基于BP神经网络的干式变压器绕组温度预测  

Prediction of Winding Temperature of Dry-Type Transformer Based on BP Neural Network

作　　者：吴文杰 李征[1] Wu Wenjie;Li Zheng(School of Information Science and Technology Donghua University,Shanghai 201620)

机构地区：[1]东华大学信息科学与技术学院,上海201620

出　　处：《科技风》2021年第31期59-61,共3页

摘　　要：变压器作为电力系统中重要的一环,实时掌握变压器的状态信息尤为重要。变压器运行时的温度信息就是判断变压器自身运行状态的一个重要因素,因此提出了基于BP神经网络的干式变压器绕组温度预测方法。首先建立了干式变压器绕组温度BP神经网络预测模型,利用云平台现有的300组数据,进行仿真分析。结果表明,基于BP神经网络的干式变压器绕组温度预测模型具有很好的预测效果,为实现干式变压器绕组的温度监控提供了参考意义。As an important part of the power system,the transformer is particularly important to grasp the status information of the transformer in real time.The temperature information during the operation of the transformer is an important factor for judging the operation status of the transformer itself,so a method for predicting the winding temperature of the dry-type transformer based on the BP neural network is proposed.First,a BP neural network prediction model for the winding temperature of dry-type transformers is established,and the 300 sets of data available on the cloud platform are used for simulation analysis.The results show that the dry-type transformer winding temperature prediction model based on BP neural network has a good prediction effect.It provides a reference for realizing the temperature monitoring of dry-type transformer windings.

关 键 词：BP神经网络 干式变压器 温度 预测 

分 类 号：TM4[电气工程—电器]