基于I-WNN的高温潜油电机温度拟合与预测  

Temperature Fitting and Prediction of High-temperature Submersible Motor Based on I-WNN

作　　者：蒋召平 李越 刘明凯 甄东芳 侯新旭 王通[2] JIANG Zhaoping;LIYue;LIUMingkai;ZHEN Dongfang;HOU Xinxu;WANGTong(Bohai Petroleum Research Institute,Tianjin Branch of CNOOC(China)Co.,Ltd.,Tianjin 300459,China;Oilfield Production Department,China Oilfield Services Limited,Tianjin 300459,China)

机构地区：[1]中海石油(中国)有限公司天津分公司渤海石油研究院,天津300459 [2]中海油田服务股份有限公司油田生产事业部,天津300459

出　　处：《电气传动》2023年第12期68-73,共6页Electric Drive

基　　金：渤海典型稠油油藏热采提高采收率及关键工艺技术研究(KJGG2022-0602)。

摘　　要：为了保障高温潜油电机高效、稳定运行,及时避免电机因运行温度过高而发生故障,进而影响生产,需要在无温度传感器情况下获取其井下温度。基于这一情况,提出了基于I-WNN的高温潜油电机温度识别与预测方法。首先对高温潜油电机运行数据进行分类;然后利用改进小波神经网络对历史数据进行训练,建立高温潜油电机运行数据与温度的映射关系,并对小波神经网络权值参数进行寻优操作得到最优权值;最后通过实验仿真,得到高温电机拟合温度值与预测温度值。In order to ensure the efficient and stable operation of high-temperature submersible motors,and to timely avoid motor failures caused by high operating temperatures,which may affect production,it is necessary to obtain their underground temperature without temperature sensors.Based on this situation,a temperature recognition and prediction method for high-temperature submersible motors based on improved wavelet neural network(I-WNN)was proposed.Firstly,the operating data of high-temperature submersible motors were classified.Then,an improved wavelet neural network was used to train historical data,a mapping relationship between the operating data of high-temperature submersible motors and temperature was established,and the weight parameters of the wavelet neural network were optimized to obtain the most weighted values.Finally,through experimental simulation,the fitted temperature values and predicted temperature values of the high-temperature motor were obtained.

关 键 词：小波神经网络 基于I-WNN的预测算法 K均值聚类 遗传算法 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]