基于改进型相似性建模的光伏积灰监测方法

Improved similarity-based modeling approach for dust deposition monitoring of photovoltaic modules

针对光伏系统积灰程度监测的问题,考虑到基于理论公式的方法误差较大,基于计算机视觉的方法成本较高,传统机器学习方法对训练数据有较高需求,提出弱监督的数据驱动方法:改进型相似性建模(SBM)方法.在原始的SBM方法的基础上,对参数选取、状态矩阵构建与更新、相似性算子设计进行针对性的改进,使得该方法更加适用于光伏系统的应用,提高准确性与响应速度.利用真实的积灰实验数据,对改进型SBM方法与其他5种方法的积灰诊断效果进行对比,包括基于理论公式的方法、前馈神经网络(FNN)、支持向量回归(SVR)、随机森林(RF)和原始SBM方法.结果表明,改进型SBM方法可以以可接受的响应速度劣势实现最佳的积灰程度监测准确性.

A weakly-supervised data-driven approach,improved similarity-based modeling(SBM)approach,was proposed aiming at the problem of dust deposition monitoring of photovoltaic modules.The great error of theoretical formulas approaches,the high cost of computer vision approaches and the large requirements for training data of classical machine learning approaches were considered.Improvements in the steps of parameters selection,state matrix construction and update and similarity operator were made based on the original SBM in order to adapt the approach to the PV systems monitoring and increase the estimated accuracy and response rate.The improved SBM and five other approaches,theoretical formulas,feedforward neural network(FNN),support vector regression(SVR),random forest(RF),and the original SBM were employed for the dust estimation,and their performances were evaluated based on the real data from a dust deposition experiment.Results show that the improved SBM has the best accuracy at the cost of acceptable response time increase.