMS). The purpose is to formalize FMDMS into logical formulas called transformer functions. Transformer logistics networks are able to handle buffers and their production orders in a way that ensures full monitoring of the logic technology stored in the memory of a transforming network (t-network). The structural and functional complexity of the t-network makes it impossible to carry out formal proof of its proper functioning for any new order placement in buffers and transformers. This is because with the growing capacity in buffers, the number of available states of tnetworks also increases, and as such, the number of transformers and buffers unable to effectively generate new production orders that protect the t-network is also increasing. The problem therefore becomes to maintain t-network equilibrium technology that guarantees the continuity of the logical operations and processes of resource transformation.
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Zhang, Fuping, Pengcheng Zhao, and Jianming Wei. "Channel Transformer Network." IEEE Access 8 (2020): 220762–78. http://dx.doi.org/10.1109/access.2020.3042644.
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Alharthi, Musleh, and Ausif Mahmood. "Enhanced Linear and Vision Transformer-Based Architectures for Time Series Forecasting." Big Data and Cognitive Computing 8, no. 5 (May 16, 2024): 48. http://dx.doi.org/10.3390/bdcc8050048.
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Time series forecasting has been a challenging area in the field of Artificial Intelligence. Various approaches such as linear neural networks, recurrent linear neural networks, Convolutional Neural Networks, and recently transformers have been attempted for the time series forecasting domain. Although transformer-based architectures have been outstanding in the Natural Language Processing domain, especially in autoregressive language modeling, the initial attempts to use transformers in the time series arena have met mixed success. A recent important work indicating simple linear networks outperform transformer-based designs. We investigate this paradox in detail comparing the linear neural network- and transformer-based designs, providing insights into why a certain approach may be better for a particular type of problem. We also improve upon the recently proposed simple linear neural network-based architecture by using dual pipelines with batch normalization and reversible instance normalization. Our enhanced architecture outperforms all existing architectures for time series forecasting on a majority of the popular benchmarks.
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Ottele, Andy, and Rahmat Shoureshi. "Neural Network-Based Adaptive Monitoring System for Power Transformer." Journal of Dynamic Systems, Measurement, and Control 123, no. 3 (February 11, 1999): 512–17. http://dx.doi.org/10.1115/1.1387248.
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Power transformers are major elements of the electric power transmission and distribution infrastructure. Transformer failure has severe economical impacts from the utility industry and customers. This paper presents analysis, design, development, and experimental evaluation of a robust failure diagnostic technique. Hopfield neural networks are used to identify variations in physical parameters of the system in a systematic way, and adapt the transformer model based on the state of the system. In addition, the Hopfield network is used to design an observer which provides accurate estimates of the internal states of the transformer that can not be accessed or measured during operation. Analytical and experimental results of this adaptive observer for power transformer diagnostics are presented.
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Majeed, Issah Babatunde, and Nnamdi I. Nwulu. "Impact of Reverse Power Flow on Distributed Transformers in a Solar-Photovoltaic-Integrated Low-Voltage Network." Energies 15, no. 23 (December 6, 2022): 9238. http://dx.doi.org/10.3390/en15239238.
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Modern low-voltage distribution systems necessitate solar photovoltaic (PV) penetration. One of the primary concerns with this grid-connected PV system is overloading due to reverse power flow, which degrades the life of distribution transformers. This study investigates transformer overload issues due to reverse power flow in a low-voltage network with high PV penetration. A simulation model of a real urban electricity company in Ghana is investigated against various PV penetration levels by load flows with ETAP software. The impact of reverse power flow on the radial network transformer loadings is examined for high PV penetrations. Using the least squares method, simulation results are modelled in Excel software. Transformer backflow limitations are determined by correlating operating loads with PV penetration. At high PV penetration, the models predict reverse power flow into the transformer. Interpolations from the correlation models show transformer backflow operating limits of 78.04 kVA and 24.77% at the threshold of reverse power flow. These limits correspond to a maximum PV penetration limit of 88.30%. In low-voltage networks with high PV penetration; therefore, planners should consider transformer overload limits caused by reverse power flow, which degrades transformer life. This helps select control schemes near substation transformers to limit reverse power flow.
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Adegboye, B. A. "Power Quality Assessment in a Distribution Network." Advanced Materials Research 62-64 (February 2009): 53–59. http://dx.doi.org/10.4028/www.scientific.net/amr.62-64.53.
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The paper explores power quality disturbances on a specified section of the distribution network of a Textile Industry in Kaduna State of Nigeria. The 33kV PHCN incoming to the industry is stepped down to 11kV by a 7.5MVA, 33/11kV three-phase transformer. This transformer supplies various 11/.415kV transformers present in the distribution network. Another 11kV PHCN incoming is used in event of any failure from the 33/11kV transformer. The paper focuses on Transformer No. 1, a 150kVA, 11/.415kV three-phase transformer operating at 0.9 power factor, located at printing and dying (P/D) building 1. Majority of the loads on it are inductive. Measurements were taken at the secondary terminal of this transformer by the use of the Harmonitor 3000 power analyzer, which generates the voltage and current waveforms, power factor, voltage and current total harmonic distortion and the apparent power of the red, yellow and blue phases of the transformer. Analyses of these data reveal the disturbances due to harmonics in the phases and neutral of the transformer. The effect of the harmonic current is seen as poor power factor of the transformer. Considering the observations and analyses of the power quality of the transformer 1 (P/D), the paper proposes some recommendations for improving the power quality of the distribution network under study.
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Kumari, Rekha, Gurpreet Kaur, Aditya Rawat, Harshit Chauhan, Kartik Singh Negi, and Rishi Mishra. "ANALYSIS OF TRANSFORMER-DEEP NEURAL NETWORK USING DEEP LEARNING." International Journal of Engineering Applied Sciences and Technology 8, no. 2 (June 1, 2023): 313–19. http://dx.doi.org/10.33564/ijeast.2023.v08i02.048.
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Transformers were first used for natural language processing (NLP) tasks, but they quickly spread to other deep learning fields, including computer vision. They assess the interdependence of pairs. Attention is a part that enables to dynamically highlight relevant features of the input data (words in the case of text strings, parts of images in the case of visual Transformers). The cost grows continually with the number of tokens. The most common Trans- former Architecture for image classification uses only the Transformer Encoder to transform the various input tokens. However, the decoder component of the traditional Transformer Architecture is also used in a variety of other applications. In this section, we first introduce the Attention Mechanism (Section 1), followed by the Basic Transformer Block, which includes the Vision Transformer (Section 2).
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Sun, Zhiqing, Yi Xuan, ZikaiCao, Jian Liu, Tiechao Dai, Weihao Liu, Gangjin Ye, et al. "Transformer parameter estimation in distribution network based on deformable transformer." Journal of Physics: Conference Series 2758, no. 1 (April 1, 2024): 012006. http://dx.doi.org/10.1088/1742-6596/2758/1/012006.
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Abstract With the large number of distributed power sources and the dynamic change of load, the abnormal parameters of distribution transformers become more and more complicated. So it is particularly important to estimate their parameters accurately. For a low voltage distribution network with a limited number of measuring equipment, a Transformer parameter estimation method based on a Deformable Transformer is proposed in this paper. Firstly, a Transformer parameter estimation model based on a Deformable Transformer network is established by using historical measurement data. Then, a quality evaluation method of parameter estimation is proposed to test the accuracy of parameter estimation. Finally, the effectiveness of the proposed method is verified by practical data.
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Al-Yahya, Maha, Hend Al-Khalifa, Heyam Al-Baity, Duaa AlSaeed, and Amr Essam. "Arabic Fake News Detection: Comparative Study of Neural Networks and Transformer-Based Approaches." Complexity 2021 (April 16, 2021): 1–10. http://dx.doi.org/10.1155/2021/5516945.
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Fake news detection (FND) involves predicting the likelihood that a particular news article (news report, editorial, expose, etc.) is intentionally deceptive. Arabic FND started to receive more attention in the last decade, and many detection approaches demonstrated some ability to detect fake news on multiple datasets. However, most existing approaches do not consider recent advances in natural language processing, i.e., the use of neural networks and transformers. This paper presents a comprehensive comparative study of neural network and transformer-based language models used for Arabic FND. We examine the use of neural networks and transformer-based language models for Arabic FND and show their performance compared to each other. We also conduct an extensive analysis of the possible reasons for the difference in performance results obtained by different approaches. The results demonstrate that transformer-based models outperform the neural network-based solutions, which led to an increase in the F1 score from 0.83 (best neural network-based model, GRU) to 0.95 (best transformer-based model, QARiB), and it boosted the accuracy by 16% compared to the best in neural network-based solutions. Finally, we highlight the main gaps in Arabic FND research and suggest future research directions.
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Kirui, Kemei Peter, David K. Murage, and Peter K. Kihato. "Impacts of Placement of Wind Turbine Generators on IEEE 13 Node Radial Test Feeder In-Line Transformer Fuse-Fuse Protection Coordination." European Journal of Engineering Research and Science 5, no. 6 (June 7, 2020): 665–74. http://dx.doi.org/10.24018/ejers.2020.5.6.1939.
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The ever increasing global demand on the electrical energy has lead to the integration of Distributed Generators (DGs) onto the distribution power systems networks to supplement on the deficiencies on the electrical energy generation capacities. The high penetration levels of DGs on the electrical distribution networks experienced over the past decade calls for the grid operators to periodically and critically asses the impacts brought by the DGs on the distribution network operations. The assessment on the impacts brought by the DGs on the distribution network operations is done by simulating the dynamic response of the network to major disturbances occurring on the network like the faults once the DGs have been connected into it. Connection of Wind Turbine Generators (WTGs) into a conventional electrical energy distribution network has great impacts on the short circuit current levels experienced during a fault and also on the protective devices used in protecting the distribution network equipment namely; the transformers, the overhead distribution lines, the underground cables and the line compensators and the shunt capacitors commonly used/found on the relatively long rural distribution feeders. The main factors which contribute to the impacts brought by the WTGs integration onto a conventional distribution network are: The location of interconnecting the WTG/s into the distribution feeder; The size/s of the WTG/s in terms of their electrical wattage penetrating the distribution network; And the type of the WTG interfacing technology used labeled/classified as, Type I, Type II, Type III and Type IV WTGs. Even though transformers are the simplest and the most reliable devices in an electrical power system, transformer failures can occur due to internal or external conditions that make the transformer incapable of performing its proper functions. Appropriate transformer protection should be used with the objectives of protecting the electrical power system in case of a transformer failure and also to protect the transformer itself from the power system disturbances like the faults. This paper was to investigate the effects of integrating WTGs on a distribution transformer Fuse-Fuse conventional protection coordination scheme. The radial distribution feeder studied was the IEEE 13 node radial test feeder and it was simulated using the Electrical Transient Analysis Program (ETAP) software for distribution transformer Fuse-Fuse protection coordination analysis. The IEEE 13 Node radial test feeder In-line transformer studied is a three-phase step down transformer having a star solidly grounded primary winding supplied at and a star solidly grounded secondary winding feeding power at a voltage of . The increase on the short circuit currents at the In-line transformer nodes due to the WTG integration continuously reduces the time coordination margins between the upstream fuse F633 and the downstream fuse F634 used to protect the transformer.
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Kirui, Kemei Peter, David K. Murage, and Peter K. Kihato. "Impacts of Placement of Wind Turbine Generators on IEEE 13 Node Radial Test Feeder In-Line Transformer Fuse-Fuse Protection Coordination." European Journal of Engineering and Technology Research 5, no. 6 (June 7, 2020): 665–74. http://dx.doi.org/10.24018/ejeng.2020.5.6.1939.
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The ever increasing global demand on the electrical energy has lead to the integration of Distributed Generators (DGs) onto the distribution power systems networks to supplement on the deficiencies on the electrical energy generation capacities. The high penetration levels of DGs on the electrical distribution networks experienced over the past decade calls for the grid operators to periodically and critically asses the impacts brought by the DGs on the distribution network operations. The assessment on the impacts brought by the DGs on the distribution network operations is done by simulating the dynamic response of the network to major disturbances occurring on the network like the faults once the DGs have been connected into it. Connection of Wind Turbine Generators (WTGs) into a conventional electrical energy distribution network has great impacts on the short circuit current levels experienced during a fault and also on the protective devices used in protecting the distribution network equipment namely; the transformers, the overhead distribution lines, the underground cables and the line compensators and the shunt capacitors commonly used/found on the relatively long rural distribution feeders. The main factors which contribute to the impacts brought by the WTGs integration onto a conventional distribution network are: The location of interconnecting the WTG/s into the distribution feeder; The size/s of the WTG/s in terms of their electrical wattage penetrating the distribution network; And the type of the WTG interfacing technology used labeled/classified as, Type I, Type II, Type III and Type IV WTGs. Even though transformers are the simplest and the most reliable devices in an electrical power system, transformer failures can occur due to internal or external conditions that make the transformer incapable of performing its proper functions. Appropriate transformer protection should be used with the objectives of protecting the electrical power system in case of a transformer failure and also to protect the transformer itself from the power system disturbances like the faults. This paper was to investigate the effects of integrating WTGs on a distribution transformer Fuse-Fuse conventional protection coordination scheme. The radial distribution feeder studied was the IEEE 13 node radial test feeder and it was simulated using the Electrical Transient Analysis Program (ETAP) software for distribution transformer Fuse-Fuse protection coordination analysis. The IEEE 13 Node radial test feeder In-line transformer studied is a three-phase step down transformer having a star solidly grounded primary winding supplied at and a star solidly grounded secondary winding feeding power at a voltage of . The increase on the short circuit currents at the In-line transformer nodes due to the WTG integration continuously reduces the time coordination margins between the upstream fuse F633 and the downstream fuse F634 used to protect the transformer.
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Moon, Ji-Hwan, Gyuho Choi, Yu-Hwan Kim, and Won-Yeol Kim. "PCTC-Net: A Crack Segmentation Network with Parallel Dual Encoder Network Fusing Pre-Conv-Based Transformers and Convolutional Neural Networks." Sensors 24, no. 5 (February 24, 2024): 1467. http://dx.doi.org/10.3390/s24051467.
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Cracks are common defects that occur on the surfaces of objects and structures. Crack detection is a critical maintenance task that traditionally requires manual labor. Large-scale manual inspections are expensive. Research has been conducted to replace expensive human labor with cheaper computing resources. Recently, crack segmentation based on convolutional neural networks (CNNs) and transformers has been actively investigated for local and global information. However, the transformer is data-intensive owing to its weak inductive bias. Existing labeled datasets for crack segmentation are relatively small. Additionally, a limited amount of fine-grained crack data is available. To address this data-intensive problem, we propose a parallel dual encoder network fusing Pre-Conv-based Transformers and convolutional neural networks (PCTC-Net). The Pre-Conv module automatically optimizes each color channel with a small spatial kernel before the input of the transformer. The proposed model, PCTC-Net, was tested with the DeepCrack, Crack500, and Crackseg9k datasets. The experimental results showed that our model achieved higher generalization performance, stability, and F1 scores than the SOTA model DTrC-Net.
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Vasilevskij, V. V., and M. O. Poliakov. "Reproducing of the humidity curve of power transformers oil using adaptive neuro-fuzzy systems." Electrical Engineering & Electromechanics, no. 1 (February 23, 2021): 10–14. http://dx.doi.org/10.20998/2074-272x.2021.1.02.
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Introduction. One of the parameters that determine the state of the insulation of power transformers is the degree of moisture content of cellulose insulation and transformer oil. Modern systems of continuous monitoring of transformer equipment have the ability to accumulate data that can be used to reproduce the dynamics of moisture content in insulation. The purpose of the work is to reproduce the curve of the of humidity of transformer oil based on the results of measuring the temperature of the upper and lower layers of oil without the need for direct measurement of moisture content by special devices. Methodology. The construction of a fuzzy neural network is carried out using networks based on adaptive neuro-fuzzy system ANFIS. The network generated using the Grid Partition algorithm without clustering and Subtractive Clustering. Results. The paper presents a comparative analysis of fuzzy neural networks of various architectures in terms of increasing the accuracy of reproducing the moisture content of transformer oil. For training and testing fuzzy neural networks, the results of continuous monitoring of the temperature of the upper and lower layers of transformer oil during two months of operation used. Considered twenty four variants of the architecture of ANFIS models, which differ in the membership functions, the number of terms of each input quantity, and the number of training cycles. The results of using the constructed fuzzy neural networks for reproducing the dynamics of moisture content of transformer oil during a month of operation of the transformer are presented. The reproducing accuracy was assessed using the root mean square error and the coefficient of determination. The test results indicate the sufficient adequacy of the proposed models. Consequently, the RMSE value for the network constructed using Grid Partition method was 0.49, and for the network built using the Subtractive Clustering method – 0.40509.
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Lynch, A. C. "Blumlein's transformer-bridge network." Engineering Science and Education Journal 2, no. 3 (1993): 117. http://dx.doi.org/10.1049/esej:19930037.
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Hanus, Oleksii, and Kostiantyn Starkov. "STUDY OF THE NATURE OF OVERVOLTAGES IN THE ELECTRICAL NETWORK ARISING FROM VOLTAGE TRANSFORMERS." Bulletin of the National Technical University "KhPI". Series: Energy: Reliability and Energy Efficiency, no. 1 (2) (July 2, 2021): 28–36. http://dx.doi.org/10.20998/2224-0349.2021.01.05.
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A non-linear dynamic mathematical model of voltage transformer has been considered and overvoltages arising on the elements of voltage transformer equivalent circuit during transient processes have been investigated. The influence of voltage transformer secondary circuit capacitance on overvoltage multiplicity in the primary circuits and the duration of transients has been determined. The advantages of approximation of nonlinearity of voltage transformers by hyperbolic sine are used. Mathematical expressions determining the nature of changes in the forced and free components of the transient process in an electrical network with a voltage transformer have been obtained. It is shown that with the increase of the electric network capacitance the duration of the transition process damping increases and the frequency of the forced oscillations and the level of overvoltage decrease. It is proved that even small, in comparison with the primary nominal sinusoidal voltage, aperiodic components of the voltage transient process can lead to significant overvoltages during voltage transformer outages. It has been substantiated that both the secondary resistance and the switching torque influence the overvoltage multiplicity arising in the primary winding of voltage transformers. It is shown that the closed secondary winding worsens the disconnection process of non-linear inductance of voltage transformers. The values to which overvoltages increase in this case are determined. According to the results of calculations it is determined that with open secondary winding of voltage transformers the duration of transient process significantly increases. It has been found that the decrease of frequency of forced oscillations, which occurs in this case, is accompanied by an increase of currents in the primary winding of the voltage transformer, which is dangerous in terms of thermal stability of the winding insulation. It is shown, that closing the secondary winding of voltage transformers leads to significant reduction of transient damping time. It is suggested that this algorithm can be used to provide a rapid breakdown (suppression) of ferroresonant processes. The effectiveness of such a measure of stopping of ferroresonance processes as short-term shunting of secondary winding of voltage transformers has been investigated. The correlation of parameters of electric networks (capacity of busbar sections, nonlinearity of characteristics of voltage transformers, disconnection torque, etc.) at which ferroresonance process may occur and consideration of which may allow, in terms of prevention of ferroresonance processes, to identify substations (electric networks) that require more detailed research has been determined. The results of analytical studies were tested in the electric networks of JSC "Kharkivoblenergo" and used in the electricity distribution system for the selection of specific voltage transformers for certain configurations of electrical networks.
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Azizian, Davood, and Mehdi Bigdeli. "A new cast-resin transformer thermal model based on recurrent neural networks." Archives of Electrical Engineering 66, no. 1 (March 1, 2017): 17–28. http://dx.doi.org/10.1515/aee-2017-0002.
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Abstract Thermal modeling in the transient condition is very important for cast-resin dry-type transformers. In the present research, two novel dynamic thermal models have been introduced for the cast-resin dry-type transformer. These models are based on two artificial neural networks: the Elman recurrent networks (ELRN) and the nonlinear autoregressive model process with exogenous input (NARX). Using the experimental data, the introduced neural network thermal models have been trained. By selecting a typical transformer, the trained thermal models are validated using additional experimental results and the traditional thermal models. It is shown that the introduced neural network based thermal models have a good performance in temperature prediction of the winding and the cooling air in the cast-resin dry-type transformer. The introduced thermal models are more accurate for the temperature analysis of this transformer and they will be trained easily. Finally, the trained and validated thermal models are employed to evaluate the life-time and the reliability of a typical cast-resin dry-type transformer.
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Zhao, Guanghui, Zelin Wang, Yixiong Huang, Huirong Zhang, and Xiaojing Ma. "Transformer-Based Maneuvering Target Tracking." Sensors 22, no. 21 (November 4, 2022): 8482. http://dx.doi.org/10.3390/s22218482.
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When tracking maneuvering targets, recurrent neural networks (RNNs), especially long short-term memory (LSTM) networks, are widely applied to sequentially capture the motion states of targets from observations. However, LSTMs can only extract features of trajectories stepwise; thus, their modeling of maneuvering motion lacks globality. Meanwhile, trajectory datasets are often generated within a large, but fixed distance range. Therefore, the uncertainty of the initial position of targets increases the complexity of network training, and the fixed distance range reduces the generalization of the network to trajectories outside the dataset. In this study, we propose a transformer-based network (TBN) that consists of an encoder part (transformer layers) and a decoder part (one-dimensional convolutional layers), to track maneuvering targets. Assisted by the attention mechanism of the transformer network, the TBN can capture the long short-term dependencies of target states from a global perspective. Moreover, we propose a center–max normalization to reduce the complexity of TBN training and improve its generalization. The experimental results show that our proposed methods outperform the LSTM-based tracking network.
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Cremasco, Andrea, Wei Wu, Andreas Blaszczyk, and Bogdan Cranganu-Cretu. "Network modelling of dry-type transformer cooling systems." COMPEL - The international journal for computation and mathematics in electrical and electronic engineering 37, no. 3 (May 8, 2018): 1039–53. http://dx.doi.org/10.1108/compel-12-2016-0534.
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Purpose The application of dry-type transformers is growing in the market because the technology is non-flammable, safer and environmentally friendly. However, the unit dimensions are normally larger and material costs become higher, as no oil is present for dielectric insulation or cooling. At designing stage, a transformer thermal model used for predicting temperature rise is fundamental and the modelling of cooling system is particularly important. This paper aims to describe a thermal model used to compute dry transformers with different cooling system configurations. Design/methodology/approach The paper introduces a fast-calculating thermal and pressure network model for dry-transformer cooling systems, preliminarily verified by analytical methods and advanced CFD simulations, and finally validated with experimental results. Findings This paper provides an overview of the network model of dry-transformer cooling system, describing its topology and its main variants including natural or forced ventilation, with or without cooling duct in the core, enclosure with roof and floor ventilation openings and air barriers. Finally, it presents a formulation for the new heat exchanger element. Originality/value The network approach presented in this paper allows to model efficiently the cooling system of dry-type transformers. This model is based on physical principles rather than empirical assessments that are valid only for specific transformer technologies. In comparison with CFD simulation approach, the network model runs much faster and the accuracies still fall in acceptable range; therefore, one is able to utilize this method in optimization procedures included in transformer design systems.
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Jiao, Jinyue, Zhiqiang Gong, and Ping Zhong. "Dual-Branch Fourier-Mixing Transformer Network for Hyperspectral Target Detection." Remote Sensing 15, no. 19 (September 24, 2023): 4675. http://dx.doi.org/10.3390/rs15194675.
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In recent years, transformers have shown great potential in hyperspectral image processing and have also been gradually applied in hyperspectral target detection (HTD). Nonetheless, applying a typical transformer to HTD remains challenging. The heavy computation burden of the multi-head self-attention (MSA) in transformers limits its efficient HTD, while the limited ability to extract local spectral features can reduce the discrimination of the learned spectral features. To further explore the potential of transformers for HTD, for balance of representation ability and computational efficiency, we propose a dual-branch Fourier-mixing transformer network for hyperspectral target detection (DBFTTD). First, this work explores a dual-branch Fourier-mixing transformer network. The transformer-style network replaces the MSA sublayer in the transformer with a Fourier-mixing sublayer, which shows advantages in improving computational efficiency and learning valuable spectral information effectively for HTD. Second, this work proposes learnable filter ensembles in the Fourier domain that are inspired by ensemble learning to improve detection performance. Third, a simple but efficient dropout strategy is proposed for data augmentation. Sufficient and balanced training samples are constructed for training the dual-branch network, and training samples for balanced learning can further improve detection performance. Experiments on four data sets indicate that our proposed detector is superior to the state-of-the-art detectors.
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Fiennes, J., and C. R. de Souza. "The Complex Transformer as a Network-Model Element." International Journal of Electrical Engineering & Education 40, no. 1 (January 2003): 27–35. http://dx.doi.org/10.7227/ijeee.40.1.3.
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Nafisi, Hamed, Mehrdad Abedi, and Gevorg B. Gharehpetian. "Locating Pd in Transformers through Detailed Model and Neural Networks." Journal of Electrical Engineering 65, no. 2 (March 1, 2014): 75–82. http://dx.doi.org/10.2478/jee-2014-0011.
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Abstract In a power transformer as one of the major component in electric power networks, partial discharge (PD) is a major source of insulation failure. Therefore the accurate and high speed techniques for locating of PD sources are required regarding to repair and maintenance. In this paper an attempt has been made to introduce the novel methods based on two different artificial neural networks (ANN) for identifying PD location in the power transformers. In present report Fuzzy ARTmap and Bayesian neural networks are employed for PD locating while using detailed model (DM) for a power transformer for simulation purposes. In present paper PD phenomenon is implemented in different points of transformer winding using threecapacitor model. Then impulse test is applied to transformer terminals in order to use produced current in neutral point for training and test of employed ANNs. In practice obtained current signals include noise components. Thus the performance of Fuzzy ARTmap and Bayesian networks for correct identification of PD location in a noisy condition for detected currents is also investigated. In this paper RBF learning procedure is used for Bayesian network, while Markov chain Monte Carlo (MCMC) method is employed for training of Fuzzy ARTmap network for locating PD in a power transformer winding and results are compared.
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Abdullah, A. M., R. Ali, S. B. Yaacob, K. Ananda-Rao, and N. A. Uloom. "Transformer Health Index by Prediction Artificial Neural Networks Diagnostic Techniques." Journal of Physics: Conference Series 2312, no. 1 (August 1, 2022): 012002. http://dx.doi.org/10.1088/1742-6596/2312/1/012002.
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Abstract This paper presents the artificial neural network diagnostic techniques for predicting the health index in transformer. Collection data is measured and tested from insulation resistance in between phase-ground, phase to phase and also the winding resistance transformer. The data was collected from 10 units of transformers from Company Transformer Manufacturing and Servicing (CTMS) in Malaysia. The data was used to calculate condition transformer index or health index transformer. Condition transformer index can identify whether transformer in good condition or not good condition. The purpose of knowing transformer health index or condition transformer index is to prevent failures functional transformer and ensure transformer in stable condition. Prediction health index or condition transformer index can be determined by artificial neural network. Therefore, it can monitor and observe very closely conditions of the transformer. Data health index transformer is very important because it know the condition transformer and can solve the major problem in transformer or do the maintenance in early stage before the transformer is totally malfunction.
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Braña, L., A. Costa, and R. Lopes. "Development of a power transformer model for high-frequency transient phenomena." Renewable Energy and Power Quality Journal 19 (September 2021): 217–21. http://dx.doi.org/10.24084/repqj19.260.
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In recent years, the proliferation of distributed renewable energy sources and the application of new rules for the exploitation of electrical networks imposed by the markets have dictated increasingly demanding operating conditions for electric power transformers, creating new challenges in their exploration and conservation. Transformers that, in addition to the transmission lines, are certainly the most important and critical element of any electrical energy system. Adequate models are necessary to accurately describe transformer behavior and internal response when submitted to different external requests imposed by the network, particularly during transient phenomena, as well as, to properly assess system vulnerabilities and network optimization. This effort is being carried out today by several research groups in the world, namely from Cigré and IEEE. In this work, a transformer model to be integrated into a timedomain equivalent circuit is developed and discussed. Results obtained with this model are compared with measurements obtained by the Cigré JWG A2/C4.52 in a power transformer used as a reference for the working group.
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Zheng, Jianhan, Shengqing Gui, and Haomin Zhang. "Transformer Vibration Analysis Based on Double Branch Convolutional Neural Network." Journal of Physics: Conference Series 2503, no. 1 (May 1, 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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Lin, Jun, Lei Su, Yingjie Yan, Gehao Sheng, Da Xie, and Xiuchen Jiang. "Prediction Method for Power Transformer Running State Based on LSTM_DBN Network." Energies 11, no. 7 (July 19, 2018): 1880. http://dx.doi.org/10.3390/en11071880.
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It is of great significance to accurately get the running state of power transformers and timely detect the existence of potential transformer faults. This paper presents a prediction method of transformer running state based on LSTM_DBN network. Firstly, based on the trend of gas concentration in transformer oil, a long short-term memory (LSTM) model is established to predict the future characteristic gas concentration. Then, the accuracy and influencing factors of the LSTM model are analyzed with examples. The deep belief network (DBN) model is used to establish the transformer operation using the information in the transformer fault case library. The accuracy of state classification is higher than the support vector machine (SVM) and back-propagation neural network (BPNN). Finally, combined with the actual transformer data collected from the State Grid Corporation of China, the LSTM_DBN model is used to predict the transformer state. The results show that the method has higher prediction accuracy and can analyze potential faults.
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Azmi Murad Abd Aziz, Mohd Aizam Talib, Ahmad Farid Abidin, and 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, no. 3 (May 15, 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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Lin, Yubin, Jiyu Li, Xiaofei Ruan, Xiaoyu Huang, and Jinbo Zhang. "Energy consumption analysis of power grid distribution transformers based on an improved genetic algorithm." PeerJ Computer Science 9 (October 26, 2023): e1632. http://dx.doi.org/10.7717/peerj-cs.1632.
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With the promotion of energy transformation, the utilization ratio of electrical power is progressively rising. Since electrical power is challenging to store, real-time production and consumption become imperative, imposing significant demands on the dependability and operational efficiency of electrical power apparatus. Suppose the load distribution among multiple transformers within a transformer network exhibits inequality. In such instances, it will amplify the total energy consumption during the voltage conversion process, and local, long-term high-load transformer networks become more susceptible to failures. In this article, we scrutinize the matter of transformer energy utilization in the context of electricity transmission within grid systems. We propose a methodology grounded on genetic algorithms to optimize transformer energy usage by dynamically redistributing loads among diverse transformers based on their operational status monitoring. In our experimentation, we employed three distinct approaches to enhance energy efficiency. The experimental findings evince that this approach facilitates swifter attainment of the optimal power level and diminishes the overall energy consumption during transformer operation. Moreover, it exhibits a heightened responsiveness to fluctuations in power demand from the electrical grid. Experimental results manifest that this technique can truncate monitoring time by 27% and curtail the overall energy consumption of the distribution transformer network by 11.81%. Lastly, we deliberate upon the potential applications of genetic algorithms in the realm of power equipment management and energy optimization issues.
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Lara, Hector, and Esteban Inga. "Efficient Strategies for Scalable Electrical Distribution Network Planning Considering Geopositioning." Electronics 11, no. 19 (September 28, 2022): 3096. http://dx.doi.org/10.3390/electronics11193096.
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This article presents a heuristic model to find the optimal route or layout of a subway electrical distribution network, obtaining full coverage of users in different scenarios and respecting technical criteria such as maximum distance to avoid voltage drop and capacity. In this way, the location of the transformer substations is achieved through an analysis of candidate sites. The medium voltage network will connect each transformer to a minimum spanning tree (MST), reducing the cost of materials associated with constructing the electrical grid. This work considers the latitude and longitude of each house and electrical count. Georeferenced scenario information is taken from the OpenStreetMap platform to provide an authentic context for distance and location calculations in the deployment of the power grid. The heuristic model offers to decrease time in solving the electrical network layout. As input variables, different powers of the "multi-transformer" transformers are considered to minimize the number of transformers and solve the power supply, reducing the transformers’ oversizing and minimizing the transformers’ idle capacity. The experimentation showed that none exceeded the limit allowed in an urban area of 3.5%.
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Bernadić, Alen, and Zahira Anane. "NEUTRAL POINT CONNECTIONS IN MV POWER NETWORKS WITH GROUNDING ZIGZAG TRANSFORMERS – ANALYSIS AND SIMULATIONS." Journal of Energy - Energija 68, no. 1 (April 30, 2019): 42–48. http://dx.doi.org/10.37798/20196812.
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Treatment of transformer neutral point in middle-voltage (MV) networks become an important issue with increasing proportion of MV cables in power networks. As consequence, overall capacitance of MV network is increased and moreover earth fault currents magnitudes. In MV networks with feeding transformer winding in delta connection (isolated networks), that earth fault current increase requires forming of artificial ground point – a neutral connection point on a three-phase ungrounded power system. Grounding transformer use, in zigzag or delty-wye connection, is common, well-known solution for constructing neutral connection in power systems. Physical characteristics of grounding transformers, protection principles, short-circuit calculations with symmetrical components and simulation techniques are presented in this paper. Characteristical operational modalities of MV power networks are also revieved on practical examples.
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Jaiswal, Sushma, Harikumar Pallthadka, Rajesh P. Chinchewadi, and Tarun Jaiswal. "Optimized Image Captioning: Hybrid Transformers Vision Transformers and Convolutional Neural Networks: Enhanced with Beam Search." International Journal of Intelligent Systems and Applications 16, no. 2 (April 8, 2024): 53–61. http://dx.doi.org/10.5815/ijisa.2024.02.05.
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Deep learning has improved image captioning. Transformer, a neural network architecture built for natural language processing, excels at image captioning and other computer vision applications. This paper reviews Transformer-based image captioning methods in detail. Convolutional neural networks (CNNs) extracted image features and RNNs or LSTM networks generated captions in traditional image captioning. This method often has information bottlenecks and trouble capturing long-range dependencies. Transformer architecture revolutionized natural language processing with its attention strategy and parallel processing. Researchers used Transformers' language success to solve image captioning problems. Transformer-based image captioning systems outperform previous methods in accuracy and efficiency by integrating visual and textual information into a single model. This paper discusses how the Transformer architecture's self-attention mechanisms and positional encodings are adapted for image captioning. Vision Transformers (ViTs) and CNN-Transformer hybrid models are discussed. We also discuss pre-training, fine-tuning, and reinforcement learning to improve caption quality. Transformer-based image captioning difficulties, trends, and future approaches are also examined. Multimodal fusion, visual-text alignment, and caption interpretability are challenges. We expect research to address these issues and apply Transformer-based image captioning to medical imaging and distant sensing. This paper covers how Transformer-based approaches have changed image captioning and their potential to revolutionize multimodal interpretation and generation, advancing artificial intelligence and human-computer interactions.
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Xu, Honghua, Yong Li, Lei Zhu, and Ziqiang Xu. "Condition assessment of transformers in wind farm based on modified one-dim residual neural network." Journal of Physics: Conference Series 2378, no. 1 (December 1, 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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Zhou, Li, Tongqin Shi, Songquan Huang, Fangchao Ke, Zhenxi Huang, Zhaoyang Zhang, and Jinzheng Liang. "Convolutional neural network for real-time main transformer detection." Journal of Physics: Conference Series 2229, no. 1 (March 1, 2022): 012021. http://dx.doi.org/10.1088/1742-6596/2229/1/012021.
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Abstract For substation constructions, the main transformer is the dominant electrical equipment, and its arrival and operation affect the progress of project directly. In the context of smart grid construction, in order to improve the efficiency of real-time main transformer detection, this paper proposes an identification and detection method based on the SSD algorithm. The SSD algorithm is able to extract the target device (such as main transformer) accurately and the Lenet algorithm module can analyse the features contained in the image. To improve the accuracy of the detection method, the image migration algorithm of VGG-Net is used to expand the negative samples of main transformers to improve the generalisation of the algorithm. Finally, the image set collected in the real substation projects is used for validation, and result shows that the method identifies main transformers more accurately, with high effectiveness and feasibility.
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Hartono, Muharni, Adipura, Martiningsih, Otong, and Muhammad Irvan. "ANALYSIS OF POWER TRANSFORMATOR CONDITIONS USING DGA METHOD USING ARTIFICIAL NEURAL NETWORK IN KRAKATAU ELECTRICAL POWER COMPANY." International Journal of Engineering Technologies and Management Research 7, no. 6 (June 16, 2020): 77–88. http://dx.doi.org/10.29121/ijetmr.v7.i6.2020.572.
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Test method that can be done for transformer oil with DGA method. In identifying early transformer conditions, one of them is using IEC 60599 Standards. The artificial neural network training process used 341 data in the presence of nine conditions based on the IEC standard. The best network architecture configuration is a configuration with 3 neurons in the input layer, 10 neurons in the first hidden layer, 20 neurons in the second hidden layer, 20 neurons in the third hidden layer and 4 neurons in the output layer with the transfer logic. The results of the training give a regression value of 0.95216 and MSE (Mean Square Error) is worth 0.000216. Testing of artificial neural networks is done 19 first test data is performed to determine the number of transformer conditions that can be diagnosed by each method. From the test data obtained the accuracy value for artificial neural network models is 94.7%.
The following will guide the structure of your abstract:
Motivation/Background: Using the neural network method in this study is expected to improve accuracy and improve the transformer analysis process. Transformer to make one effective and fast way for transformers.
Method: The IEC method is an effective method for implementing transformers. The way this method works is by comparing the concentration of solute, then the results are represented into nine kinds of conditions. However, this method has a weakness that is the length of time in the analysis process. Therefore, to overcome these deficiencies, this study uses the Artificial Neural Network (ANN) method with a comparison of the use of gas as its input and the condition transformer as its target.
Results: The results of the training give a regression value of 0.95216 and MSE (Mean Square Error) is worth 0.000216.
Conclusions: This study uses 460 data from existing data into 2 namely data for training that brings 341 data and data for testing to get 19 data. In this study using a neural network resolves the problem in this study. in this study obtained an accuracy of 94.4%, so this artificial neural network method has good potential to assist in this study.
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Wang, Jianbin, Lei Shen, and Weiming Fan. "A TSENet Model for Predicting Cellular Network Traffic." Sensors 24, no. 6 (March 7, 2024): 1713. http://dx.doi.org/10.3390/s24061713.
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Wireless sensor networks (WSNs) are gaining traction in the realm of network communication, renowned for their adaptability, configuration, and flexibility. The forthcoming network traffic within WSNs can be forecasted through temporal sequence models. In this correspondence, we present a method (TSENet) that can accurately predict the traffic in the cellular network. TSENet is composed of transformers and self-attention network. We have designed a temporal transformer module specifically for extracting temporal features. This module accomplishes this by modeling the traffic flow within each grid of the communication network at both near-term and periodical intervals. Simultaneously, we amalgamate the spatial features of each grid with information from its correlated grids, generating spatial predictions within the spatial transformer. Furthermore, we employ self-attention aggregation to capture dependencies between external factor features and cellular data features. Empirical assessments performed on a genuine cellular traffic dataset offer compelling evidence substantiating the efficacy of TSENet.
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Alibašić, Emir, Predrag Marić, and Srete N. Nikolovski. "Transient Phenomena during the Three-Phase 300MVA Transformer Energization on the Transmission Network." International Journal of Electrical and Computer Engineering (IJECE) 6, no. 6 (December 1, 2016): 2499. http://dx.doi.org/10.11591/ijece.v6i6.11406.
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<p>Connecting the transformer to the network may incur inrush current, which is significantly higher than the rated current of the transformer. The main cause of this phenomenon lies in the nonlinearity of the magnetic circuit. The value of the inrush current depends of the time moment of the energization and the residual magnetism in the transformer core. While connecting, the operating point of the magnetization characteristic can be found deep in the saturation region resulting in occurrence of large transformer currents that can trigger the transformer protection. Tripping of protection immediately after the transformer energization raises doubts about the transformer health. Inrush current can cause a number of other disadvantages such as the negative impact on other transformers connected on the same busbar; the increase of the transformer noise due to the large current value, the increase of the voltage drops in the network. The paper presents a simulation of the 300 MVA transformer energization using the MATLAB/Simulink software.</p><p> </p><p> </p>
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Alibašić, Emir, Predrag Marić, and Srete N. Nikolovski. "Transient Phenomena during the Three-Phase 300MVA Transformer Energization on the Transmission Network." International Journal of Electrical and Computer Engineering (IJECE) 6, no. 6 (December 1, 2016): 2499. http://dx.doi.org/10.11591/ijece.v6i6.pp2499-2505.
Full textAbstract:
<p>Connecting the transformer to the network may incur inrush current, which is significantly higher than the rated current of the transformer. The main cause of this phenomenon lies in the nonlinearity of the magnetic circuit. The value of the inrush current depends of the time moment of the energization and the residual magnetism in the transformer core. While connecting, the operating point of the magnetization characteristic can be found deep in the saturation region resulting in occurrence of large transformer currents that can trigger the transformer protection. Tripping of protection immediately after the transformer energization raises doubts about the transformer health. Inrush current can cause a number of other disadvantages such as the negative impact on other transformers connected on the same busbar; the increase of the transformer noise due to the large current value, the increase of the voltage drops in the network. The paper presents a simulation of the 300 MVA transformer energization using the MATLAB/Simulink software.</p><p> </p><p> </p>
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Astashev, Mikhail G., Artem S. Vanin, Vladimir M. Korolev, Dmitriy I. Panfilov, Pavel A. Rashitov, and Vladimir N. Tulskii. "Assessment of the Technical and Economic Effect from Using Automatic Voltage Control Devices on 10/0.4 kV Transformers in Power Distribution Networks." Vestnik MEI, no. 5 (2021): 27–36. http://dx.doi.org/10.24160/1993-6982-2021-5-27-36.
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The article addresses the problem of ensuring permissible voltage levels in distribution electrical networks of various types: distribution networks of large cities, regional distribution electrical networks, and distribution electrical networks containing renewable energy sources. The most typical factors causing the voltage to go beyond the permissible limits specified by the relevant regulatory documents are pointed out. The negative factors conducive to the voltage at the consumer end deviating from the permissible limits, including a long length of network lines, high network load, low controllability of the network, load schedule nonuniformity, and poor observability of the network, are analyzed. The existing principles of voltage control in electrical distribution networks, namely, automatic and seasonal regulation, are studied. A distribution electrical network test model representing a real network fragment is developed. The model operation modes have been verified based on the data of measurements carried out in the original distribution electrical network. The voltage distributions in a medium voltage network during its operation under the conditions of the highest and lowest loads are demonstrated. It is shown, on the test model example, how the network voltage can be controlled by automatically regulating the voltage at the power supply center and selecting a fixed position of the NLTC at 10/0.4 kV transformer substations. It is shown that the use of power transformer OLTCs does not ensure sufficient means for adequately controlling the voltage in networks containing long power lines and featuring highly nonuniform seasonal and daily load schedules. The technical efficiency and economic feasibility of using automatic voltage regulation devices on 10/0.4 kV transformers for local voltage control are analyzed. The economic efficiency of applying automatic voltage regulation devices at 6--10/0.4 kV substations was evaluated in comparison with other means for improving the power distribution network voltage quality by upgrading the 10 kV feeder lines or installing a voltage booster at the inlet to the problematic 10 kV network section. The application field of automatic voltage regulators in the form of semiconductor devices for regulating the transformer output voltage at distribution transformer substations is shown.
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Xu, Xiangkai, Zhejun Feng, Changqing Cao, Mengyuan Li, Jin Wu, Zengyan Wu, Yajie Shang, and Shubing Ye. "An Improved Swin Transformer-Based Model for Remote Sensing Object Detection and Instance Segmentation." Remote Sensing 13, no. 23 (November 25, 2021): 4779. http://dx.doi.org/10.3390/rs13234779.
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Remote sensing image object detection and instance segmentation are widely valued research fields. A convolutional neural network (CNN) has shown defects in the object detection of remote sensing images. In recent years, the number of studies on transformer-based models increased, and these studies achieved good results. However, transformers still suffer from poor small object detection and unsatisfactory edge detail segmentation. In order to solve these problems, we improved the Swin transformer based on the advantages of transformers and CNNs, and designed a local perception Swin transformer (LPSW) backbone to enhance the local perception of the network and to improve the detection accuracy of small-scale objects. We also designed a spatial attention interleaved execution cascade (SAIEC) network framework, which helped to strengthen the segmentation accuracy of the network. Due to the lack of remote sensing mask datasets, the MRS-1800 remote sensing mask dataset was created. Finally, we combined the proposed backbone with the new network framework and conducted experiments on this MRS-1800 dataset. Compared with the Swin transformer, the proposed model improved the mask AP by 1.7%, mask APS by 3.6%, AP by 1.1% and APS by 4.6%, demonstrating its effectiveness and feasibility.
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Goran Jerbić. "APPLICATION OF PHASE SHIFTING TRANSFORMERS IN THE CROATIAN POWER SUPPLY SYSTEM." Journal of Energy - Energija 56, no. 2 (November 16, 2022): 216–31. http://dx.doi.org/10.37798/2007562353.
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The phase shifting transformers with on load tap changer are increasingly found in modern power supply systems, especially under the present conditions of the full opening of the electricity market. The construction of the Žerjavinec TS 400/220/110 kV for the first time introduces into the Croatian transmission system a 400/220/(10,5) kV 400 MVA phase shifting network transformer with on load tap changer. The present article highlights some specific aspects of phase shifting transformers in the light of their application in the Croatian system. For a more efficient use of the advantages of phase shifting transformers, the transformer of this type at Žerjavinec would need to be provided with a complementary transformer (pair), either within the Croatian transmission network or within the neighbouring systems.
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Li, Qingbiao, Chunhua Wu, Zhe Wang, and Kangfeng Zheng. "Hierarchical Transformer Network for Utterance-Level Emotion Recognition." Applied Sciences 10, no. 13 (June 28, 2020): 4447. http://dx.doi.org/10.3390/app10134447.
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While there have been significant advances in detecting emotions in text, in the field of utterance-level emotion recognition (ULER), there are still many problems to be solved. In this paper, we address some challenges in ULER in dialog systems. (1) The same utterance can deliver different emotions when it is in different contexts. (2) Long-range contextual information is hard to effectively capture. (3) Unlike the traditional text classification problem, for most datasets of this task, they contain inadequate conversations or speech. (4) To better model the emotional interaction between speakers, speaker information is necessary. To address the problems of (1) and (2), we propose a hierarchical transformer framework (apart from the description of other studies, the “transformer” in this paper usually refers to the encoder part of the transformer) with a lower-level transformer to model the word-level input and an upper-level transformer to capture the context of utterance-level embeddings. For problem (3), we use bidirectional encoder representations from transformers (BERT), a pretrained language model, as the lower-level transformer, which is equivalent to introducing external data into the model and solves the problem of data shortage to some extent. For problem (4), we add speaker embeddings to the model for the first time, which enables our model to capture the interaction between speakers. Experiments on three dialog emotion datasets, Friends, EmotionPush, and EmoryNLP, demonstrate that our proposed hierarchical transformer network models obtain competitive results compared with the state-of-the-art methods in terms of the macro-averaged F1-score (macro-F1).
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Jianwen, Mo, Mo Lunlin, Yuan Hua, Lin Leping, and Chen Lingping. "CNN with Embedding Transformers for Person Reidentification." Mathematical Problems in Engineering 2023 (July 14, 2023): 1–12. http://dx.doi.org/10.1155/2023/4591991.
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For person reidentification (ReID), most slicing methods (such as part-based convolutional baseline (PCB) and AlignedReID) introduce a lot of background devoid of pedestrian parts, resulting in the cross-aliasing of features in the deep network. Besides, the resulting component features are not perfectly aligned with each other, thus affecting model performance. We propose a convolutional neural network (CNN) with embedding transformers (CET) person ReID network architecture based on the respective advantages of CNN and transformer. In CET, first, the residual transformer (RT) structure is first embedded in the backbone network of CNN to obtain a feature extractor, named transformers in CNN. The feature aliasing phenomenon is improved by utilizing transformer’s advantage in grasping the relevance of global information. Second, a feature fuse with learnable vector structure for fusing the output vector is added to the output of the transformer at the end of the network. A two branches loss structure is designed to balance the two different fusion strategies. Finally, the self-attention mechanism in transformer is used for automatic part alignment of human body parts to solve the part alignment problem caused by inaccurate detection frames. The experimental results show that CET network architecture achieves better performance than PCB and some other block-slicing methods.
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Wang, Cheng, Zhixin Fu, Zheng Zhang, Weiping Wang, Huatai Chen, and Da Xu. "Fault Diagnosis of Power Transformer in One-Key Sequential Control System of Intelligent Substation Based on a Transformer Neural Network Model." Processes 12, no. 4 (April 19, 2024): 824. http://dx.doi.org/10.3390/pr12040824.
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With the introduction of numerous technologies and equipment, the volume of data in smart substations has undergone exponential growth. In order to enhance the intelligent management level of substations and promote their efficient and sustainable development, the one-key sequential control system of smart substations is being renovated. In this study, firstly, the intelligent substation is defined and compared with the traditional substation. The one-key sequential control system is introduced, and the main issues existing in the system are analyzed. Secondly, experiments are conducted on the winding temperature, insulation oil temperature, and ambient temperature of power transformers in the primary equipment. Combining data fusion technology and transformer neural network models, a Power Transformer-Transformer Neural Network (PT-TNNet) model based on data fusion is proposed. Subsequently, comparative experiments are conducted with multiple algorithms to validate the high accuracy, precision, recall, and F1 score of the PT-TNNet model for equipment state monitoring and fault diagnosis. Finally, using the efficient PT-TNNet, Random Forest, and Extra Trees models, the cross-validation of the accuracy of winding temperature and insulation oil temperature of transformers is performed, confirming the superiority of the PT-TNNet model based on transformer neural networks for power transformer state monitoring and fault diagnosis, its feasibility for application in one-key sequential control systems, and the optimization of one-key sequential control system performance.
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Ravikumar, V., S. Ranjith, and T. Bhavyasree. "Automatic Load Sharing of Distribution Transformers to Reduce Over all Losses in Distribution network." E3S Web of Conferences 309 (2021): 01126. http://dx.doi.org/10.1051/e3sconf/202130901126.
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An increase in the development of Industries and rapid growth in the population has led to an increase in the power demand in the distribution network. With these increased needs, the existing distribution transformer have become overloaded conditions. Due to overload on the transformer, the efficiency and power factor drops and also it led to increase in the transformer voltage regulation and windings get overheated. This paper presents a novel topology called transformer auto stop-start that will automatically energise and de-energise, one pair of transformers at a kV/V Distribution network. In this way, the proposed technique reduces overall electric losses. Performance of transformer under different load conditions are illustrated by simulation.
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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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Lei, Tianliang, Lixin Ji, Gengrun Wang, Shuxin Liu, Lan Wu, and Fei Pan. "Transformer-Based User Alignment Model across Social Networks." Electronics 12, no. 7 (April 3, 2023): 1686. http://dx.doi.org/10.3390/electronics12071686.
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Cross-social network user identification refers to finding users with the same identity in multiple social networks, which is widely used in the cross-network recommendation, link prediction, personality recommendation, and data mining. At present, the traditional method is to obtain network structure information from neighboring nodes through graph convolution, and embed social networks into the low-dimensional vector space. However, as the network depth increases, the effect of the model will decrease. Therefore, in order to better obtain the network embedding representation, a Transformer-based user alignment model (TUAM) across social networks is proposed. This model converts the node information and network structure information from the graph data form into sequence data through a specific encoding method. Then, it inputs the data to the proposed model to learn the low-dimensional vector representation of the user. Finally, it maps the two social networks to the same feature space for alignment. Experiments on real datasets show that compared with GAT, TUAM improved ACC@10 indicators by 11.61% and 16.53% on Facebook–Twitter and Weibo–Douban datasets, respectively. This illustrates that the proposed model has a better performance compared to other user alignment models.
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Duan, Shiyao, Jiaojiao Li, Rui Song, Yunsong Li, and Qian Du. "Unmixing-Guided Convolutional Transformer for Spectral Reconstruction." Remote Sensing 15, no. 10 (May 18, 2023): 2619. http://dx.doi.org/10.3390/rs15102619.
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Deep learning networks based on CNNs or transformers have made progress in spectral reconstruction (SR). However, many methods focus solely on feature extraction, overlooking the interpretability of network design. Additionally, models exclusively based on CNNs or transformers may lose other prior information, sacrificing reconstruction accuracy and robustness. In this paper, we propose a novel Unmixing-Guided Convolutional Transformer Network (UGCT) for interpretable SR. Specifically, transformer and ResBlock components are embedded in Paralleled-Residual Multi-Head Self-Attention (PMSA) to facilitate fine feature extraction guided by the excellent priors of local and non-local information from CNNs and transformers. Furthermore, the Spectral–Spatial Aggregation Module (S2AM) combines the advantages of geometric invariance and global receptive fields to enhance the reconstruction performance. Finally, we exploit a hyperspectral unmixing (HU) mechanism-driven framework at the end of the model, incorporating detailed features from the spectral library using LMM and employing precise endmember features to achieve a more refined interpretation of mixed pixels in HSI at sub-pixel scales. Experimental results demonstrate the superiority of our proposed UGCT, especially in the grss_d f c_2018 dataset, in which UGCT attains an RMSE of 0.0866, outperforming other comparative methods.
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Dong, P., and L. Xing. "Transformer Network Assisted VMAT Optimization." International Journal of Radiation Oncology*Biology*Physics 114, no. 3 (November 2022): e577. http://dx.doi.org/10.1016/j.ijrobp.2022.07.2242.
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Odinaev, Ismoil, Andrey Pazderin, Murodbek Safaraliev, Firuz Kamalov, Mihail Senyuk, and Pavel Y. Gubin. "Detection of Current Transformer Saturation Based on Machine Learning." Mathematics 12, no. 3 (January 25, 2024): 389. http://dx.doi.org/10.3390/math12030389.
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One of the tasks in the operation of electric power systems is the correct functioning of the protection system and emergency automation algorithms. Instrument voltage and current transformers, operating in accordance with the laws of electromagnetism, are most often used for information support of the protection system and emergency automation algorithms. Magnetic core saturation of the specified current transformers can occur during faults. As a result, the correct functioning of the protection system and emergency automation algorithms is compromised. The consequences of current transformers saturation are mostly reflected in the main protections of network elements operating on a differential principle. This work aims to consider the analysis of current transformer saturation detection methods. The problem of identifying current transformer saturation is reduced to binary classification, and methods for solving the problem based on artificial neural networks, support vector machine, and decision tree algorithms are proposed. Computational experiments were performed, and their results were analyzed with imbalanced (dominance of the number of current transformer saturation modes over the number of modes with its normal operation) and balanced classes 0 (no current transformer saturation) and 1 (current transformer saturation).
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Kim, June-Woo, and Ho-Young Jung. "Voice-to-voice conversion using transformer network*." Phonetics and Speech Sciences 12, no. 3 (September 2020): 55–63. http://dx.doi.org/10.13064/ksss.2020.12.3.055.
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