Prediction of Temperature Inside Switchgear Cabinet Based on GA Optimized BP Neural Network

SANG Zhongqing; YUAN Huisheng; YOU Yimin; DAI Dongyun; ZHOU Shunxiong; LIAO Quanchang; JIANG Weiyun

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

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Prediction of Temperature Inside Switchgear Cabinet Based on GA Optimized BP Neural Network

Research & Analysis | 更新时间：2026-08-12

- Prediction of Temperature Inside Switchgear Cabinet Based on GA Optimized BP Neural Network
- High Voltage Apparatus Vol. 62, Issue 8, Pages: 34-42(2026)
- 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：
- 基金信息：
- DOI：10.13296/j.1001-1609.hva.2026.08.005
  CLC：
- Received：16 December 2025，
  
  Revised：2026-02-13，
  
  Published：16 August 2026
- 稿件说明：
移动端阅览
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：

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

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