光伏电站环网柜温湿度非线性耦合预测模型研究

Research on the prediction model of temperature and humidity in the ring main unit based on nonlinear coupling method

大型太阳能光伏电站中的环网柜工作环境复杂多变,面对温差大、潮湿等恶劣环境,极易发生环网柜运行故障,影响太阳能光伏的安全稳定接入并网。环网柜温湿度具有明显的线性和非线性变化特征,基于环网柜内部温湿度实测数据,利用自回归移动平均(ARIMA)模型和径向基函数(RBF)模型对线性和非线性数据处理能力的优势,构建ARIMA-RBF权重组合温湿度预测模型,对某光伏电站实际环网柜内温湿度进行动态预测。预测结果表明:相较于单一模型,ARIMA-RBF权重组合模型的预测精度更高、稳定性更好;该组合模型通过适当的加权策略充分发挥了单一模型对数据不同特征的处理能力,能较好地评估环网柜内部温湿度状态,可为建立更具普适性的预测模型提供参考,并有助于减少环网柜因长期超温和潮湿环境下运行引起的故障。

The working environment of the ring main unit(RMU)in large solar photovoltaic power plants is complex and variable,faced with harsh environments such as temperature differences and humidity,it is extremely easy to cause operational failures of the ring grid cabinet,which seriously affects the safe and stable connection of solar photovoltaic to transmission lines.Based on the measured temperature and humidity data inside the RMU,utilizing the advantages of ARIMA and RBF model in linear and nonlinear data processing,a temperature and humidity prediction model with ARIMA-RBF weight combination is constructed to dynamically predict the temperature and humidity inside the RMU.The dynamic prediction of temperature and humidity in the actual loop cabinet of a photovoltaic power station is carried out.The prediction results show that,compared with the single model,the ARMI-RBF weight combination model has higher prediction accuracy and better stability.The combined model gives full play to the processing ability of a single model for different characteristics of data through appropriate weighting strategies,and can better evaluate the temperature and humidity state inside the RMU.It can provide a reference for the establishment of a more universal prediction model,and help to reduce the failure caused by long-term operation of the ring cabinet under ultra-mild and humid environment.