基于GA优化BP神经网络预测开关柜内部设备温度

桑仲庆; 袁会生; 游一民; 戴冬云; 周顺雄; 廖权昌; 姜维云

doi:10.13296/j.1001-1609.hva.2026.08.005

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基于GA优化BP神经网络预测开关柜内部设备温度

研究与分析 | 更新时间：2026-08-12

- 基于GA优化BP神经网络预测开关柜内部设备温度
- Prediction of Temperature Inside Switchgear Cabinet Based on GA Optimized BP Neural Network
- 高压电器 2026年62卷第8期页码：34-42
- 作者机构：
  
  厦门理工学院电气工程与自动化学院，福建厦门 361024
  厦门市高端电力装备及智能控制重点实验室，福建厦门 361006
- 作者简介：
  
  桑仲庆（1977—），男，高级工程师，研究生导师，主要从事电气设备一二次融合研究（E-mail：1668422809@qq.com）。
  袁会生（1996—），男，硕士研究生，主要从事高压电器在线监测及故障诊断研究（通信作者）（E-mail：1010917287@qq.com）。
- 基金信息：
- DOI：10.13296/j.1001-1609.hva.2026.08.005
  中图分类号：
- 收稿：2025-12-16，
  
  修回：2026-02-13，
  
  纸质出版：2026-08-16
- 稿件说明：
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桑仲庆, 袁会生, 游一民, 等. 基于GA优化BP神经网络预测开关柜内部设备温度[J]. 高压电器, 2026,62(8):34-42.

SANG Zhongqing, YUAN Huisheng, YOU Yimin, et al. Prediction of Temperature Inside Switchgear Cabinet Based on GA Optimized BP Neural Network[J]. High Voltage Apparatus, 2026, 62(8): 34-42.
桑仲庆, 袁会生, 游一民, 等. 基于GA优化BP神经网络预测开关柜内部设备温度[J]. 高压电器, 2026,62(8):34-42. DOI： 10.13296/j.1001-1609.hva.2026.08.005.

SANG Zhongqing, YUAN Huisheng, YOU Yimin, et al. Prediction of Temperature Inside Switchgear Cabinet Based on GA Optimized BP Neural Network[J]. High Voltage Apparatus, 2026, 62(8): 34-42. DOI： 10.13296/j.1001-1609.hva.2026.08.005.

摘要

由于开关柜通入电流后，内部设备会产生热量，当温度长期超出阈值，会造成设备损坏无法保证安全，因此需要对开关柜内部温度进行监测，提前对柜内设备的温度进行预测，方便对设备进行维护。为了能够准确预测开关柜内部设备温度，基于BP神经网络，采用GA算法对BP神经网络优化，提出GA-BP神经网络开关柜内部设备温度预测模型。首先分析影响开关柜温度上升的影响因素，并将其作为预测模型的输入数据；再通过预测模型的训练与测试；最后通过衡量指标来评价网络模型的优劣。测试结果表明，该方法能够有效预测开关柜内部设备的温度值，为变电站内设备进行维护提供了便利。

Abstract

When current flows through the switchgear cabinet

its internal components generate heat. Prolonged exposure to temperature above the threshod can damage the equipment and pose safety risk. Threfore

internal temperature monitoring and advance temperature prediction are required to enable timely maintenance. In order to accurately predict the temperature of the internal components within the switchgear cabinet a temperature prediction model for internal components of the switchgear cabinet based on the BP neural network is proposed

where the GA algorithm is used to optimize the BP neural network. First

the influencing factors affecting the temperature rise of the switchgear cabinet are analyzed and used as the input data of the prediction model. Then

the prediction model is trained and tested.Finally

the pros and cons of the network model are evaluated through measurement indicators. The test results show that this method can effectively predict the temperature value of the components within the switchgear cabinet

providing convenience for the maintenance of equipment in the substation.

关键词

Keywords

references

黎才添.变电站设备无线温度监测系统的研究与应用[D].广州:华南理工大学,2015.

Li Caitian. Research and application of wireless temperature monitoring system for substation equipment[D]. Guangzhou: South China University of Technology,2015.

阮江军,黎鹏,黄道春,等.中压开关柜内部短路燃弧热—力效应研究综述[J].高电压技术,2018,44(10):3340-3351.

Ruan Jiangjun, Li Peng, Huang Daochun, et al. Review on thermalmechanical effects research of MV switchgear due to internal short circuit arcing[J]. High Voltage Engineering,2018,44(10):3340-3351.

田超华,赵欢,黄鸿基,等.基于深度学习的配电网开关柜电晕放电检测设计[J]. 电子设计工程,2025,33(1):51-54.

Tian Chaohua, Zhao Huan, Huang Hongji, et al. Design of corona discharge detection for distribution network switchgear based on deep learning[J]. Electronic Design Engineering ,2025 ,33(1):51-54.

李刚毅. 基于多传感信息融合的开关柜局放类型诊断研究[J].电器与能效管理技术,2025(1):23-27.

Li Gangyi. Research on partial discharge type diagnosis of switchgear based on multi-sensor information fusion[J]. Electrical Appliances and Energy Efficiency Management Technology,2025 (1):23-27.

王文华.高压开关柜局部放电故障诊断分析方法[J].无损检测, 2018,40(11):75-79.

Wang Wenhua. Analytical method in partial discharge fault diagnosis of high-voltage switchgear[J]. Non-Destructive Testing, 2018,40(11):75-79.

王真,刘子全,路永玲,等.基于RFID传感器和深度学习的开关柜故障诊断研究[J]. 电力科学与技术学报,2025,40(2):179-185.

Wang Zhen, Liu Ziquan, Lu Yongling, et al. Fault detection in switchgear based on RFID sensors and deep learning[J]. Journal of Electric Power Science and Technology,2025,40(2):179-185.

方江华,谢堪恒,贺平,等.基于多传感器融合与机器学习的开关柜潜伏性故障检测方法[J]. 电子设计工程,2025,33 (18):26-30.

Fang Jianghua, Xie Kanheng, He Ping, et al. Latent fault detection method for switchgear based on multi-sensor fusion and machine learning[J]. Electronic Design Engineering,2025,33(18):26-30.

盛连军,周超杰,陈侃,等.基于I-GWO改进RF的开关柜状态诊断算法[J].智慧电力,2025,53(1):38-44.

Sheng Lianjun, Zhou Chaojie, Chen Kan, et al. Switchgear condition diagnosis algorithm based on I-GWO improved random forest[J]. Smart Power,2025,53(1):38-44.

陈凯彬,耿新,郭营生,等.基于ZigBee和CAN总线的高压开关柜温度在线监测预警系统设计[J].科技风,2020(10):3.

Chen Kaibin, Geng Xin, Guo Yingsheng, et al. Design of on-line temperature monitoring and early warning system for high voltage switchgear based on ZigBee and CAN bus[J]. Technology Wind, 2020(10):3.

王浩,白张,吴劲松,等.高海拔地区中压充气式开关柜温升特性研究[J].电测与仪表,2025,62(11):88-94.

Wang Hao, Bai Zhang, Wu Jinsong, et al. Research on temperature rise characteristics of medium voltage gas-filledswitchgear in high altitude areas[J]. Electrical Measurement & Instrumentation, 2025,62(11):88-94.

吴小忠,甘星,严亚兵,等.基于过热故障仿真的高压开关柜测温点优化方法[J]. 西安工程大学学报,2025,39(6):128-138.

Wu Xiaozhong, Gan Xing, Yan Yabing, et al. Optimization method of high-voltage switchgear temperature measurement points based on overheating fault simulation[J]. Journal of Xi'an Polytechnic University,2025,39(6):128-138.

康晶,任召田,刘振笃,等. 环保气体绝缘开关柜电场仿真与优化[J]. 电器与能效管理技术,2025(1):56-60.

Kang Jing, Ren Zhaotian, Liu Zhendu, et al. Simulation and optimization on electric field of environment-friendly gas insulated switchgear[J]. Electrical Appliances and Energy Efficiency Management Technology,2025(1):56-60.

林奕夫,叶兆平,陈雪,等.基于二阶电路的开关柜局部放电检测方法[J]. 浙江电力,2024,43(8):113-120.

Lin Yifu, Ye Zhaoping, Chen Xue, et al. A partial discharge detection method for switch cabinets based on a second-order circuit[J]. Zhejiang Electric Power,2024,43(8):113-120.

薛禹胜,郁琛,赵俊华,等.关于短期及超短期风电功率预测的评述[J].电力系统自动化,2015(6):141-151.

Xue Yusheng, Yu Chen, Zhao Junhua, et al. A review on shortterm and ultra-short-term wind power prediction[J]. Automation of Electric Power Systems,2015(6):141-151.

杨茂,张罗宾.基于数据驱动的超短期风电功率预测综述[J].电力系统保护与控制,2019,47(13):171-186.

Yang Mao, Zhang Luobin. Review on ultra-short term wind power forecasting based on data - driven approach[J]. Power System Protection and Control,2019,47(13):171-186.

王晓丹.基于深度学习的风力发电机故障预测研究[D].重庆:重庆工商大学,2019.

Wang Xiaodan. Research on wind turbine fault prediction based on deep learning[D]. Chongqing: Chongqing Technology and Business University,2019.

Bai X, An Z, Hou Y, et al. Health assessment and management of wind turbine blade based on the fatiguetest data[J]. Microelectronics Reliability,2017(75):205-214.

于春蕾,李梦悦,尹伟石.基于PCA-BP组合模型的股价预测分析[J].长春理工大学学报(自然科学版),2021,44(4):125-130.

Yu Chunlei, Li Mengyue, Yin Weishi. Analysis of stock price forecast based on PCA-BP combination model[J]. Journal of Changchun University of Science and Technology(Natural Science Edition),2021,44(4):125-130.

冯波,张瑞,孙冲,等.基于序列理论的电网用户用电负荷自动预测研究[J].自动化技术与应用,2023,42(2):17-20.

Feng Bo, Zhang Rui, Sun Chong, et al. Research on automatic power load prediction of power grid users based on sequence theory[J]. Techniques of Automation and Applications,2023,42(2):17-20.

马创涛,邵景峰.烟花算法改进BP神经网络预测模型及其应用[J].控制工程,2020,27(8):1324-1331.

Ma Chuangtao, Shao Jingfeng. Fireworks algorithm improved BP neural network forecasting model and its application[J]. Journal of Control Engineering,2020,27(8):1324-1331.

王英立,陶帅,候晓晓,等.基于MIV分析的GA-BP神经网络光伏短期发电预测[J].太阳能学报,2020,41(8):236-242.

Wang Yingli, Tao Shuai, Hou Xiaoxiao, et al. GA-BP neural network photovoltaic power generation short-term forecast based on MIV analysis[J]. Acta Energiae Solaris Sinica,2020,41(8):236-242.

游佳凝,高玲,王庆.基于GA-BP神经网络的颗粒阻尼器减振特性预测[J].福建师范大学学报(自然科学版),2023,39(3):106-115.

You Jianing, Gao Ling, Wang Qing. Prediction of vibration damping characteristics with particle dampers based on GA-BP neural network[J]. Journal of Fujian Normal University(Natural Science Edition),2023,39(3):106-115.

黄杨森.基于BP神经网络的转子振动预警方法研究[J].发电设备,2023,37(3):186-191.

Huang Yangsen. Research on early warning method for rotor vibration based on BP neural network[J]. Power Equipment,2023, 37(3):186-191.

李炘,吴金颖,苏慧敏,等.基于BP神经网络的手部尺寸预测模型构建[J].丝绸,2023,60(5):59-65.

Li Xin, Wu Jinying, Su Huimin, et al. Construction of hand size prediction models based on BP neural network[J]. Silk,2023,60 (5):59-65.

黄敬尧,程煜,李雅恬.基于DCM-PCA和GA-BP的逆变器故障诊断[J].电力科学与技术学报,2024,39(1):260-271.

Huang Jingyao, Cheng Yu, Li Yatian. Fault diagnosis of inverter based on DCM-PCA and GA-BP neural network[J]. Journal of Electric Power Science and Technology,2024,39(1):260-271.

墨蒙,赵龙章,龚嫒雯,等.基于遗传算法优化的BP神经网络研究应用[J].现代电子技术,2018,41(9):41-44.

Mo Meng, Zhao Longzhang, Gong Aiwen, et al. Research and application of BP neural network based on genetic algorithm optimization[J]. Modern Electronics Technique,2018,41(9):41-44.

邹春玲,熊静,刘超,等.基于遗传算法优化BP神经网络的飞机油耗预测方法[J].智能计算机与应用,2023,13(3):226-230.

Zou Chunling, Xiong Jing, Liu Chao, et al. Aircraft fuel consumption prediction method based on BP neural network optimized by genetic algorithm[J]. Intelligent Computer and Applications,2023, 13(3):226-230.

王团结,赵赛,郑思嘉.应用BP神经网络的人脸识别系统[J].福建电脑,2020,36(7):27-29.

Wang Tuanjie, Zhao Sai, Zheng Sijia. Face recognition system based on BP neural network[J]. Fujian Computer ,2020,36(7):27-29.

何汶静,赵毅.电气光纤光栅测温火灾预警技术[J].消防科学与技术,2011,30(8):711-714.

He Wenjing, Zhao Yi. Bragg fiber gratings temperature monitoring system for warning of electric fire[J]. Fire Science and Technology, 2011,30(8):711-714.

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