Relevant bibliographies by topics / Transformer-based algorithm

Academic literature on the topic 'Transformer-based algorithm'

Author: Grafiati
Published: 10 December 2022
Last updated: 29 January 2023

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Journal articles on the topic "Transformer-based algorithm"

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Tripathy, Manoj. "Power Transformer Differential Protection Based on Neural Network Principal Component Analysis, Harmonic Restraint and Park's Plots." Advances in Artificial Intelligence 2012 (August 28, 2012): 1–9. http://dx.doi.org/10.1155/2012/930740.

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This paper describes a new approach for power transformer differential protection which is based on the wave-shape recognition technique. An algorithm based on neural network principal component analysis (NNPCA) with back-propagation learning is proposed for digital differential protection of power transformer. The principal component analysis is used to preprocess the data from power system in order to eliminate redundant information and enhance hidden pattern of differential current to discriminate between internal faults from inrush and overexcitation conditions. This algorithm has been developed by considering optimal number of neurons in hidden layer and optimal number of neurons at output layer. The proposed algorithm makes use of ratio of voltage to frequency and amplitude of differential current for transformer operating condition detection. This paper presents a comparative study of power transformer differential protection algorithms based on harmonic restraint method, NNPCA, feed forward back propagation neural network (FFBPNN), space vector analysis of the differential signal, and their time characteristic shapes in Park’s plane. The algorithms are compared as to their speed of response, computational burden, and the capability to distinguish between a magnetizing inrush and power transformer internal fault. The mathematical basis for each algorithm is briefly described. All the algorithms are evaluated using simulation performed with PSCAD/EMTDC and MATLAB.
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Li, Zhenhua, Xingxin Chen, Lin Wu, Abu-Siada Ahmed, Tao Wang, Yujie Zhang, Hongbin Li, Zhenxing Li, Yanchun Xu, and Yue Tong. "Error Analysis of Air-Core Coil Current Transformer Based on Stacking Model Fusion." Energies 14, no. 7 (March 30, 2021): 1912. http://dx.doi.org/10.3390/en14071912.

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Air-core coil current transformer is a key piece of equipment in the digital substation development. However, it is more vulnerable to various faults when compared with the traditional electromagnetic current transformer. Aiming at understanding the effect of various parameters on the performance of the air-core coil current transformer, this paper investigates the influence of these factors using the maximum information coefficient. The interference mechanism of influencing factors on the transformer error is also analyzed. Finally, the Stacking model fusion algorithm is used to predict transformer errors. The developed base model consists of deep learning, integrated learning and traditional learning algorithms. Compared with gated recurrent units and extreme gradient boosting algorithms, the prediction model based on stacking model fusion algorithm proposed in this paper features higher accuracy and reliability which helps improve the performance and safety of future digital substations.
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Sun, Zaiming, Chang’an Liu, Hongquan Qu, and Guangda Xie. "PVformer: Pedestrian and Vehicle Detection Algorithm Based on Swin Transformer in Rainy Scenes." Sensors 22, no. 15 (July 28, 2022): 5667. http://dx.doi.org/10.3390/s22155667.

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Pedestrian and vehicle detection plays a key role in the safe driving of autonomous vehicles. Although transformer-based object detection algorithms have made great progress, the accuracy of detection in rainy scenarios is still challenging. Based on the Swin Transformer, this paper proposes an end-to-end pedestrian and vehicle detection algorithm (PVformer) with deraining module, which improves the image quality and detection accuracy in rainy scenes. Based on Transformer blocks, a four-branch feature mapping model was introduced to achieve deraining from a single image, thereby mitigating the influence of rain streak occlusion on the detector performance. According to the trouble of small object detection only by visual transformer, we designed a local enhancement perception block based on CNN and Transformer. In addition, the deraining module and the detection module were combined to train the PVformer model through transfer learning. The experimental results show that the algorithm performed well on rainy days and significantly improved the accuracy of pedestrian and vehicle detection.
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Wang, Fu Zhong, Shu Min Shao, and Peng Fei Dong. "Research on Transformer Fault Diagnosis Method Based on Artificial Immune Network and Fuzzy C-Means Clustering Algorithm." Applied Mechanics and Materials 574 (July 2014): 468–73. http://dx.doi.org/10.4028/www.scientific.net/amm.574.468.

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The transformer is one of the indispensable equipment in transformer substation, it is of great significance for fault diagnosis. In order to accurately judge the transformer fault types, an algorithm is proposed based on artificial immune network combined with fuzzy c-means clustering to study on transformer fault samples. Focus on the introduction of data processing of transformer faults based on artificial immune network, the identification of transformer faults based on fuzzy c-means clustering, and the simulation process. The experimental results show that the proposed algorithm can classify power transformer fault types effectively, and the algorithm has a good application prospect in the transformer fault diagnosis.
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Yan, Tai Shan, Guan Qi Guo, Wu Li, and Wei He. "An Improved Neural Network Algorithm and its Application in Fault Diagnosis." Advanced Materials Research 765-767 (September 2013): 2355–58. http://dx.doi.org/10.4028/www.scientific.net/amr.765-767.2355.

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Aiming at BP neural network algorithms limitation such as falling into local minimum easily and low convergence speed, an improved BP algorithm with two times adaptive adjust of training parameters (TA-BP algorithm) was proposed. Besides the adaptive adjust of training rate and momentum factor, this algorithm can gain appropriate permitted convergence error by adaptive adjust in the course of training. TA-BP algorithm was applied in fault diagnosis of power transformer. A fault diagnosis model for power transformer was founded based on neural network. The illustrational results show that this algorithm is better than traditional BP algorithm in both convergence speed and precision. We can realize a fast and accurate diagnosis for power transformer fault by this algorithm.
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Islam, M. Fakhrul, Joarder Kamruzzaman, and Guojun Lu. "Improved ANN Based Tap-Changer Controller Using Modified Cascade-Correlation Algorithm." Journal of Advanced Computational Intelligence and Intelligent Informatics 9, no. 3 (May 20, 2005): 226–34. http://dx.doi.org/10.20965/jaciii.2005.p0226.

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Artificial Neural Network (ANN) based tap changer control of closed primary bus and cross network connected parallel transformers has demonstrated potential use in power distribution system. In those research works the proposed ANN for application in this control were developed using various algorithms and concluded that a network trained by Bayesian Regularization (BR) backpropagation algorithm produced the best performance measured in terms of correct tap changing decisions. However, further improvement of ANN based transformer tap changer operation is always desirable. A general rule for obtaining good generalization is to use the smallest network that solves the problem. In this paper, we show that a small sized ANN is obtainable for further improvement of transformer tap changer operation by modifying the standard Cascade-Correlation algorithm. The modification incorporates weight smoothing of output layer weights in Cascade-Correlation learning using Bayesian frame work. Experimental results demonstrate that significant improvement in performance is achieved when an ANN is trained by modified Cascade-Correlation algorithm instead of standard Cascade-Correlation or Bayesian Regularization backpropagation algorithm. A comparison of performances of different algorithms in application to transformer tap changer operation is analyzed and the results are presented.
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Mudholkar, R. R., S. R. Sawant, G. G. Tengshe, and A. B. Bagwan. "Fuzzy Logic Transformer Design Algorithm (FLTDA)." Active and Passive Electronic Components 22, no. 1 (1999): 17–29. http://dx.doi.org/10.1155/1999/53850.

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In the present communication we report a novel fuzzy based algorithm developed for transformer designing. The Fuzzy Logic Transformer Design Algorithm (FLTDA) incorporates the experience of human transformer designers and builders in terms of minimum of fuzzy rules. It easily accomodates the linguistic design concepts and linguistic values of transformer specifications. The FLTDA allows to use assumptions, approximations, estimations and guess-figures for specifications in the beginning of design-route and adjusts the parameters in iterations yielding optimum design results.As a first attempt towards the development of FLTDA only preliminary results have been worked out in trial designs. Comparison between conventional design method and fuzzy based method is made by working out the typical design problems.
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Meng, Junhong, Maninder Singh, Manish Sharma, Daljeet Singh, Preet Kaur, and Rajeev Kumar. "Online Monitoring Technology of Power Transformer based on Vibration Analysis." Journal of Intelligent Systems 30, no. 1 (January 1, 2021): 554–63. http://dx.doi.org/10.1515/jisys-2020-0112.

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Abstract This paper presents a method for the study of the influence of stability of a power transformer on the power system based on the vibration principle. Traditionally, the EMD and EEMD algorithms are employed to test the box vibration signal data of the power transformer under three working conditions. The proposed method utilizes a partial EMD screening along with MPEEMD method for the online monitoring of power transformer. A complete online monitoring system is designed by using the STM32 processor and LabVIEW system. The proposed system is compared with EMD and EEMD algorithms in terms of the number of IMFs obtained by decomposition, maximum correlation coefficient, and mean square error. The inherent mode correlation, when compared with the mean square error of the reconstructed signal, shows that the reconstruction error of MPEEMD algorithm is 4.762×10−15 which is better than the traditional EMD algorithm. It is observed from the results that the proposed method outperforms both EMD and EEMD algorithms.
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Dai, Jianzhuo, Sicong Zhang, Jiagui Tao, and Kaifeng Hu. "Transformer Connection Terminal Registration Based on Objective Function." Journal of Physics: Conference Series 2404, no. 1 (December 1, 2022): 012039. http://dx.doi.org/10.1088/1742-6596/2404/1/012039.

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Abstract In recent years, the traditional ICP algorithm has high requirements for the initial position of point clouds, and low registration ability for point clouds with low overlap. The Microsoft Kinect depth sensor was used to obtain the point cloud data of the target object from the real scene. Then, pre-processing such as point cloud segmentation, filtering and down sampling. In the coarse registration, the feature point sampling consistency algorithm was used to make the point cloud obtain a better initial position. Finally, a point-to-surface ICP algorithm optimized by linear least squares was proposed in the fine registration. The experimental results show that the root mean square error of the improved algorithm is 0.761mm and the time is 52.32ms. Compared with the ICP algorithm based on SIFT feature points and the improved ICP algorithm based on feature point sampling consistency, the registration accuracy of the improved algorithm is increased by 21.0% and 43.3%, and the speed is increased by 18.9% and 30.2%.
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Orosz, Tamás, David Pánek, and Pavel Karban. "FEM Based Preliminary Design Optimization in Case of Large Power Transformers." Applied Sciences 10, no. 4 (February 17, 2020): 1361. http://dx.doi.org/10.3390/app10041361.

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Since large power transformers are custom-made, and their design process is a labor-intensive task, their design process is split into different parts. In tendering, the price calculation is based on the preliminary design of the transformer. Due to the complexity of this task, it belongs to the most general branch of discrete, non-linear mathematical optimization problems. Most of the published algorithms are using a copper filling factor based winding model to calculate the main dimensions of the transformer during this first, preliminary design step. Therefore, these cost optimization methods are not considering the detailed winding layout and the conductor dimensions. However, the knowledge of the exact conductor dimensions is essential to calculate the thermal behaviour of the windings and make a more accurate stray loss calculation. The paper presents a novel, evolutionary algorithm-based transformer optimization method which can determine the optimal conductor shape for the windings during this examined preliminary design stage. The accuracy of the presented FEM method was tested on an existing transformer design. Then the results of the proposed optimization method have been compared with a validated transformer design optimization algorithm.
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Dissertations / Theses on the topic "Transformer-based algorithm"

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Lin, Zhe-Liang, and 林哲良. "Degradation Evaluation of Insulating Oil for Oil-filled Power Transformer Based on Intelligent Algorithms." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/6y7pk7.

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碩士
國立勤益科技大學
電機工程系
105
Large-scale power transformer is one of the most important electrical equipment in the power system. Operating condition affects the power system’s safety and stability directly. Once it is out of function, it will have a big impact and property loss for the whole system and production line. Furthermore, the power system’s safety and stability play significant role via the transformer failure mode research. To stop the power failure test is no need for dissolved gas analysis in order to facilitate online monitoring. Therefore, it is officially recognized as the oil-filled power transformer that is one of the most effective methods in the early potential failure stage. The study aims to China Steel and Dragon Steel Corporations oil-filled power transformer to evaluate the deterioration performance of the insulating oil. Finally, by utilizing the neural network and the extension method is to create the diagnosis system. The recognition precision could achieve accurate evaluation result exclude the noise interference according the sample testing result from the neural network diagnosis in comparison to the actual failure type. During the extension diagnosis and go through the extension factor way to figure out the practice of environment, the tolerance of accuracy and temperature variation to find out the evaluation results.
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Book chapters on the topic "Transformer-based algorithm"

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Hou, Fujin, Kun Zhou, Longbin Li, Yuan Tian, Jie Li, and Jian Li. "A Vulnerability Detection Algorithm Based on Transformer Model." In Lecture Notes in Computer Science, 43–55. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-06791-4_4.

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Xuhong, Huang, and Zhao Nan. "Transformer Fault Identification Method Based on Improved Roberts Algorithm." In Advances in Intelligent Information Hiding and Multimedia Signal Processing, 215–23. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-1057-9_21.

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Galdi, V., L. Ippolito, A. Piccolo, and A. Vaccaro. "Genetic Algorithm Based Parameters Identification for Power Transformer Thermal Overload Protection." In Artificial Neural Nets and Genetic Algorithms, 308–11. Vienna: Springer Vienna, 2001. http://dx.doi.org/10.1007/978-3-7091-6230-9_76.

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Vázquez Martínez, Ernesto, Héctor Esponda Hernández, and Manuel A. Andrade Soto. "A New Transformer Differential Protection Algorithm Based on Data Pattern Recognition." In Big Data Analytics in Future Power Systems, 143–68. Boca Raton : Taylor & Francis, a CRC title, part of the Taylor &: CRC Press, 2018. http://dx.doi.org/10.1201/9781315105499-8.

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Tang, Xinglu, Hui Xi, Qianqian Chen, and Tian Ran Lin. "Rolling Bearing Remaining Useful Life Prediction Based on LSTM-Transformer Algorithm." In Proceedings of IncoME-VI and TEPEN 2021, 207–15. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-99075-6_18.

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Yu, Huadong, Qing Wu, Yongling Lu, Chengbo Hu, Yubo Wang, and Guohua Liu. "Research on Fault Diagnosis of Power Transformer Equipment Based on KNN Algorithm." In Advances in Intelligent Systems and Computing, 172–76. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-70990-1_25.

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Cui, Lujun, Hui-chao Shang, Gang Zhang, You-ping Chen, Yong Li, and Ze-xiang Zhao. "The Design of Electronic Transformer Sophisticated Calibration System Based on FFT Algorithm." In Advances in Intelligent and Soft Computing, 115–20. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-30223-7_19.

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Zhao, Weiguo, Yanning Kang, Gangzhu Pan, and Xinfeng Huang. "Fault Diagnosis of Power Transformer Based on BP Combined with Genetic Algorithm." In Communications in Computer and Information Science, 33–38. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-18129-0_6.

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Guoxiang, Chang, Gao Qiaoli, Gao Xinming, and Cheng Junting. "Transformer Fault Diagnosis Based on BP Neural Network by Improved Apriori Algorithm." In Green Energy and Networking, 255–66. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-62483-5_27.

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Tian, Guangqiang, Yulin Ren, Fuzhong Wang, and Li Zhang. "Ultrasonic Localization of Transformer Partial Discharge Based on Improved Fruit Fly Algorithm." In Lecture Notes in Electrical Engineering, 767–76. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-6328-4_77.

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Conference papers on the topic "Transformer-based algorithm"

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Akpinar, Kubra Nur, Okan Ozgonenel, and Unal Kurt. "Transformer Protection Algorithm Based on S-Transform." In 2020 28th Signal Processing and Communications Applications Conference (SIU). IEEE, 2020. http://dx.doi.org/10.1109/siu49456.2020.9302473.

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Xi, Zhizhong, Jingen Wang, and Yanqing Kang. "Oriented Target Detection Algorithm Based on Transformer." In AIPR 2021: 2021 4th International Conference on Artificial Intelligence and Pattern Recognition. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3488933.3488954.

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Zhao, Jiyin, Ruirui Zheng, and Jianpo Li. "Transformer fault diagnosis based on homotopy BP algorithm." In Instruments (ICEMI). IEEE, 2009. http://dx.doi.org/10.1109/icemi.2009.5274664.

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Ozgonenel, O., M. A. Khan, and M. A. Rahman. "Wavelet power based transformer internal fault protection algorithm." In IET 9th International Conference on Developments in Power Systems Protection (DPSP 2008). IEE, 2008. http://dx.doi.org/10.1049/cp:20080050.

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Ouyang, Fan, Yongqiang Liu, Zhaowen Liang, Zitian Qiu, and Bo Yuan. "Parameter identification of transformer based on PSO algorithm." In 2018 International Conference on Power System Technology (POWERCON). IEEE, 2018. http://dx.doi.org/10.1109/powercon.2018.8601728.

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Chen, Yufeng, Xiuming Du, and Liwei Zhou. "Transformer defect correlation analysis based on Apriori algorithm." In 2016 IEEE International Conference on High Voltage Engineering and Application (ICHVE). IEEE, 2016. http://dx.doi.org/10.1109/ichve.2016.7800686.

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Yanshuo, Lv. "Transformer Intelligent Diagnosis Method Based on AFSA-MKELM Algorithm." In the 2019 International Conference. New York, New York, USA: ACM Press, 2019. http://dx.doi.org/10.1145/3366194.3366275.

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Zheng Xiao-Li, Wang Shen-Qiang, Cao Yan-Zhao, and Wei Lei-Yuan. "Transformer fault diagnosis based on improved fuzzy ISODATA algorithm." In 2014 International Conference on Power System Technology (POWERCON). IEEE, 2014. http://dx.doi.org/10.1109/powercon.2014.6993582.

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Zhang, Chen, and Tangwen Yang. "Transformer-based Algorithm for Commodity Detection in Fisheye Images." In 2022 16th IEEE International Conference on Signal Processing (ICSP). IEEE, 2022. http://dx.doi.org/10.1109/icsp56322.2022.9965271.

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Li, Zongbo, Zaibin Jiao, Yifei Wang, and Feng Ma. "A magnetization hysteresis-based power transformer fault detection algorithm." In 2017 IEEE Power & Energy Society General Meeting (PESGM). IEEE, 2017. http://dx.doi.org/10.1109/pesgm.2017.8273825.

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