太阳能光伏发电系统中蓄电池SOC预测模型及监控方法研究

赵巍巍; 王洪诚

赵巍巍,王洪诚.太阳能光伏发电系统中蓄电池SOC预测模型及监控方法研究[J].电测与仪表,2014,51(16):.
Zhao Weiwei,Wang Hongcheng.Battery SOC Forecasting Model and monitoring method in Solar PV System[J].Electrical Measurement & Instrumentation,2014,51(16):.

太阳能光伏发电系统中蓄电池SOC预测模型及监控方法研究

Battery SOC Forecasting Model and monitoring method in Solar PV System

DOI：

中文关键词: SOC VRLA电池组 BP神经网络预测实时监控 DSP

英文关键词:SOC VRLA Battery Back-Propagation Neural Network Algorithm Real-time monitoring DSP

基金项目:

作者中文名	作者英文名	单位
赵巍巍	Zhao Weiwei	四川建筑职业技术学院
王洪诚	Wang Hongcheng	西南石油大学

摘要点击次数: 1389

中文摘要:

建立了一种基于反向传播（BP）神经网络算法的阀控密封式铅酸蓄电池（VRLA）的SOC预测模型，利用MATLAB仿真对三层BP网络模型的性能进行了校验，采用由TMS320F28335为核心组成的硬件控制电路对VRLA蓄电池组进行了实时数据采集，依据预测出的SOC值和控制电路，实现对蓄电池组的放电工作状态的智能监测与控制，保证了系统的经济、高效、安全可靠运行。监测控制系统具有蓄电池SOC预测，端电压、充放电电流等参数实时监控，数据传输及状态显示等功能，具有较高的实际应用价值。

英文摘要:

State of charge (SOC) forecasting model, which are based on Back-Propagation neural network algorithm, was given for Solar PV System. Matlab programs was wrote to check the performance of the three layers BP network model, and corresponding experiments was done，Based on that, this method utilized TMS320F28335 as MCU of the hardware circuit to gather real-time data, and utilized the predicting value of SOC and control circuits to realize the intelligent monitoring of Valve-Regulated Lead Acid Battery pack, and ensured the economic, efficient, safe and reliable operation of the system. The monitoring system was Provided with SOC prediction, charge and discharge current parameters real-time monitoring, data transmission and status display, etc.

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