课题基金 / 基金详情

High Frequency Transformer Winding Power Loss Reduction

High Frequency Transformer Winding Power Loss Reduction
减少高频变压器绕组功率损耗
批准号:
1611048
负责人:
Shuo Wang
金额:
$20.0万
依托单位:
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2016
资助国家:
美国
项目状态:
已结题
起止时间:
2016-09-01 至 2020-08-31

项目摘要

项目成果

Shuo Wang的其他基金

相似基金

相关文献

中文摘要
翻译
当电流流过传统的变压器绕组时,电流在绕组内的分布并不均匀。电流集中度高的地方绕组功率损耗大,而电流集中度低的地方绕组没有得到充分利用。电流浓度高,绕组功率损耗大,导致能量转换效率降低,温度高。高温降低了变压器的使用寿命,因此在变压器设计中必须使用更多的绕组来降低功率损耗。因此,变压器体积庞大,重量大,增加了变压器成本,不能满足当今对高度紧凑设计的需求。本研究力求从根本上解决这一问题。研究者初步发现,变压器绕组内的电流分布总是达到最小磁能状态。基于这一发现,利用绕组中最小磁能状态与功率损耗之间的关系,开发了一种新的变压器绕组设计技术。该技术可以使电流比现有技术更均匀地分布在变压器绕组内。该技术可大大降低变压器绕组功率损耗,提高变压器能效,提高变压器可靠性,降低变压器成本,提高变压器功率密度。该技术也可以应用于其他磁性元件。变压器和磁性元件广泛应用于许多电子和电气应用领域,如消费电子产品,工业产品和运输系统。因此,这项技术有望为社会带来重大的科学和经济影响。教育计划教育电气工程专业的学生,促进多样性。本研究的目标是发展一种新的变压器绕组设计技术,以减少变压器绕组的功率损耗。研究人员将研究变压器绕组中的最小磁能状态,探索最小磁能状态与绕组电流分布之间的关系,并开发一种寻找所需绕组电流或磁动势(MMF)分布的技术,以最大限度地减少变压器绕组的功率损耗。首先研究绕组功率损耗与磁能绕组之间的关系。PI先前发现,影响绕组导体内部电流分布和并联绕组导体之间电流分担的趋肤效应和接近效应符合最小磁能理论。研究了高频变压器绕组中最小磁能状态与绕组电流分布或分担的关系。我们将探索一种通过使绕组之间的磁能最小化来实现最小绕组功率损耗的技术。本文将提出一种利用最小磁能模型求解所需MMF剖面的方法。将研究一种将所需的MMF实现为串联或并联绕组匝的技术。在研究活动中发展的理论和技术可以应用于任何变压器和磁性元件设计的许多应用。与传统的广泛有限元分析(FEA)或基于实验的试错绕组设计方法相比,该研究可以揭示高频变压器绕组电流分布和共享的基本原理。该方法应能够快速、直接地找到绕组损耗最小的最佳变压器绕组设计。该方法还可为变压器绕组设计提供有益的指导。该方法不仅有助于设计人员了解变压器绕组内的电流分布和共享,而且还使设计人员能够根据所需的电流共享配置文件来控制变压器绕组内的电流。采用该技术,可以大大降低变压器绕组的功率损耗,大大提高变压器的功率密度。初步研究表明,利用待开发的理论和技术,与传统的全交错绕组结构相比,绕组功率损耗可降低33-41.5%。
英文摘要
When electrical current flows through conventional transformer windings, the current is not evenly distributed within the windings. High winding power losses are generated where the current has high concentration and the winding is not fully utilized where the current has low concentration. High current concentration generates high winding power loss which leads to reduced energy conversion efficiency and high temperature. High temperature reduces transformer lifetime, so more windings must be used in transformer design to reduce power loss. As a result, transformers are bulky and heavy which increases transformer cost and cannot meet today's demand for highly compact designs. This research pursues a fundamental solution to this problem. It has been preliminarily discovered by the investigator that the current distribution within transformer windings always reaches a minimum magnetic energy state. Based on this discovery, a novel transformer winding design technique is being developed using the relationship between the minimum magnetic energy state and power loss in the windings. The technique can make current much more evenly distributed within transformer windings than is possible with existing techniques. The technique can greatly reduce transformer winding power loss, improve transformer energy efficiency, improve transformer reliability, reduce transformer cost and improve transformer power density. The technique can also be applied to other magnetic components. Transformers and magnetic components are widely used in many electronics and electrical application areas such as consumer electronics, industry products and transportation systems. Thus this technique is expected to bring significant scientific and economic impacts to society. The education plan educates electrical engineering students and promotes diversity.The goal of this research is to develop a novel transformer winding design technique to minimize power loss in transformer windings. The researchers will investigate the minimum magnetic energy state in transformer windings, explore the relationship between the minimum magnetic energy state and winding current distribution and develop a technique to find desired winding current or magnetomotive force (MMF) profiles to minimize transformer winding power loss. The relationship between the winding power loss and magnetic energy windings will be first investigated. The PI has previously found that skin and proximity effects which influence the current distribution within winding conductors and the current sharing among parallel winding conductors comply with the minimum magnetic energy theory. The relationship between the minimum magnetic energy state and the winding current distribution or sharing in high frequency transformer windings will be investigated. A technique to achieve the minimum winding power loss by minimizing the magnetic energy among windings will be explored. A method to solve for the desired MMF profile using the minimum magnetic energy model will be developed. A technique to implement the desired MMF to either series or parallel winding turns will be studied. The theory and techniques to be developed in the research activity can be applied to any transformers and to magnetic component design in many applications. Compared with conventional extensive finite element analysis (FEA) or experiment-based trial-and-error winding design methods, the research can reveal the fundamentals of the winding current distribution and sharing in high frequency transformers. The method should be capable of quickly and directly find the best transformer winding design with the lowest winding power loss. The method should also provide useful guidance in transformer winding design. The methodology will not only help designers understand the current distribution and sharing within transformer windings but also give designers capability to steer the currents within transformer windings based on the desired current sharing profiles. With this technique, the transformer's power losses in windings can be greatly reduced and its power density can be greatly improved. Preliminary research shows that compared with conventional fully interleaved winding structure, 33-41.5% winding power loss reduction should be achievable using the theory and techniques to be developed.
期刊论文(0)
专著(0)
科研奖励(0)
会议论文
Adaptive Multi-Source Transfer Learning Approaches for Environmental Challenges
  • 批准号:
    EP/Y002539/1
  • 项目类别:
    Research Grant
  • 资助金额:
    $20.97万
  • 财政年份:
    2024
  • 负责人:
    Shuo Wang
  • 依托单位:
CAREER: A Multi-layer Dynamic Network Control for Agile, Optimized, and Sustainable Supply Chains
  • 批准号:
    2238269
  • 项目类别:
    Continuing Grant
  • 资助金额:
    $50.3万
  • 财政年份:
    2023
  • 负责人:
    Shuo Wang
  • 依托单位:
Advanced Electromagnetic Analysis and High-frequency Impedance Design for Magnetic Ferrite Inductors and Transformers
  • 批准号:
    2322529
  • 项目类别:
    Standard Grant
  • 资助金额:
    $40.0万
  • 财政年份:
    2023
  • 负责人:
    Shuo Wang
  • 依托单位:
Collaborative Research: PPoSS: Planning: S3-IoT: Design and Deployment of Scalable, Secure, and Smart Mission-Critical IoT Systems
  • 批准号:
    2028897
  • 项目类别:
    Standard Grant
  • 资助金额:
    $2.71万
  • 财政年份:
    2020
  • 负责人:
    Shuo Wang
  • 依托单位:
国内基金
海外基金
基于Transformer架构的结核病流行智能预测模型构建及跨病种迁移研究
  • 批准号:
    2026JJ50556
  • 项目类别:
    省市级项目
  • 资助金额:
    --
  • 批准年份:
    2026
  • 负责人:
    周军华
  • 依托单位:
Transformer 和DCE 影像组学联合驱动的脑胶质瘤复发和治疗反应鉴别研究
  • 批准号:
    ZCLQN26F0206
  • 项目类别:
    省市级项目
  • 资助金额:
    --
  • 批准年份:
    2026
  • 负责人:
    雷蕾
  • 依托单位:
基于拓扑增强 Transformer 底层注意力的 LiDAR 点云环境特征语义解码
  • 批准号:
    JCZRMS202602136
  • 项目类别:
    省市级项目
  • 资助金额:
    --
  • 批准年份:
    2026
  • 负责人:
  • 依托单位:
基于“Transformer+”的沿边跨境金融时序多模态融合预测方法与风险预警机制研究
  • 批准号:
    72664059
  • 项目类别:
    地区科学基金项目
  • 资助金额:
    27.00万元
  • 批准年份:
    2026
  • 负责人:
    丁冰清
  • 依托单位: