基于数据融合的光伏组件故障诊断PV Module Fault Diagnosis Based on Data Fusion
陈凌;韩伟;张经炜;
摘要(Abstract):
对不同故障下光伏组件内部等效参数和外特性电气参数进行特征提取,分别采用改进人工鱼群算法优化径向基函数神经网络(improved artificial fish swarm algorithm-radical basic function neural network,IAFSA-RBFNN)算法和相关向量机(relevance vector machine,RVM)算法,建立了基于内部等效参数和外特性电气参数的4种光伏组件故障诊断模型,用于光伏组件的初步故障诊断。在此基础上,提出了一种基于改进证据相似度的光伏组件数据融合故障诊断模型,将上述4种模型的诊断结果作为该改进数据融合算法的基本概率分配(basic probability assignment,BPA)函数值,在决策层进行融合诊断输出,仿真和实验结果验证了上述方法可有效提高故障诊断的精度。
关键词(KeyWords): 光伏组件;数据融合;径向基函数神经网络;相关向量机;故障诊断;改进证据相似度
基金项目(Foundation): 江苏省普通高校研究生科研创新计划(CXZZ12_0228)~~
作者(Author): 陈凌;韩伟;张经炜;
Email:
DOI: 10.13335/j.1000-3673.pst.2016.2294
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