Transformer optimization system design based on deep learning and evolutionary algorithm

He, Hongjia; Li, Peipei; Zhou, Hang; Jiang, Yiyi

doi:10.1088/1742-6596/1827/1/012084

Cited by 3 publications

(1 citation statement)

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“…Define the model output from the scene model and business model output and get a 3D scene that matches the actual business model. For design clarification, refer to the final EHF data for relevant design information [13,14].…”

Section: Optimization Of Visual Features In Industrial Designmentioning

confidence: 99%

Application of Deep Learning Algorithm in Visual Optimization of Industrial Design

Zhang

2024

SCPE

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In order to understand the application of degree learning algorithms in industrial design visual optimization, the author proposes an application research based on deep learning algorithms in industrial design visual optimization. The author first starts with the basic principles of deep learning algorithms and provides a detailed explanation of the basic structure of the single-layer network of deep learning algorithms, as well as the restricted Boltzmann machine and its training process. Finally, an example of the performance improvement brought by the application of deep learning technology in handwritten digit recognition was given through an automatic encoder, and a simple summary of deep learning technology was made. Secondly, the NCI matching algorithm is used to match industrial design products and reconstruct the point cloud of industrial products to accurately detect their features. On this basis, deep learning algorithms are applied to construct a visual optimization model for industrial design, determine the output format of the model scene and the output situation of industrial design, and process the model according to the changing characteristics of the industrial design model to comprehensively edit the data of the model. Finally, targeted optimization of the industrial design visual optimization model is carried out based on the technical characteristics of deep learning algorithms, in order to complete the industrial design visual optimization. The experimental comparison results show that the industrial design visual optimization method optimized in this design has higher visual clarity than the traditional industrial design method, reaching 98%. Greatly improves the visual clarity of industrial design.

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Section: Optimization Of Visual Features In Industrial Designmentioning

confidence: 99%

Application of Deep Learning Algorithm in Visual Optimization of Industrial Design

Zhang

2024

SCPE

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Process Analysis of Depression Expression and Action Changes and Their Psychological Integration Based on Deep Learning Algorithms

Zhao

2024

Lecture Notes in Networks and Systems

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Optimization of Transformer Windings Based on FEA‐XGBoost and NSGA‐III Algorithm

Bai-di,

Yong-feng,

Jing-yu

et al. 2024

Journal of Electrical and Computer Engineering

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Power transformers are indispensable components for energy transmission and voltage regulation. Since the leakage impedance affects the short‐circuit current, magnetic leakage distribution, and manufacturing cost, the accurate calculation of transformer impedance is vital for transformer design. Generally, leakage impedance is mainly decided by the design parameters of the windings, both analytically and numerically. In most given literature studies, the leakage impedance was optimized and analyzed by adjusting the design parameters of the windings without considering the consequent influence on transformer loss and manufacturing costs. A multiobjective optimization model considering the leakage impedance, manufacturing cost, and operating loss of the windings is presented in this paper. The eXtreme Gradient Boosting (XGBoost) model is built using 2048 finite‐element analysis (FEA) samples and utilized as a leakage impedance predictor. XGBoost shows better accuracy and consumes much less time compared to analytical and FEA methods. Subsequently, the presented multiobjective model is optimized using multiobjective algorithms and the NSGA‐III shows the best performance among the NSGA‐II, MOPSO, and MODE. The results show that the leakage impedance is closer to the required value. Besides, the winding manufacturing cost and loss are meanly decreased by 4.7% and 4.1%, respectively, in the engineering case.

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Transformer optimization system design based on deep learning and evolutionary algorithm

Cited by 3 publications

References 0 publications

Application of Deep Learning Algorithm in Visual Optimization of Industrial Design

Application of Deep Learning Algorithm in Visual Optimization of Industrial Design

Process Analysis of Depression Expression and Action Changes and Their Psychological Integration Based on Deep Learning Algorithms

Optimization of Transformer Windings Based on FEA‐XGBoost and NSGA‐III Algorithm

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