基于BP神经网络的太阳能路灯SOC预测  

SOC Prediction of Solar Street Lamps Based on BP Neural Network

作　　者：张安莉[1,2] 谢檬 李翔[1] 杜阳光 ZHANG Anli;XIE Meng;LI Xiang;DU Yangguang(School of Electrical and Information Engineering,Xi'an Jiaotong University City College,Xi'an Shaanxi 710018,China;Photovoltaic Technologies and Systems Shaanxi Provincial University Engineering Research Center,Xi'an Jiaotong University City College,Xi'an Shaanxi 710018,China;Robot and Intelligent Manufacturing Shaanxi Provincial University Engineering Research Center Xi'an Shaanxi 710018,China;State Grid Xianyang Power Supply Company,Xianyang Shaanxi 712000,China)

机构地区：[1]西安交通大学城市学院电气信息学院,陕西西安710018 [2]光伏技术与系统陕西省高校工程研究中心,陕西西安710018 [3]机器人与智能制造陕西省高校工程研究中心,陕西西安710018 [4]国网咸阳供电公司,陕西咸阳712000

出　　处：《电子器件》2024年第5期1227-1232,共6页Chinese Journal of Electron Devices

基　　金：陕西省教育厅教学改革重点攻关项目(23BG054);陕西省教育厅教学改革项目(23BY193);机器人与智能制造陕西省高校工程研究中心基金项目(2022GZ04);中国电子教育学会教育教学改革研究项目(DJZ23009);西安交通大学城市学院2024年度科研潜力培育项目(2024PY01)。

摘　　要：针对光伏发电应用领域太阳能路灯系统的过充电或过放电现象对蓄电池本身特性产生影响、降低使用寿命的问题,采用单片机和LabVIEW进行太阳能路灯蓄电池电压检测,采用BP神经网络进行太阳能路灯蓄电池荷电率(SOC)预测。BP神经网络将测得数据建立SOC(State of Charge)预测模型,LabVIEW可视化面板实时显示测量数据、波形及预测结果,实现太阳能路灯智能化控制。测试结果表明,系统能够实时检测蓄电池充电电压,并预测电池工作状态,BP神经网络蓄电池SOC预测值与蓄电池电量实测误差为0.1%~0.4%,满足网络误差要求。In view of the problem that the overcharging or overdischarge phenomenon of solar street lamp system in the application field of photovoltaic power generation affects the characteristics of the battery itself,single chip computer and LabVIEW are used to conduct solar street lamp battery voltage detection,and BP neural network is used to predict the state of charge(SOC)of solar street lamp battery.A BP neural network SOC prediction model is established for the measured data,and the LabVIEW visualization panel displays the measurement data,waveform and prediction results in real time,so as to realize the intelligent control of solar street lamps.The test results show that the system can detect the battery charging voltage in real time and predict the operating state of the battery.The error of the predicted value of the battery SOC and the measured battery power is about 0.1%~0.4%,which meets the network error requirements.

关 键 词：SOC预测 BP神经网络 蓄电池 LABVIEW 

分 类 号：TM923[电气工程—电力电子与电力传动]