Fast and Robust Parameter Identification Method for Jiles-Atherton Hysteresis Model of Transformer Core

LIU Liqing; HE Siming; FENG Junji; FANG Qiong; GUAN Yonggang; ZOU Jun

doi:10.13296/j.1001-1609.hva.2020.01.009

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Fast and Robust Parameter Identification Method for Jiles-Atherton Hysteresis Model of Transformer Core

更新时间：2025-04-26

- Fast and Robust Parameter Identification Method for Jiles-Atherton Hysteresis Model of Transformer Core
- High VoltageApparatus Vol. 56, Issue 1, Pages: 55-60(2020)
- 作者机构：
  
  1. 国网天津市电力公司电力科学研究院
  2. 清华大学电机工程与应用电子技术系
  3. 国网天津市电力公司设备管理部
- 作者简介：
- 基金信息：
- DOI：10.13296/j.1001-1609.hva.2020.01.009
  CLC： TM41
- Published：2020
- 稿件说明：
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[1]刘力卿,何思名,冯军基,方琼,关永刚,邹军.一种快速高鲁棒性变压器铁心Jiles-Atherton磁滞模型参数辨识方法[J].高压电器,2020,56(01):55-60.

LIU Liqing, HE Siming, FENG Junji, et al. Fast and Robust Parameter Identification Method for Jiles-Atherton Hysteresis Model of Transformer Core[J]. High voltageapparatus, 2020, 56(1): 55-60.
[1]刘力卿,何思名,冯军基,方琼,关永刚,邹军.一种快速高鲁棒性变压器铁心Jiles-Atherton磁滞模型参数辨识方法[J].高压电器,2020,56(01):55-60. DOI： 10.13296/j.1001-1609.hva.2020.01.009.

LIU Liqing, HE Siming, FENG Junji, et al. Fast and Robust Parameter Identification Method for Jiles-Atherton Hysteresis Model of Transformer Core[J]. High voltageapparatus, 2020, 56(1): 55-60. DOI： 10.13296/j.1001-1609.hva.2020.01.009.

摘要

Jiles-Atherton（J-A）模型是变压器剩磁与励磁涌流计算的重要方法

但在使用中存在着参数辨识的难题。文中从参数辨识的公式法、遗传算法、Nelder-Mead（NM）单纯形算法原理及各自特点出发

提出一种综合运用公式法和两种优化算法对实测磁化曲线进行参数辨识的方法。算例分析结果表明

该方法对于经典JA模型参数识别具有较高的精度

在误差影响下体现出良好的鲁棒性

是一种快速、有效的J-A模型参数辨识方法。

Abstract

The J-A model is an important method for calculating the residual flux and inrush current of transformers

but there is a problem of parameter identification in use. This paper proposes a method of comprehensively applying the formula method

genetic algorithm and Nelder-Mead method for parameter identification

based on the principles and characteristics of these methods. The results of the example analysis show that the method has high precision for the parameter identification of classical J-A model

and it has good robustness under the influence of error as a fast and effective J-A model parameter identification method.

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