PREDICTION OF ASH ACCUMULATION DEGREE OF PHOTOVOLTAIC MODULES BASED ON AVERAGE GRAYSCALE VALUE AND RANDOM FOREST ALGORITHM
This article numerically processes the grayscale histogram of visible light images of photovoltaic modules,introduces the concept of average grayscale value,and combines it with the photovoltaic experimental platform built by the school to confirm the corresponding relationship between the average grayscale value and the ash density of photovoltaic modules.Based on this conclusion,the image of a visual inspection platform for a photovoltaic power station in Inner Mongolia is identified and processed,and it is found that over time,the change in the average gray level difference between the clean side and the accumulated side is positively correlated with power generation loss.Based on the meteorological and image data collected by the experimental platform,a prediction model for the degree of ash accumulation is established using the random forest algorithm.Based on relevant meteorological factors,the average gray level of the ash accumulation board from the previous day is combined as the input variable to predict the average gray level of the ash accumulation board day by day.Different tuning parameters are set to improve the optimization robustness of the prediction algorithm.
photovoltaic modulesash depositionrandom forestimage recognitionaverage grayscale value
NETL
NSTL
万方数据
