基于GA优化BP神经网络预测开关柜内部设备温度OA

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.