基于随机森林和专家系统的分布式光伏电站阴影遮挡诊断  

Shadow occlusion diagnosis of distributed photovoltaic power station based on random forest and expert system

作　　者：刘新锋 张旖旎 徐惠三 宋玲[1] 陈梦雅 LIU Xinfeng;ZHANG YiNi;XU Huisan;SONG Ling;CHEN Mengya(School of Computer Science and Technology,Shandong Jianzhu University,Jinan 250101,Shandong,China)

机构地区：[1]山东建筑大学计算机科学与技术学院,山东济南250101

出　　处：《山东大学学报（工学版）》2021年第2期98-104,114,共8页Journal of Shandong University（Engineering Science）

基　　金：山东建筑大学博士基金资助项目(X19023Z0101);国家自然科学基金资助项目(51975332)。

摘　　要：针对分布式光伏电站阴影遮挡提出一种基于随机森林算法的人机协同判别方法。通过遮挡机理分析和逆变器遥测参数转换构建组串直流侧电流离散率、太阳高度角、太阳方位角及电站瞬时发电水平等关键特征参数,搭建随机森林遮挡诊断模型。基于网格搜索法和K折交叉验证法优化参数,通过准确率对比确定基于信息增益的分裂方式。对比支持向量、逻辑回归及决策树等主流算法模型,发现随机森林算法在遮挡诊断场景中具有较强的优势,结合专家系统得出诊断方位后,现场验证了"基于信息增益的随机森林和专家系统"方法的有效性。A human-machine collaborative discriminant method based on the random forest algorithm was proposed to diagnose distributed photovoltaic shadow occlusion. Key characteristic parameters, such as the current dispersion rate on the direct current side of the string, solar altitude angle, solar azimuth angle, and instantaneous power generation level of the power station, were constructed based on the analysis of the shadow occlusion mechanism and the conversion of inverter telemetry parameters. The random forest shadow occlusion diagnosis model was subsequently established. The parameters were optimized based on the grid search method and the K-fold cross-validation method, and the splitting method based on information gain was determined by comparing the accuracy with other machine learning algorithms, such as support vector machine, logistic regression, and decision tree. The random forest algorithm had obvious advantages in shadow occlusion diagnosis scenes. An expert system was combined to obtain the diagnosis position, and then the effectiveness of the method using the random forest algorithm based on information gain and an expert system was verified on site.

关 键 词：分布式光伏 随机森林 专家系统 阴影遮挡诊断 遮挡机理分析 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]