Vision-based Monitoring And Performance Evaluation Of Photovoltaic Panel Dust Deposition

In recent years,photovoltaic power generation is the main form of power generation for renewable energy.With its low pollution,high reliability and wide applicability,the global installed capacity has reached 660 GW.However,due to the long-term exposure of solar panels(photovoltaic panels)to outdoor environments,they are highly susceptible to dust pollution,and the accumulation of ash formed over time will seriously affect the photovoltaic conversion efficiency(ie,power generation efficiency)of photovoltaic panels.At present,the photovoltaic panel ash monitoring system can only measure the power generation efficiency in a single way,and can not analyze the ash accumulation of the photovoltaic panel in real time,nor can it play a guiding role in cleaning and maintenance.Therefore,this paper builds an online monitoring experiment system for the ash deposition status of photovoltaic panels,which provides a reliable basis for the cleaning cycle guidance of photovoltaic panels.Firstly,the basic theory of existing photovoltaic power generation system is studied in detail,and the influence of climate and environmental factors on the performance of photovoltaic panels and the dust characteristics of photovoltaic panels are analyzed.Based on this theory,an online monitoring experiment system for photovoltaic state of photovoltaic panels was built.Based on the C# language,the on-line monitoring software for photovoltaic panel ashing status was designed,and the power generation parameters and meteorological parameters of photovoltaic panels were monitored,calculated and recorded in real time.Secondly,the state of the photovoltaic panels at different ash densities is restored by simulating the natural ash deposition process of the photovoltaic panels.The improved interpolation algorithm is used to denoise the photovoltaic image of the photovoltaic panel,and then the gray image of the photovoltaic panel is analyzed,and the calculation method of the average gray value of the image is proposed.Finally,based on the experimental data,a dynamic characteristic prediction model of the influence of dust accumulation on the electric power loss rate is constructed.Based on the prediction model,this project establishes a method for assessing the economic loss of ash from two aspects of electricity loss and cleaning and maintenance costs caused by ash,and determines the optimal cleaning by minimizing the sum of annual accumulated electricity loss and cleaning and maintenance costs.Cycle and quantitative analysis of the impact of PV power plant installed capacity,annual cumulative cleaning time,grid-connected electricity price and unit area cleaning fee on the optimal cleaning cycle.Taking 50 MW photovoltaic power station as an example,theanalysis results show that the optimal cleaning cycle of photovoltaic power station is 10.14 days,and the number of cleaning times is about 36 times per year.The research results of this subject can be used for real-time monitoring of the ash accumulation status of various photovoltaic power plants,cleaning cycle guidance,improving the utilization of energy by photovoltaic panels,and reducing operating and maintenance costs.