基于量子粒子群的双向电子变压器输出端谐振参数优化

江友华; 帅禄玮; 吴琦娜; 曹以龙

doi:10.7500/AEPS20191106001

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基于量子粒子群的双向电子变压器输出端谐振参数优化

研制与开发 | 更新时间：2025-06-16

- 基于量子粒子群的双向电子变压器输出端谐振参数优化
- Optimization of Resonance Parameter for Output Port of Bi-directional Electronic Transformer Based on Quantum Particle Swarm
- 电力系统自动化-方正平台 2020年44卷第11期页码：171-180
- 作者机构：
  
  1.上海电力大学电子与信息工程学院，上海市 200090
  2.国网上海市电力公司市南供电公司，上海市 200233
- 作者简介：
  
  江友华(1974—)，男，博士，教授，主要研究方向：谐波治理、电能质量优化及电力系统自动化。E-mail：jyhua0306@sina.com
  帅禄玮(1995—)，男，通信作者，硕士研究生，主要研究方向：电力电子与电动汽车。E-mail：2216135201 @qq.com
  吴琦娜(1994—)，女，硕士，主要研究方向：电能质量优化及电力系统自动化。E-mail：444255912@qq.com
- 基金信息：
  
  上海市科技创新行动计划资助项目(19DZ1205402);国家自然科学基金资助项目(61202369)
- DOI：10.7500/AEPS20191106001
  中图分类号：
- 收稿日期：2019-11-06，
  
  修回日期：2020-02-07，
  
  网络出版日期：2020-04-13，
  
  纸质出版日期：2020-06-10
- 稿件说明：
移动端阅览
江友华,帅禄玮,吴琦娜等.基于量子粒子群的双向电子变压器输出端谐振参数优化[J].电力系统自动化,2020,44(11):171-180.

JIANG Youhua,SHUAI Luwei,WU Qina,et al.Optimization of Resonance Parameter for Output Port of Bi-directional Electronic Transformer Based on Quantum Particle Swarm[J].Automation of Electric Power Systems,2020,44(11):171-180.
江友华,帅禄玮,吴琦娜等.基于量子粒子群的双向电子变压器输出端谐振参数优化[J].电力系统自动化,2020,44(11):171-180. DOI： 10.7500/AEPS20191106001.

JIANG Youhua,SHUAI Luwei,WU Qina,et al.Optimization of Resonance Parameter for Output Port of Bi-directional Electronic Transformer Based on Quantum Particle Swarm[J].Automation of Electric Power Systems,2020,44(11):171-180. DOI： 10.7500/AEPS20191106001.

摘要

为了降低双向电子变压器输出端的DC-DC变换电路中的开关损耗，目前广泛采用的是电容-电感-电感-电容（CLLC）谐振电路。其中谐振参数的选取直接影响电子变压器的输出性能。为此，引入量子粒子群优化(QPSO)算法对谐振参数进行智能优化设计。该算法能够对存在不可导点的目标函数进行优化，通过引入量波动方程，增加了粒子搜索能力，解决了粒子群优化（PSO）算法局部收敛等问题。其优化过程首先利用谐振点分区法对变换器的参数进行建模，然后分析和研究谐振电感、励磁电感与谐振网络直流增益、输入阻抗角以及相对损耗率的直接关系，得到参数优化的约束条件。在此基础上，利用QPSO算法得到电路相对损耗最小的谐振网络参数。最后，以CLLC谐振变换电路为例，通过实验验证了在宽输入电压下、全负载范围内，该优化设计方法与传统基波分析法与时域法相比，效率均提高了1%左右，样机最高效率达97%，从而证明了所提优化方法的有效性。

Abstract

In order to reduce the switching loss of DC-DC converter at the output port of bi-directional electronic transformer

a capacitor-inductor-inductor-capacitor （CLLC） resonant circuit is widely used. One of the problems is the selection of resonance parameters

which will directly affect the output performance of electronic transformer. A quantum particle swarm optimization (QPSO) algorithm is introduced to implement the intelligent optimization design of resonance parameters. The algorithm can optimize the objective function with non-differentiable points

and the quantity wave equation is used to enhance the particle search ability and to solve the local convergence problem of particle swarm optimization (PSO) algorithm. In the optimization process， a resonance point partition method is applied to establish the mathematical model of DC-DC converter

and the relationship between resonance parameters and the characteristics of the resonance network including the DC gain

input impedance angle and relative loss rate are analyzed to obtain the constraints of parameter optimization. On this basis

QPSO is used to obtain the resonance network parameters of the relative minimum loss of the circuit. Finally

taking CLLC resonance converter as an example

it is verified by experiments that in the wide-input-voltage and full-load-range conditions

the efficiency of the optimized design method based on QPSO algorithm is about 1% higher than that based on the traditional fundamental analysis (FHA) method and time domain method

and the highest efficiency of prototype reaches 97%. Thus the effectiveness of the optimization method proposed is proven.

关键词

Keywords

references

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