Research on optimal receiver radius of wireless power transfer system based on BP neural network

Wu, Feng; Jing, Fansheng; Zhao, Wenhan; Chu, Zhoujian; Li, Qiang; Chu, Xiaohu; Zhu, Xueqiong

doi:10.1016/j.egyr.2020.11.003

Cited by 15 publications

(5 citation statements)

References 2 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…ω ij and ω kj are the hidden layer and output layer weight, θ i and α k are the hidden layer and output layer thresholds, respectively. Its node element characteristic (transfer function) is Sigmoid type [ 30 ].…”

Section: The Proposed Modelmentioning

confidence: 99%

[Retracted] Prediction Model of Hot Metal Silicon Content Based on Improved GA‐BPNN

Cui

Han

et al. 2021

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

The inconsistency of the detection period of blast furnace data and the large time delay of key parameters make the prediction of the hot metal silicon content face huge challenges. Aiming at the problem that the hot metal silicon content is not consistent with the detection period of time series of multiple control parameters, the cubic spline interpolation fitting model was used to realize the data integration of multiple detection periods. The large time delay of the blast furnace iron making process was analyzed. Moreover, Spearman analysis was combined with the weighted moving average method to optimize the data set of silicon content prediction. Aiming at the problem of low prediction accuracy of the ordinary neural network model, genetic algorithm was used to optimize parameters on the BP neural network model to improve the convergence speed of the model to achieve global optimization. Combined with the autocorrelation analysis of the hot metal silicon content, a modified model for the prediction of hot metal silicon content based on error analysis was proposed to further improve the accuracy of the prediction. The model comprehensively considers problems such as data detection inconsistency, large time delay, and inaccuracy of prediction results. Its average absolute error is 0.05009, which can be used in actual production.

show abstract

Section: The Proposed Modelmentioning

confidence: 99%

[Retracted] Prediction Model of Hot Metal Silicon Content Based on Improved GA‐BPNN

Cui

Han

et al. 2021

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

show abstract

“…In the result, the optimizer gave a large inner radius R 0 (as shown in Figure 6 and Table 1) in the optimized coils, and that large value improved the coupling coefficient and the mutual inductance between the transmitter and the receiver coils, as investigated in [15,22], where the magnetic flux between the coils was at its maximum. The idea of a quasi-elliptic shape was borrowed from [23], which contributes to giving high flexibility to create any arbitrary geometries between the circular and rectangular (or square) shapes.…”

Section: Parameter Valuementioning

confidence: 87%

An Efficient Design of Inductive Transmitter and Receiver Coils for Wireless Power Transmission

et al. 2023

View full text Add to dashboard Cite

Wireless power transmission (WPT) is commonly used today in many important daily applications, such as electric vehicles, mobile phones, and implanted medical devices. The transmitter and receiver coils are essential elements in the WPT system, and the coupling coefficient between these coils plays an important role in increasing the power transfer efficiency. In this work, we introduce a new approach to optimizing the coupling coefficient between the transmitter and the receiver coils by changing the geometries and locations of the coil turns. In the optimization process, the geometry of the turns varies from a rhombus to a circular and then a rectangular shape according to a quasi-elliptical parameter value. The Neuman formula is used to calculate the self-inductance, mutual inductance, and coupling coefficient for each specific geometry and turn location. The configuration with the highest coupling coefficient is then selected at the end of the optimization process. The final WPT coils are tested and verified using Ansys software through electromagnetic and AC analysis simulations. The results show that the new approach could achieve smooth and easily manufacturable coils with higher coupling coefficients, thereby increasing the power transfer efficiency of WPT.

show abstract

“…Additionally, magnetic materials can increase the efficiency of energy transfer by enhancing the The magnetic losses in IM windings can be decreased with the use of magnetic materials, drawn in Figure 5 Eddy current losses in conducting materials and hysteresis losses in magnetic materials are only two examples of the causes of these losses. By containing and directing the magnetic flux along a specified route, magnetic materials with high magnetic permeability, such as ferrite cores, can assist in minimizing these losses and increase system effectiveness [29]. Table 4 are not better than the results in Table 1, Table 2, and Table 3 since it has a little reduction in its efficiency.…”

Section: A Case 1: Normal Windingmentioning

confidence: 99%

Enhancing efficiency of magnetic energy by implementing square-shaped materials adjacent to induction machine windings

Habibi,

Mustika,

Aripriharta

et al. 2023

J. Mechatron. Electr. Power Veh. Technol.

View full text Add to dashboard Cite

This study provides a worthwhile method for increasing the magnetic field energy and induction machine (IM) effectiveness. The coupling between the transmitter and receiver windings in the IM system can be improved by creating materials with specific electromagnetic properties. This added material has altered the magnetic flow as well as the energy of the magnetic field. Eventually, it is possible to calculate the efficiency of the magnetic field, or the ratio of primary to secondary magnetic energy. With the use of two-dimensional finite element analysis, numerical results on five cases with various configurations of a magnetic substance have been produced. This material, which varies in length or breadth, is positioned close to the windings of the transmitter, receiver, or both. Case 3, in which the transmitter generates a magnetic field on the receiver side with a minimum energy of 0.05 J and a maximum energy of 0.015 J, is the ideal material configuration for DC current. Currently, the system efficiency is 0.29 on average. A 1 kHz transmitter's energy is constant under all conditions, but its counterpart's energy fluctuates significantly, with case 5 receiving the most energy. Therefore, case 5 turns into the optimal structural arrangement. It can be inferred that case 5 similarly dominates the other with an efficiency of 0.0026, which is much greater than that of 1 kHz efficiency, while the windings are operating at 1 MHz. This leads to stronger magnetic field coupling and increased power transfer effectiveness.

show abstract

Research on optimal receiver radius of wireless power transfer system based on BP neural network

Cited by 15 publications

References 2 publications

[Retracted] Prediction Model of Hot Metal Silicon Content Based on Improved GA‐BPNN

[Retracted] Prediction Model of Hot Metal Silicon Content Based on Improved GA‐BPNN

An Efficient Design of Inductive Transmitter and Receiver Coils for Wireless Power Transmission

Enhancing efficiency of magnetic energy by implementing square-shaped materials adjacent to induction machine windings

Contact Info

Product

Resources

About