高压电器2026，Vol.62Issue(8)：34-42,9.DOI:10.13296/j.1001-1609.hva.2026.08.005
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
桑仲庆 1袁会生 1游一民 1戴冬云 1周顺雄 1廖权昌 1姜维云1
作者信息
- 1. 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
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摘要
Abstract
When current flows through the switchgear cabinet,its internal components generate heat.Prolonged ex-posure to temperature above the threshod can damage the equipment and pose safety risk.Threfore,internal temper-ature monitoring and advance temperature prediction are required to enable timely maintenance.In order to accu-rately 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 mod-el is trained and tested.Finally,the pros and cons of the network model are evaluated through measurement indica-tors.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
开关柜/预测/温度/BP神经网络/GA算法Key words
switchgear cabinet/prediction/temperature/BP neural networks/GA algorithm引用本文复制引用
桑仲庆,袁会生,游一民,戴冬云,周顺雄,廖权昌,姜维云..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):34-42,9.
