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Ningxia University (2021)

Identification and Analysis of Dust on the Surface of Photovoltaic Panels in Desertification Areas

苏国庆

Titre : Identification and Analysis of Dust on the Surface of Photovoltaic Panels in Desertification Areas

Auteur : 苏国庆

Grade : Master 2021

Université : Ningxia University

Résumé partiel
In recent years,the solar photovoltaic power generation industry has developed rapidly.Open areas such as deserts and wastelands are important areas for the construction of large-scale centralized photovoltaic power plants.The surrounding areas are usually relatively empty,and the surface form is generally sandy.When severe weather such as strong winds and sandstorms occur,floating dust and flying dust will appear in the photovoltaic power station,causing sand particles in the sky to settle on the surface of the photovoltaic panels,which will have many important negative effects on the photovoltaic power generation system.Due to factors such as the arrangement of photovol taic panels,topography and other factors,the amount of dust on the surface of the photovoltaic panel is not uniform,and the dust on the surface of the photovoltaic panel in some locations is relatively more serious,which affects the performance and life of the photovoltaic cell in the corresponding range to a certain extent.It brings a lot of uncertainty to the forecast of the power generation of photovoltaic power plants.Therefore,the realization of non-contact identification of dust on the surface of the photovoltaic panel and direct extraction of sand parameters is of great significance to the maintenance and operation of photovoltaic power plants.The rapid development of deep learning target detection technology and image processing technology provides new technical ideas for the automatic detection of dust accumulation in photovoltaic power stations.In view of this,the work content of this article is as follows:First of all,in response to the demand for detection of dust targets on the surface of photovoltaic panels in the operation of photovoltaic power plants,an SSD model with Resnet50 as the feature extraction backbone network is proposed,and the channel and spatial attention mechanism modules and feature pyramid networks are added to detect the surface dust of photovoltaic panels.Training on 2400 photovoltaic panel surface dust image data sets,the average accuracy of the improved SSD algorithm is 92.65%,and the detection speed is 26.4FPS.And through YOLOV3,Faster-RCNN,SSD and improved SSD algorithm to compare the surface dust detection of photovoltaic panels and the detection results of photovoltaic panel surface dust in complex environments.

Mots clés : Photovoltaic panel surface dust ;Target detection ;Watershed algorithm ;Sand parameters ;

Présentation (CNKI)

Page publiée le 2 mars 2022