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作 者:张丽 ZHANG Li(Chongging Fuling Electric Power Industrial Co.Ltd.,Chongqing 408000,China)
机构地区:[1]重庆涪陵电力实业股份有限公司,重庆408000
出 处:《工业加热》2024年第3期40-43,47,共5页Industrial Heating
基 金:涪陵电力多组态智慧能源融合设备管理平台科技研发项目(52789921N00H)。
摘 要:为提高电源柜温度测量精度,提出一种基于多通道高精度测量的电源柜温度校正方法。方法首先采用K均值聚类算法和人工鱼群优化算法优化径向基函数网络的最佳基函数中心和宽度,以及隐藏层与输出层的连接权值等参数,实现RBF神经网络的优化;然后将优化后的RBF网络应用于多通道高精度电源柜测温系统,实现测量电源柜温度的校正。仿真结果表明,所提方法可实现基于多通道高精度测量的电源柜温度非线性误差的校正,使测量的温度误差小于0.4℃;相较于标准RBF网络和BP神经网络,所提的方法具有更快的训练速度和更小的均方根误差,可用于对测温精度要求较高的测温环境。而本研究的创新点在于基于热电偶测量原理,实现了非线性温度变化的自适应补偿,提高了测量的精度。In order to improve the accuracy of power cabinet temperature measurement,a power cabinet temperature correction method based on multi-channel high-accuracy measurement is proposed.Firstly,K-means clustering algorithm and artificial fish swarms algorithm(AFSA)were adopted to optimize the optimal basis function center and width of the radial basis function network(RBF),as well as the connection weights of the hidden layer and the output layer,thus the optimization of the RBF network was realized.Then,the improved RBF network was applied to the multi-channel high-accuracy power cabinet temperature measurement system,thus the temperature of the measuring power cabinet was corrected.The simulation results show that the proposed method can realize the correction of the temperature nonlinear error of the power cabinet based on multi-channel high-accuracy measurement,which makes the temperature error measured less than 0.4℃.Compared with the standard RBF network and BP neural network,the proposed method has faster training speed and smaller root mean square error,and can be used in temperature measurement environments that require higher temperature measurement accuracy.The innovation of this research lies in the realization of adaptive compensation of nonlinear temperature change based on the principle of thermocouple measurement and the improvement of measurement accuracy.
关 键 词:测温校正 电源柜温度 K均值聚类算法 AFSA算法 RBF网络 热电偶
分 类 号:TH86[机械工程—仪器科学与技术]
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