| 基于RFID的电力设备全寿命周期故障预测 |
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| 引用本文：黄小琼1,沈曙明2,李晨1,鲁然1,金阳忻1.基于RFID的电力设备全寿命周期故障预测[J].电网与清洁能源,2023,39(11):80~85 |
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| 基金项目:国网浙江省电力有限公司科技项目（5211YF20000Q） |
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| 中文摘要:为了延长电力设备寿命，引入RFID技术预测电力设备全寿命周期故障。使用RFID技术定位并剔除设备故障数据，利用z-score法归一化处理数据；使用交叉熵理论获取数据的随机变量，建立概率密度函数，据此构建设备的全寿命周期故障预测模型；以构建的模型为依据，使用曲线拟合方法获取电力设备的健康指数分布函数，从而实现电力设备的全寿命周期故障预测。实验结果表明：该方法预测电力设备全寿命周期时，高、低温环境下振动峰峰值误差为0.1 ㎝，波动均匀，无突变；全寿命周期故障预测时间较少，为6 s；预测性能较优，故障数据剔除后的预测性能灵敏度为94.8%。 |
| 中文关键词:RFID技术 电力设备 全寿命周期 预测方法 预测模型 |
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| The Full Life Cycle Fault Prediction of Power Equipment Based on RFID |
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| Abstract:To prolong the life of power equipment， the RFID technology is introduced to predict the full life cycle faults of power equipment in this paper. Firstly， the RFID technology is used to locate and eliminate the fault data while the Z-score method is used to normalize the data. Secondly， the probability density function is established by using the data random variables obtained by the cross entropy theory， and the full life cycle fault prediction model of the equipment is constructed through the density function. Finally， based on the model， the health index distribution function of power equipment is obtained by the curve fitting method， so as to realize the prediction. The experimental results show that when the proposed method is used to predict the full life cycle of power equipment， the peak-to-peak vibration error in high and low temperature environment is 0.1 cm， and the fluctuation is uniform without any sudden changes. The time used for the full life cycle prediction is shorter at only 6 s. The prediction performance is better， and the sensitivity of the prediction performance after the fault data is eliminated is 94.8%. |
| keywords:RFID technology power equipment full life cycle prediction method prediction model |
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