Journal articles: 'Transformer-based algorithm'

1

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.

Full text

Abstract:

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.

APA, Harvard, Vancouver, ISO, and other styles

2

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.

Full text

Abstract:

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.

APA, Harvard, Vancouver, ISO, and other styles

3

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.

Full text

Abstract:

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.

APA, Harvard, Vancouver, ISO, and other styles

4

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.

Full text

Abstract:

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.

APA, Harvard, Vancouver, ISO, and other styles

5

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.

Full text

Abstract:

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.

APA, Harvard, Vancouver, ISO, and other styles

6

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.

Full text

Abstract:

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.

APA, Harvard, Vancouver, ISO, and other styles

7

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.

Full text

Abstract:

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.

APA, Harvard, Vancouver, ISO, and other styles

8

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.

Full text

Abstract:

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.

APA, Harvard, Vancouver, ISO, and other styles

9

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.

Full text

Abstract:

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%.

APA, Harvard, Vancouver, ISO, and other styles

10

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.

Full text

Abstract:

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.

APA, Harvard, Vancouver, ISO, and other styles

11

Jiatang, Cheng, Ai Li, and Xiong Yan. "Transformer Fault Diagnosis Based on Multi-Algorithm Fusion." Recent Advances in Electrical & Electronic Engineering (Formerly Recent Patents on Electrical & Electronic Engineering) 9, no. 3 (February 10, 2017): 249–54. http://dx.doi.org/10.2174/2352096509666161115143928.

Full text

APA, Harvard, Vancouver, ISO, and other styles

12

Chen, Guowei. "3D Human Pose Estimation Based on Transformer Algorithm." Mobile Information Systems 2022 (August 29, 2022): 1–9. http://dx.doi.org/10.1155/2022/6858822.

Full text

Abstract:

Human pose estimation (HPE) is a fundamental problem in computer vision, and it is also the basis of applied research in many fields, which can be used for virtual fitting, fashion analysis, behavior analysis, human-computer interaction, and auxiliary pedestrian detection. The purpose of HPE is to use image processing and machine learning methods to find out the positions and types of joints of people in pictures. There are two main difficulties in HPE. First, the complex human images make the model need to learn a highly nonlinear mapping relationship, and the learning of this mapping relationship is extremely difficult. Second, the highly nonlinear mapping relationship needs to be learned by using a model with high complexity, and a model with high complexity requires a lot of computational overhead. In this context, this paper studies the 3D HPE based on the transformer. We introduce the research status of HPE at home and abroad and provide a theoretical basis for designing the transformer 3D HPE model in this paper. We introduce the technical principle and optimization scheme of CNN and transformer and propose a 3D HPE model based on transformer. We used two datasets, COCO and the MPII datasets, and performed a number of experiments to find the best parameters for model development and then assess the model’s performance. The experimental findings suggest that the strategy described in this study outperforms all other methods on both datasets. The average precision (AP) of our model reaches up to 79% on COCO dataset but a PCKh-0.5 score of 81.5% on the MPII dataset.

APA, Harvard, Vancouver, ISO, and other styles

13

Jiao, Zaibin, and Zongbo Li. "Novel Magnetization Hysteresis-Based Power-Transformer Protection Algorithm." IEEE Transactions on Power Delivery 33, no. 5 (October 2018): 2562–70. http://dx.doi.org/10.1109/tpwrd.2018.2837022.

Full text

APA, Harvard, Vancouver, ISO, and other styles

14

陈, 勇. "Image Denoising Algorithm Based on Improved Transformer Network." Computer Science and Application 12, no. 12 (2022): 2763–71. http://dx.doi.org/10.12677/csa.2022.1212280.

Full text

APA, Harvard, Vancouver, ISO, and other styles

15

Lian, Han. "The State Monitoring Method of Electronic Voltage Transformer Based on L-M Algorithm." Journal of Advanced Computational Intelligence and Intelligent Informatics 23, no. 3 (May 20, 2019): 385–89. http://dx.doi.org/10.20965/jaciii.2019.p0385.

Full text

Abstract:

In the traditional method of monitoring the state of electronic voltage transformer, there are problems of large monitoring error and weak robustness. Therefore, a new state monitoring method of electronic voltage transformer based on L-M algorithm is proposed. The relationship between input voltage and output voltage of capacitor voltage divider in electronic voltage transformer is obtained by using Laplasse transform. The transfer function model of electronic voltage transformer is constructed based on the relationship result and L-M algorithm. The transfer function model is used to analyze the frequency characteristics of the electronic voltage transformer and the range of normal measurement frequency, and then the partial pressure ratio of the electronic voltage transformer under the high frequency condition is derived. On this basis, by calculating the over voltage amplitude on the two sides of acquisition card in the electronic voltage transformer, the capacitance value between the two adjacent coaxial cylindrical cylinders of the capacitance divider in the electronic voltage transformer is obtained, thus the monitoring of the state of the electronic voltage transformer is completed. The experimental results show that the proposed method has low detection error and strong robustness, and can effectively improve the reliability of electronic voltage transformer.

APA, Harvard, Vancouver, ISO, and other styles

16

Gu, B., and J. C. Tan. "An Instantaneous Measurement Based Transformer Protection Scheme." Advanced Materials Research 383-390 (November 2011): 5188–92. http://dx.doi.org/10.4028/www.scientific.net/amr.383-390.5188.

Full text

Abstract:

A transformer protection scheme using fault component computed from instantaneous measurement values is proposed in this paper. The algorithm utilizes reactive power directional elements computed from the received IEC 61850-9-2 sampled values, and uses the ratio of active and reactive currents to determine an inrush condition. A transformer fault is declaimed if the directional elements from all transformer terminals seen the fault in its forward direction. Extensive simulation tests show that the proposed algorithm is sensitive to detecting faults, and is able to distinguish faults internal or external to the protected transformer zone, and to discriminate a fault from inrush conditions.

APA, Harvard, Vancouver, ISO, and other styles

17

Li, Zhi Bin, and Qi Ben Li. "Multi-Level Fault Diagnosis of Power Transformer Based on Fusion Technology." Advanced Materials Research 860-863 (December 2013): 1925–28. http://dx.doi.org/10.4028/www.scientific.net/amr.860-863.1925.

Full text

Abstract:

Traditional transformer fault diagnosis based on single source of information has significant limitation in identification of transformer fault type because of power transformers complex structure and changeable operating environment. So fusion technology is introduced into the fault diagnosis of power transformer. This method divides the progress of transformer fault diagnosis into two fusion levels. The first level is to ascertain whether it is overheated or discharged by content of gases dissolved in transformer oil. The second level is to ascertain the location or cause of the fault by electric data. The intelligence algorithms which are used in these two levels are both the improved BP neural network algorithm. Finally, the effectiveness is validated by the result of practical fault diagnosis examples.

APA, Harvard, Vancouver, ISO, and other styles

18

Huang, Shizhen, Enhao Tang, Shun Li, Xiangzhan Ping, and Ruiqi Chen. "Hardware-friendly compression and hardware acceleration for transformer: A survey." Electronic Research Archive 30, no. 10 (2022): 3755–85. http://dx.doi.org/10.3934/era.2022192.

Full text

Abstract:

<abstract> <p>The transformer model has recently been a milestone in artificial intelligence. The algorithm has enhanced the performance of tasks such as Machine Translation and Computer Vision to a level previously unattainable. However, the transformer model has a strong performance but also requires a high amount of memory overhead and enormous computing power. This significantly hinders the deployment of an energy-efficient transformer system. Due to the high parallelism, low latency, and low power consumption of field-programmable gate arrays (FPGAs) and application specific integrated circuits (ASICs), they demonstrate higher energy efficiency than Graphics Processing Units (GPUs) and Central Processing Units (CPUs). Therefore, FPGA and ASIC are widely used to accelerate deep learning algorithms. Several papers have addressed the issue of deploying the Transformer on dedicated hardware for acceleration, but there is a lack of comprehensive studies in this area. Therefore, we summarize the transformer model compression algorithm based on the hardware accelerator and its implementation to provide a comprehensive overview of this research domain. This paper first introduces the transformer model framework and computation process. Secondly, a discussion of hardware-friendly compression algorithms based on self-attention and Transformer is provided, along with a review of a state-of-the-art hardware accelerator framework. Finally, we considered some promising topics in transformer hardware acceleration, such as a high-level design framework and selecting the optimum device using reinforcement learning.</p> </abstract>

APA, Harvard, Vancouver, ISO, and other styles

19

Zhang, Peng, Ying Zhang, Xinhua Si, and Zhibin Yu. "Power Transformer Fault Detection Based on Improved Capsule Coverage Algorithm." Journal of Physics: Conference Series 2333, no. 1 (August 1, 2022): 012017. http://dx.doi.org/10.1088/1742-6596/2333/1/012017.

Full text

Abstract:

Abstract In the process of transformer fault detection, oil chromatographic analysis is usually used to observe the change of the dissolved gas content in the transformer oil, warn and judge the internal fault of the transformer. However, in the actual monitoring environment, there inevitably are the problems of missing data, redundancy, and uncertain dimensions. In order to solve the problem of low recognition efficiency or even incorrect recognition caused by the above problems in transformer fault detection, the idea of nuclear is introduced to the improved capsule coverage algorithm. The experimental results obtained by this improved algorithm indicate that the optimized capsule coverage algorithm can realize the accurate identification of transformer faults, it can not only improve the fault maintenance efficiency, but also ensure the transformer has a good power supply level, which is beneficial to improve the construction of national comprehensive power grid.

APA, Harvard, Vancouver, ISO, and other styles

20

El-kenawy, El-Sayed M., Fahad Albalawi, Sayed A. Ward, Sherif S. M. Ghoneim, Marwa M. Eid, Abdelaziz A. Abdelhamid, Nadjem Bailek, and Abdelhameed Ibrahim. "Feature Selection and Classification of Transformer Faults Basedon Novel Meta-Heuristic Algorithm." Mathematics 10, no. 17 (September 1, 2022): 3144. http://dx.doi.org/10.3390/math10173144.

Full text

Abstract:

Detecting transformer faults is critical to avoid the undesirable loss of transformers from service and ensure utility service continuity. Transformer faults diagnosis can be determined based on dissolved gas analysis (DGA). The DGA traditional techniques, such as Duval triangle, Key gas, Rogers’ ratio, Dornenburg, and IEC code 60599, suffer from poor transformer faults diagnosis. Therefore, recent research has been developed to diagnose transformer fault and the diagnostic accuracy using combined traditional methods of DGA with artificial intelligence and optimization methods. This paper used a novel meta-heuristic technique, based on Gravitational Search and Dipper Throated Optimization Algorithms (GSDTO), to enhance the transformer faults’ diagnostic accuracy, which was considered a novelty in this work to reduce the misinterpretation of the transformer faults. The robustness of the constructed GSDTO-based model was addressed by the statistical study using Wilcoxon’s rank-sum and ANOVA tests. The results revealed that the constructed model enhanced the diagnostic accuracy up to 98.26% for all test cases.

APA, Harvard, Vancouver, ISO, and other styles

21

Shao, Ningning, Xiaoqiang Chen, and Ying Wang. "Research on IPSO-RBF transformer fault diagnosis based on Adam optimization." Journal of Physics: Conference Series 2290, no. 1 (June 1, 2022): 012117. http://dx.doi.org/10.1088/1742-6596/2290/1/012117.

Full text

Abstract:

Abstract As the lifeblood element of the power system, the transformer plays a pivotal role in power transmission and voltage conversion. The fault prediction of the transformer can not only realize the early warning before the fault but also provide theoretical support for the formulation of a transformer maintenance scheme, which can improve the safety and reliability of the power system. In this paper, a new method of transformer fault diagnosis based on dissolved gas in oil is proposed by combining the Adam optimization algorithm based on the classical momentum concept with the PSO algorithm. Firstly, a PSO-RBF transformer fault diagnosis model is constructed. Through the simulation experiment of nonlinear collocation of acceleration factors, the nonlinear exponential decreasing collocation is used to improve the optimization ability of particle swarm optimization. The simulation analysis is carried out based on the transformer fault data within the jurisdiction of Lanzhou electric power company. The diagnosis results verify that the diagnosis rate and stability of the IPSO-RBF-Adam transformer fault diagnosis model are better than the PSO-RBF model.

APA, Harvard, Vancouver, ISO, and other styles

22

Zhang, Shuang, Qing He Hu, and Xing Wei Wang. "Application of Intelligent Algorithm to Transformer Optimal Design." Advanced Materials Research 219-220 (March 2011): 1578–83. http://dx.doi.org/10.4028/www.scientific.net/amr.219-220.1578.

Full text

Abstract:

The paper studies transformer optimal design, establishes optimal transformer model based on total owning cost. It adopts penalty function to process objective function with weighted coefficients. For prematurity and low speed of convergence of Simple Genetic Algorithm, improved adaptive genetic algorithm is adopted. It increases crossover and mutation rates, and improves fitness function. It is adopted to search for minimum total owning cost of transformer. The result shows that the algorithm performs well, increases converging speed and betters solution.

APA, Harvard, Vancouver, ISO, and other styles

23

Wu, Tianyi, Xiangyi Xu, Lu Chen, and Weijie Tang. "Transformer evaluation strategy based on improved machine learning." Journal of Physics: Conference Series 2221, no. 1 (May 1, 2022): 012019. http://dx.doi.org/10.1088/1742-6596/2221/1/012019.

Full text

Abstract:

Abstract A transformer state assessment method based on an improved extreme learning machine is proposed in this paper, which introduces an adaptive evolution algorithm into the extreme learning machine. This method is used in the evaluation model to evaluate the state of the transformer. The algorithm is trained and tested through sample sets. Furthermore, the test results are analyzed to prove the feasibility of the proposed control strategy.

APA, Harvard, Vancouver, ISO, and other styles

24

Luo, Maolin, Linghua Xu, Yongliang Yang, Min Cao, and Jing Yang. "Laboratory Flame Smoke Detection Based on an Improved YOLOX Algorithm." Applied Sciences 12, no. 24 (December 15, 2022): 12876. http://dx.doi.org/10.3390/app122412876.

Full text

Abstract:

Fires in university laboratories often lead to serious casualties and property damage, and traditional sensor-based fire detection techniques suffer from fire warning delays. Current deep learning algorithms based on convolutional neural networks have the advantages of high accuracy, low cost, and high speeds in processing image-based data, but their ability to process the relationship between visual elements and objects is inferior to Transformer. Therefore, this paper proposes an improved YOLOX target detection algorithm combining Swin Transformer architecture, the CBAM attention mechanism, and a Slim Neck structure applied to flame smoke detection in laboratory fires. The experimental results verify that the improved YOLOX algorithm has higher detection accuracy and more accurate position recognition for flame smoke in complex situations, with APs of 92.78% and 92.46% for flame and smoke, respectively, and an mAP value of 92.26%, compared with the original YOLOX algorithm, SSD, Faster R-CNN, YOLOv4, and YOLOv5. The detection accuracy is improved, which proves the effectiveness and superiority of this improved YOLOX target detection algorithm in fire detection.

APA, Harvard, Vancouver, ISO, and other styles

25

Shixi, Su, Luo Hui, and Liao Zhikun. "Design of control algorithm based on high voltage transformer." Journal of Physics: Conference Series 1754, no. 1 (February 1, 2021): 012202. http://dx.doi.org/10.1088/1742-6596/1754/1/012202.

Full text

APA, Harvard, Vancouver, ISO, and other styles

26

Li, Jifang, Genxu Li, Chen Hai, and Mengbo Guo. "Transformer Fault Diagnosis Based on Multi-Class AdaBoost Algorithm." IEEE Access 10 (2022): 1522–32. http://dx.doi.org/10.1109/access.2021.3135467.

Full text

APA, Harvard, Vancouver, ISO, and other styles

27

Erenturk, K. "ANFIS-Based Compensation Algorithm for Current-Transformer Saturation Effects." IEEE Transactions on Power Delivery 24, no. 1 (January 2009): 195–201. http://dx.doi.org/10.1109/tpwrd.2008.2005882.

Full text

APA, Harvard, Vancouver, ISO, and other styles

28

Basha, A. M., and K. Anantha Raman. "PC based IIR filter algorithm for power transformer relaying." Electric Power Systems Research 28, no. 2 (November 1993): 123–27. http://dx.doi.org/10.1016/0378-7796(93)90005-y.

Full text

APA, Harvard, Vancouver, ISO, and other styles

29

Bastard, P. "Neural network-based algorithm for power transformer differential relays." IEE Proceedings - Generation, Transmission and Distribution 142, no. 4 (1995): 386. http://dx.doi.org/10.1049/ip-gtd:19951817.

Full text

APA, Harvard, Vancouver, ISO, and other styles

30

Ali, E., O. P. Malik, A. Knight, S. Abdelkader, A. Helal, and H. Desouki. "Ratios-based universal differential protection algorithm for power transformer." Electric Power Systems Research 186 (September 2020): 106383. http://dx.doi.org/10.1016/j.epsr.2020.106383.

Full text

APA, Harvard, Vancouver, ISO, and other styles

31

Jaiswal, Gajanan C., Makarand S. Ballal, Prasad A. Venikar, Dhananjay R. Tutakne, and Hiralal M. Suryawanshi. "Genetic algorithm-based health index determination of distribution transformer." International Transactions on Electrical Energy Systems 28, no. 5 (January 19, 2018): e2529. http://dx.doi.org/10.1002/etep.2529.

Full text

APA, Harvard, Vancouver, ISO, and other styles

32

Zheng, Rui Rui, Bao Chun Wu, and Ji Yin Zhao. "Prediction of Power Transformer Fault Based on Auto Regression Model." Advanced Materials Research 317-319 (August 2011): 2230–33. http://dx.doi.org/10.4028/www.scientific.net/amr.317-319.2230.

Full text

Abstract:

Dissolved gases analysis is the essence to diagnose and forecast power transformer fault. This paper utilized an Auto Regression model to predict contents of gases dissolved in power transformer oil, and adopted Akaike's Information Criterion to determine model order. Then, the prediction results of AR model are compared with results of Gray model. Finally, gray artificial immune algorithm diagnosed power transformer fault types through gases contents predicted by Auto Regression model. Experiments demonstrates that Auto Regression model has a higher accuracy than Gray Model, and the fault prediction results of the proposed algorithm are in accord with the results using real gases contents, thus , the power transformer fault prediction algorithm present in the paper is effective and reliable.

APA, Harvard, Vancouver, ISO, and other styles

33

Sun, Hui Qin, Zhi Hong Xue, Ke Jun Sun, Su Zhi Wang, and Yun Du. "Fault Diagnosis Analysis of Power Transformer Based on PSO-BP Algorithm." Advanced Materials Research 466-467 (February 2012): 789–93. http://dx.doi.org/10.4028/www.scientific.net/amr.466-467.789.

Full text

Abstract:

BP neural network is currently the most widely used of neural network models in practical application in transformer fault diagnosis. BP algorithm is a local search algorithm which is easy to make the network into the local minimum values. Network training results are poor. It discusses PSO-BP algorithm which combines the particle swarm optimization (PSO) algorithm with the BP algorithm in this paper. It uses PSO algorithm to optimize the BP network’s weights and threshold. It is used in power transformer fault diagnosis. Experimental data results show that PSO-BP network fault diagnosis accuracy is higher than BP algorithm.

APA, Harvard, Vancouver, ISO, and other styles

34

Ma, Xin, Yu Luo, Jian Shi, and Hailiang Xiong. "Acoustic Emission Based Fault Detection of Substation Power Transformer." Applied Sciences 12, no. 5 (March 7, 2022): 2759. http://dx.doi.org/10.3390/app12052759.

Full text

Abstract:

Fault detection of Substation Power Transformer by Non-contact measurement is important for the safety of machines, instruments, and human beings. To make non-contact measurement as convenient as possible, it is desirable that efficient algorithms based on AE (acoustic emission) discrimination are developed. This paper presents a system for quick and effective fault detection of substation power transformer, based on AE signals collected by non-contact single microphones. In the experiment, collected data were preprocessed in multiple ways and three machine learning algorithms were designed based on classifiers (Convolutional Neural Network (CNN), support vector machine (SVM), and k-nearest neighbors (KNN) algorithm) trained and tested by a tenfold cross-validation technique. After comparison among the designed classifiers, the results show the two-dimensional principal component analysis (2DPCA) preprocess combined with SVM achieved the best comprehensive effectiveness.

APA, Harvard, Vancouver, ISO, and other styles

35

Xu, Jun, Zi-Xuan Chen, Hao Luo, and Zhe-Ming Lu. "An Efficient Dehazing Algorithm Based on the Fusion of Transformer and Convolutional Neural Network." Sensors 23, no. 1 (December 21, 2022): 43. http://dx.doi.org/10.3390/s23010043.

Full text

Abstract:

The purpose of image dehazing is to remove the interference from weather factors in degraded images and enhance the clarity and color saturation of images to maximize the restoration of useful features. Single image dehazing is one of the most important tasks in the field of image restoration. In recent years, due to the progress of deep learning, single image dehazing has made great progress. With the success of Transformer in advanced computer vision tasks, some research studies also began to apply Transformer to image dehazing tasks and obtained surprising results. However, both the deconvolution-neural-network-based dehazing algorithm and Transformer based dehazing algorithm magnify their advantages and disadvantages separately. Therefore, this paper proposes a novel Transformer–Convolution fusion dehazing network (TCFDN), which uses Transformer’s global modeling ability and convolutional neural network’s local modeling ability to improve the dehazing ability. In the Transformer–Convolution fusion dehazing network, the classic self-encoder structure is used. This paper proposes a Transformer–Convolution hybrid layer, which uses an adaptive fusion strategy to make full use of the Swin-Transformer and convolutional neural network to extract and reconstruct image features. On the basis of previous research, this layer further improves the ability of the network to remove haze. A series of contrast experiments and ablation experiments not only proved that the Transformer–Convolution fusion dehazing network proposed in this paper exceeded the more advanced dehazing algorithm, but also provided solid and powerful evidence for the basic theory on which it depends.

APA, Harvard, Vancouver, ISO, and other styles

36

Iqteit, Nassim A., and Khalid Yahya. "Simulink model of transformer differential protection using phase angle difference based algorithm." International Journal of Power Electronics and Drive Systems (IJPEDS) 11, no. 2 (June 1, 2020): 1088. http://dx.doi.org/10.11591/ijpeds.v11.i2.pp1088-1098.

Full text

Abstract:

<p class="p1">An application of phase-angle-difference based algorithm with percentage differential relays is presented in this paper. In the situation where the transformer differential relay is under magnetizing inrush current, the algorithm will be utilized to block the process. In this study, the technique is modeled and implemented using Simulink integrated with MATLAB. The real circuit model of power transformer and current transformers are considered in the simulation model. The results confirmed the effectiveness of the technique in different operation modes; such as, magnetizing inrush currents, current transformers saturation and internal transformer faults.</p>

APA, Harvard, Vancouver, ISO, and other styles

37

Wang, MingYu, and Rui Cheng. "Research on multi-label classification method of transformer based on DBSCAN (Density-Based Spatial Clustering of Applications with Noise) clustering algorithm." Journal of Physics: Conference Series 2132, no. 1 (December 1, 2021): 012008. http://dx.doi.org/10.1088/1742-6596/2132/1/012008.

Full text

Abstract:

Abstract With the improvement of the intelligent level of power grid and the enhancement of the integrated characteristics of power grid, the degree of discretization of massive data of power equipment gradually increases, which brings great challenges to the safe and stable operation of power grid. How to process and analyze data effectively has become an important research content. Transformer is an important electrical equipment, therefore it is of great significance to monitor the operation status of transformer, to construct transformer operation characteristic label system based on multi-source heterogeneous data, and to realize multi-label classification function. In this paper, a transformer multi-label classification method of transformer based on DBSCAN(Density-Based Spatial Clustering of Applications with Noise) clustering algorithm is proposed, which can accurately identify outliers as Noise without input of the number of clustering to be divided, realize the key feature mining of transformer state, and to realize to provide flexible information association and historical data for dispatch and control operators.

APA, Harvard, Vancouver, ISO, and other styles

38

Liu, Yi, Xintao Xu, Bajian Xiang, Gang Chen, Guoliang Gong, and Huaxiang Lu. "Transformer Based Binocular Disparity Prediction with Occlusion Predict and Novel Full Connection Layers." Sensors 22, no. 19 (October 6, 2022): 7577. http://dx.doi.org/10.3390/s22197577.

Full text

Abstract:

The depth estimation algorithm based on the convolutional neural network has many limitations and defects by constructing matching cost volume to calculate the disparity: using a limited disparity range, the authentic disparity beyond the predetermined range can not be acquired; Besides, the matching process lacks constraints on occlusion and matching uniqueness; Also, as a local feature extractor, a convolutional neural network lacks the ability of global context information perception. Aiming at the problems in the matching method of constructing matching cost volume, we propose a disparity prediction algorithm based on Transformer, which specifically comprises the Swin-SPP module for feature extraction based on Swin Transformer, Transformer disparity matching network based on self-attention and cross-attention mechanism, and occlusion prediction sub-network. In addition, we propose a double skip connection fully connected layer to solve the problems of gradient vanishing and explosion during the training process for the Transformer model, thus further enhancing inference accuracy. The proposed model in this paper achieved an EPE (Absolute error) of 0.57 and 0.61, and a 3PE (Percentage error greater than 3 px) of 1.74% and 1.56% on KITTI 2012 and KITTI 2015 datasets, respectively, with an inference time of 0.46 s and parameters as low as only 2.6 M, showing great advantages compared with other algorithms in various evaluation metrics.

APA, Harvard, Vancouver, ISO, and other styles

39

Duan, Hui Da, and Qiao Song Li. "Power Transformers Fault Diagnosis Based on DRNN." Advanced Materials Research 960-961 (June 2014): 700–703. http://dx.doi.org/10.4028/www.scientific.net/amr.960-961.700.

Full text

Abstract:

In recent years, improved three-ratio is an effective method for transformer fault diagnosis based on Dissolved Gas Analysis (DGA). In this paper, diagonal recurrent neural network (DRNN) is used to resolve the online fault diagnosis problems for oil-filled power transformer based on DGA. To overcome disadvantages of BP algorithm, a new recursive prediction error algorithm (RPE) is used in this paper.In addition, to demonstrate the effectiveness and veracity of the proposed method, some cases are used in the simulation. The simulation results are satisfactory.

APA, Harvard, Vancouver, ISO, and other styles

40

Chen, Chong, Tao Wang, Ying Liu, Lianglun Cheng, and Jian Qin. "Spatial attention-based convolutional transformer for bearing remaining useful life prediction." Measurement Science and Technology 33, no. 11 (August 2, 2022): 114001. http://dx.doi.org/10.1088/1361-6501/ac7c5b.

Full text

Abstract:

Abstract The remaining useful life (RUL) prediction is of significance to the health management of bearings. Recently, deep learning has been widely investigated for bearing RUL prediction due to its great success in sequence learning. However, the improvement of the prediction accuracy of existing deep learning algorithms heavily relies on feature engineering such as handcrafted feature generation and time–frequency transformation, which increase the complexity and difficulty of the actual deployment. In this paper, a novel spatial attention-based convolutional transformer (SAConvFormer) is proposed to establish an accurate bearing RUL prediction model based on raw vibration data without prior knowledge or feature engineering. In this algorithm, firstly, a convolutional neural network enhanced by a spatial attention mechanism is proposed to squeeze the feature maps and extract the local and global features from raw bearing vibration data effectively. Then, the extracted senior features are fed into a transformer network to further explore the sequential patterns relevant to the bearing RUL. An experimental study using the XJTU-SY rolling bearings dataset revealed the merits of the proposed deep learning algorithm in terms of root-mean-square-error (RMSE) and mean-absolute-error (MAE) in comparison with other state-of-the-art algorithms.

APA, Harvard, Vancouver, ISO, and other styles

41

Yong, Mingchao, Jianmin Xue, Weijie Wang, Bangtian Wang, Lidong Lv, Qingshan Wang, Hongguang Shi, Bingxin Wu, and Bogen Chen. "Research on Condition Evaluation Algorithm of Oil-immersed Transformer Based on Naive Bayes." Journal of Physics: Conference Series 2196, no. 1 (February 1, 2022): 012021. http://dx.doi.org/10.1088/1742-6596/2196/1/012021.

Full text

Abstract:

Abstract The improved three ratio method is a general algorithm for condition evaluation of oil immersed transformer. It has high accuracy in pre-test and laboratory analysis. However, the accuracy of DGA online monitoring data is not high, resulting in the decline of the positive judgment rate of transformer condition evaluation using the improved three ratio method, which is difficult to support the development requirements of transformer intelligence. To solve this problem, a DGA state evaluation method based on Naive Bayesian algorithm is proposed. The algorithm first performs preprocessing such as median filtering on the DGA online monitoring data to remove invalid data, then uses a triple composed of three conditional attributes to describe the characteristics of the DGA data, and finally calculates the a priori probability of training samples and the a posteriori probability of test samples by naive Bayesian algorithm for state evaluation. The verification of the algorithm on the measured data set shows that the accuracy of the algorithm is better than the improved three ratio method, and the algorithm is feasible and effective

APA, Harvard, Vancouver, ISO, and other styles

42

Yang, Cheng, Tianliang Lu, Shangyi Yan, Jianling Zhang, and Xingzhan Yu. "N-Trans: Parallel Detection Algorithm for DGA Domain Names." Future Internet 14, no. 7 (July 13, 2022): 209. http://dx.doi.org/10.3390/fi14070209.

Full text

Abstract:

Domain name generation algorithms are widely used in malware, such as botnet binaries, to generate large sequences of domain names of which some are registered by cybercriminals. Accurate detection of malicious domains can effectively defend against cyber attacks. The detection of such malicious domain names by the use of traditional machine learning algorithms has been explored by many researchers, but still is not perfect. To further improve on this, we propose a novel parallel detection model named N-Trans that is based on the N-gram algorithm with the Transformer model. First, we add flag bits to the first and last positions of the domain name for the parallel combination of the N-gram algorithm and Transformer framework to detect a domain name. The model can effectively extract the letter combination features and capture the position features of letters in the domain name. It can capture features such as the first and last letters in the domain name and the position relationship between letters. In addition, it can accurately distinguish between legitimate and malicious domain names. In the experiment, the dataset is the legal domain name of Alexa and the malicious domain name collected by the 360 Security Lab. The experimental results show that the parallel detection model based on N-gram and Transformer achieves 96.97% accuracy for DGA malicious domain name detection. It can effectively and accurately identify malicious domain names and outperforms the mainstream malicious domain name detection algorithms.

APA, Harvard, Vancouver, ISO, and other styles

43

Fuentes, Carlos, Hector Chavez, and Mario R. Arrieta Paternina. "Predictive Control-Based NADIR-Minimizing Algorithm for Solid-State Transformer." Energies 15, no. 1 (December 23, 2021): 73. http://dx.doi.org/10.3390/en15010073.

Full text

Abstract:

Solid-state transformers (SSTs) are becoming an important solution to control active distribution systems. Their significant flexibility in comparison with traditional magnetic transformers is essential to ensure power quality and protection coordination at the distribution level in scenarios of large penetration of distributed energy resources such as renewables, electric vehicles and energy storage. However, the power electronic interface of SSTs decouples the nature of the inertial and frequency responses of distribution loads, deteriorating the frequency stability, especially under the integration of large-scale solar and wind power plants. Despite the virtual inertia/voltage sensitivity-based algorithms that have been proposed, the frequency sensitivity of loads and the capability of guaranteeing optimal control, considering the operating restrictions, have been overlooked. To counteract this specific issue, this work proposes a predictive control-driven approach to provide SSTs with frequency response actions by a strategy that harnesses the voltage and frequency sensibility of distribution loads and considers the limitations of voltage and frequency given by grid codes at distribution grids. In particular, the control strategy is centered in minimizing the NADIR of frequency transients. Numerical results are attained employing an empirically-validated model of the power system frequency dynamics and a dynamic model of distribution loads. Through proportional frequency control, the results of the proposed algorithm are contrasted. It is demonstrated that the NADIR improved about 0.1 Hz for 30% of SST penetration.

APA, Harvard, Vancouver, ISO, and other styles

44

Wang, Tianbing, Lei Zhang, and Yufeng Wu. "Research on transformer fault diagnosis based on GWO-RF algorithm." Journal of Physics: Conference Series 1952, no. 3 (June 1, 2021): 032054. http://dx.doi.org/10.1088/1742-6596/1952/3/032054.

Full text

APA, Harvard, Vancouver, ISO, and other styles

45

Liu, Fei, Shili Liu, Xiang Gao, and Xiaohu Zhu. "Research on Transformer Life Forecast Based on Random Forest Algorithm." Journal of Physics: Conference Series 1992, no. 4 (August 1, 2021): 042064. http://dx.doi.org/10.1088/1742-6596/1992/4/042064.

Full text

APA, Harvard, Vancouver, ISO, and other styles

46

Subramanian, Srikrishna, and Seeni Padma. "Bacterial Foraging Algorithm based Parameter Estimation of Three WINDING Transformer." Energy and Power Engineering 03, no. 02 (2011): 135–43. http://dx.doi.org/10.4236/epe.2011.32017.

Full text

APA, Harvard, Vancouver, ISO, and other styles

47

Zhong, Ji-you, Jin-xiao Wei, Xiao-rong Wu, and Hao Tang. "Transformer Fault Diagnosis Based on RapidMiner and Modified ELM Algorithm." Journal of Physics: Conference Series 1585 (July 2020): 012030. http://dx.doi.org/10.1088/1742-6596/1585/1/012030.

Full text

APA, Harvard, Vancouver, ISO, and other styles

48

Guillén, Daniel, Hector Esponda, Ernesto Vázquez, and Gina Idárraga-Ospina. "Algorithm for transformer differential protection based on wavelet correlation modes." IET Generation, Transmission & Distribution 10, no. 12 (September 2, 2016): 2871–79. http://dx.doi.org/10.1049/iet-gtd.2015.1147.

Full text

APA, Harvard, Vancouver, ISO, and other styles

49

Chen, Xue Zhen, Yong Li Zhu, and Fei Pei. "Prediction Research of Transformer Fault Based on Regular Extreme Learning Machine." Advanced Materials Research 1049-1050 (October 2014): 1205–9. http://dx.doi.org/10.4028/www.scientific.net/amr.1049-1050.1205.

Full text

Abstract:

To predict the concentration of dissolved gas in transformer oil, and then realize the transformer latent fault prediction, can effectively prevent unnecessary loss caused by the transformer faults .In order to improve the transformer fault prediction ability,this paper proposes a new transformer fault prediction model--Regular Extreme Learning Machine (RELM) prediction model。RELM algorithm introduce structure risk minimization principle on the basis of traditional ELM, using the balance factor to weigh the empirical risk and the risk of structure size, further enhance the generalization performance of ELM. Verified by examples, the proposed prediction model based on the RELM in this paper achieve better generalization performance and prediction accuracy in the forecast of gases concentration dissolved in transformer oil.

APA, Harvard, Vancouver, ISO, and other styles

50

Wang, De Wen, and Zhi Wei Sun. "Parallelization Analysis of Dissolved Gases in Transformer Oil Based on Random Forest Algorithm." Applied Mechanics and Materials 519-520 (February 2014): 98–101. http://dx.doi.org/10.4028/www.scientific.net/amm.519-520.98.

Full text

Abstract:

Dissolved gas analysis (DGA) in oil is an important method for transformer fault diagnosis. This paper use random forest parallelization algorithm to analysis the dissolved gases in transformer oil. This method can achieve a fast parallel fault diagnosis for power equipment. Experimental results of the diagnosis of parallelization of random forest algorithm with DGA samples show that this algorithm not only can improve the accuracy of fault diagnosis, and more appropriate for dealing with huge amounts of data, but also can meet the smart grid requirements for fast fault diagnosis for power transformer. And this result also verifies the feasibility and effectiveness of the algorithm.

APA, Harvard, Vancouver, ISO, and other styles

Journal articles on the topic 'Transformer-based algorithm'

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles