Vibration Scale Model of a Converter Transformer Based on the Finite Element and Similarity Principle and Its Preparation

Wang, Hao; Zhang, Li; Sun, Yunzhou; Zou, Liang

doi:10.3390/pr11071969

Cited by 4 publications

(1 citation statement)

References 29 publications

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“…By utilizing the principle of similarity and combining relevant empirical formulas, the similarity criteria for power equipment can be easily derived, which is one of the effective measures to solve the similarity problem of such large-capacity power equipment. However, due to the complex structure of the converter transformer and the existence of a large number of nonlinear problems during operation, the similarity process of the proportional model has a large number of approximate expressions, making it difficult to solve the accuracy problem of the scaled model of the converter transformer [6]. Reference [7] established a mathematical model of dual windings using the multi-conductor transmission line (MTL) method and studied the proportional model voltage distribution of an 800 kV converter transformer under lightning impulse.…”

Section: Introductionmentioning

confidence: 99%

A Convolutional Neural Network Based on Attention Mechanism for Designing Vibration Similarity Models of Converter Transformers

Wang,

Zhang,

Sun

et al. 2023

Machines

Self Cite

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A vibration scale training model for converter transformers is proposed by combining attention modules with convolutional neural networks to solve the nonlinear problem of converter transformers in similar processes. Firstly, according to the structure and operating parameters of the converter transformer, a reliable three-dimensional multi-field coupled finite element model was established considering the influence of the winding and iron core component structure on the overall vibration characteristics. By changing different input parameters such as the size and voltage of the finite element model, corresponding output parameters are obtained, and a dataset is established through data expansion for training and verifying the attention convolution model. By analyzing the prediction processes and results of five prediction models on different operating conditions datasets, it is shown that attention convolution has higher accuracy, faster convergence speed, more stable training process, and better generalization performance in the prediction process of converter transformer recognition. Based on the predictive model, a prototype of the proportional vibration model for the converter transformer with scale factor of 0.2 was designed and manufactured. By analyzing the basic experimental items and vibration characteristics of the prototype, the stability of the prototype and the reliability of the prediction model were verified.

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Section: Introductionmentioning

confidence: 99%

A Convolutional Neural Network Based on Attention Mechanism for Designing Vibration Similarity Models of Converter Transformers

Wang,

Zhang,

Sun

et al. 2023

Machines

Self Cite

View full text Add to dashboard Cite

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Finite Element Simulation and Experimental Study on Vibration Characteristics of Converter Transformer Under DC Bias

Wang,

Zhang,

Sun

et al. 2024

Prot. Control Mod. Power Syst.

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Hydro Generator Stator Bar Modeling and Simulation Based on Finite Element Simulation

Yu,

Cheng

2024

Coatings

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As motor capacity and rated voltage increase, the demand for motor insulation also increases. Additionally, the electric field distribution at the end of a large-scale hydro generator is extremely nonuniform, and corona discharge occurs. This destroys the main insulation, which significantly affects the service life of electrical machinery. The regulation of the electric field concentration at the end of a large-scale hydro generator needs to be addressed. In this manuscript, a large hydro generator with 120 MW rated capacity and 15.75 kV rated voltage was studied. For the purpose of electric field homogenization in the stator bar end, decrease of highest field strength, and surface loss, the computing methods of the electric field in the stator bar end were studied. The electric field distribution in the stator bar end was obtained. According to COMSOL 5.6 modeling software, the hydro generator stator bar is an accurate model. Using finite element analysis (FEA), the stator bar characteristics can be simulated. The different stator bars with no anti-corona structure, linear anti-corona structure, two-layer nonlinear anti-corona structure, and three-layer nonlinear anti-corona structure were compared, including the electric potential, electric field, and loss distribution under rated voltage. From the experimental results, with no anti-corona structure and linear anti-corona structure, the electric field concentration is present in the exit slot. The values of electric field and loss are both higher, which causes corona discharge easily. With the nonlinear anti-corona structure, the electric field concentration is improved significantly, effectively decreasing the highest electric field. The three-layer nonlinear anti-corona structure is the best. Compared with the two-layer nonlinear anti-corona structure, the values of the highest electric field and loss are 16% and 77% lower, respectively.

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Vibration Scale Model of a Converter Transformer Based on the Finite Element and Similarity Principle and Its Preparation

Cited by 4 publications

References 29 publications

A Convolutional Neural Network Based on Attention Mechanism for Designing Vibration Similarity Models of Converter Transformers

A Convolutional Neural Network Based on Attention Mechanism for Designing Vibration Similarity Models of Converter Transformers

Finite Element Simulation and Experimental Study on Vibration Characteristics of Converter Transformer Under DC Bias

Hydro Generator Stator Bar Modeling and Simulation Based on Finite Element Simulation

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