|本期目录/Table of Contents|

[1]谢汶含,蒋永清,孙大伟,等.基于数字孪生及GA-BP神经网络的开关柜温升风险预测*[J].中国安全生产科学技术,2025,21(2):184-190.[doi:10.11731/j.issn.1673-193x.2025.02.024]
　XIE Wenhan,JIANG Yongqing,SUN Dawei,et al.Risk prediction on temperature rise of switchgear based on digital twin and GA-BP neural network[J].JOURNAL OF SAFETY SCIENCE AND TECHNOLOGY,2025,21(2):184-190.[doi:10.11731/j.issn.1673-193x.2025.02.024]
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基于数字孪生及GA-BP神经网络的开关柜温升风险预测*
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《中国安全生产科学技术》[ISSN:1673-193X/CN:11-5335/TB]

卷:
21
期数:
2025年2期
页码:
184-190
栏目:
职业安全卫生管理与技术
出版日期:
2025-02-28

文章信息/Info

Title:
Risk prediction on temperature rise of switchgear based on digital twin and GA-BP neural network
文章编号:
1673-193X(2025)-02-0184-07
作者:
谢汶含; 蒋永清; 孙大伟; 王志伟; 孙超
（1.哈尔滨理工大学 测控技术与通信工程学院，黑龙江 哈尔滨 150080；
2.黑龙江省新产业投资集团有限公司，黑龙江 哈尔滨 150090；
3.黑龙江辰能清洁能源有限公司，黑龙江 哈尔滨 150006）
Author(s):
XIE Wenhan;  JIANG Yongqing;  SUN Dawei;  WANG Zhiwei;  SUN Chao
(1.School of Measurement and Communication Engineering,Harbin University of Science and Technology,Harbin Heilongjiang 150080,China;
2.Heilongjiang New Industry Investment Group Co.,Ltd.,Harbin Heilongjiang 150090,China;
3.Heilongjiang Chenneng Clean Energy Co.,Ltd.,Harbin Heilongjiang 150006,China)
关键词:
开关柜; 温升; 风险预测; 数字孪生; GA-BP神经网络
Keywords:
switchgear;  temperature rise;  risk prediction;  digital twin;  GA-BP neural network
分类号:
X913
DOI:
10.11731/j.issn.1673-193x.2025.02.024
文献标志码:
A
摘要:
风电机组开关柜是风电场的重要电力设备之一，为保障开关柜的稳定运行和风电机组的安全，针对开关柜内部器件温升异常问题进行研究。采用数字孪生技术对开关柜温升状态进行数字化建模，设计开关柜数字孪生架构模型，在不同条件下仿真开关柜触头温升，通过GA-BP神经网络对温升数据进行训练学习，实现触头温升异常风险预测。研究结果表明：数字孪生体可再现物理开关柜运行的全部温度数据，通过GA-BP网络模型预测开关柜温升风险平均绝对百分比误差为0.03%，可实现温升风险准确预测，避免开关柜因温升过高而导致热故障发生。
Abstract:
The switchgear of wind turbine is one of the important power equipment of wind farm.In order to ensure the stable operation of switchgear and the safety of wind turbine,the abnormal temperature rise of internal devices of the switchgear was studied.The digital twin technology was used to digitally model the temperature rise state of the switchgear,and the digital twin architecture model of the switchgear was designed.The temperature rise of contact head in the switchgear was simulated under different conditions,and the temperature rise data was trained and learned by GA-BP neural network to realize the risk prediction on the abnormal temperature rise of contact head.The results show that the digital twin body can reproduce all the temperature data of the physical switchgear operation.The average absolute percentage error of temperature rise risk of the switchgear predicted by the GA-BP network model is 0.03%,which can accurately predict the temperature rise risk and avoid the thermal failure of the switchgear due to excessive temperature rise.

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备注/Memo

备注/Memo:
收稿日期： 2024-05-10
* 基金项目： 国家自然科学基金项目(11704090)
作者简介： 谢汶含，硕士研究生，主要研究方向为智慧安全。
通信作者： 蒋永清，硕士，教授，主要研究方向为工业安全、能源安全。
更新日期/Last Update: 2025-03-04