Transformer Hot-spot Temperature Inversion Based on Multi-physics Calculation and Fuzzy Neural Network Algorithm

LUO Xiaoman; RUAN Jiangjun; DENG Yongqing; DUAN Cihan; GONG Ruohan; LIU Xiangqun

English Version Reader LoginJournal Login

您当前的位置：

首页 >

文章列表页 >

Transformer Hot-spot Temperature Inversion Based on Multi-physics Calculation and Fuzzy Neural Network Algorithm

更新时间：2025-12-08

- Transformer Hot-spot Temperature Inversion Based on Multi-physics Calculation and Fuzzy Neural Network Algorithm
- Vol. 46, Issue 3, Pages: 860-866(2020)
- 作者机构：
  
  武汉大学电气与自动化学院
- 作者简介：
- 基金信息：
- DOI：
  CLC：
- Published Online：31 March 2020，
  
  Published：2020
- 稿件说明：
移动端阅览
LUO Xiaoman, RUAN Jiangjun, DENG Yongqing, et al. Transformer Hot-spot Temperature Inversion Based on Multi-physics Calculation and Fuzzy Neural Network Algorithm[J]. 2020, 46(3): 860-866.
DOI：

LUO Xiaoman, RUAN Jiangjun, DENG Yongqing, et al. Transformer Hot-spot Temperature Inversion Based on Multi-physics Calculation and Fuzzy Neural Network Algorithm[J]. 2020, 46(3): 860-866. DOI：

摘要

热点温度是影响油浸式变压器绝缘寿命的重要因素之一。针对分布式光纤传感器测温系统测量热点温度时造成的不良影响与安装的不便性。提出一种基于多物理场计算和模糊神经网络算法的油浸式变压器热点温度反演模型。以66 kV三相三柱油浸自冷式变压器为例

采用有限体积法耦合迭代求解变压器流体–温度场

确定热点温度范围区域。筛选出环境温度、变压器顶层油温、变压器外壳及油箱顶部等12个特征点的温度建立模糊子集

再通过T–S模糊神经网络算法预测变压器绕组热点温度值。计算结果表明其与光纤传感器实际测量绕组热点温度误差<4%

比传统方法具有更高的精度。该方法实现了特征量测量非植入式

为电力变压器在线监测、热点温度计算提供一种新的计算思路。

Abstract

The hot-spot temperature of oil-immersed transformers is one of the important factors affecting the insulation life of power transformers. The adverse impact and installation inconvenience may be aroused when measuring the hot-spot temperature by the distributed optical fiber sensor temperature measurement system

therefore

we proposed an oil-immersed transformer hot spot temperature prediction model based on fuzzy neural network algorithm. Taking 66 kV three-phase three-column oil-immersed self-cooled transformer as an example

we adopted the finite volume method to iteratively solve the transformer fluid-temperature field and determine the hot spot temperature range. The fuzzy subsets of 12 characteristic points such as ambient temperature

transformer top oil temperature

transformer casing and fuel tank top were screened

and the temperature of the transformer winding hot spot was predicted by the T-S fuzzy neural network algorithm. The results show that the temperature error of the winding hot spot measured with the fiber optic sensor is within 4% and it has higher precision than traditional method. This method realizes the non-implantable characteristic measurement and provides a new method for the calculation of hot spot temperature in power transformer on-line monitoring.

关键词

Keywords

references

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Simulation of Multi-physical Field of 10 kV Oil-immersed Transformer Based on Adaptive Grid Control

油浸式变压器绕组热点动态温升演化规律及推演计算模型研究

Prediction of Winding Temperature in Power Transformers Based on Fluid Network

Two-dimensional Temperature Field Analysis of Oil-immersed Transformer Based on Non-uniformly Heat Source

Thermal Circuit Model of the Transformer Considering Environment Factors and Its Application

Related Author

DONG Xuzhu

WU Yongcong

ZHANG Chen

CHEN Qiaofeng

LIAO Ruijin

XIA Ruochun

XU Jing

HAO Jian

Related Institution

输变电装备技术全国重点实验室(重庆大学电气工程学院)

School of Electrical and Electronic Engineering, Harbin University of Science and Technology

Harbin Transformer Co.,Ltd.

Hebei Provincial Key Laboratory of Power Transmission Equipment Security Defense, North China Electric Power University

TBEA Shengyang Transformer Co.,Ltd.

AI问答

⁰