A new dust detection method for photovoltaic panel surface based on Pytorch and its economic benefit analysis

Dust accumulation on the surface of solar photovoltaic panels diminishes their power generation efficiency,leading to reduced energy generation.Regular monitoring and cleaning of solar photovoltaic panels is essential.Thus,developing optimal procedures for their upkeep is crucial for improving component efficiency,reducing maintenance costs,and conserving resources.This study introduces an improved Adam optimization algorithm designed specifically for detecting dust on the surface of solar photovoltaic panels.Although the traditional Adam algorithm is the preferred choice for optimizing neural network models,it occasionally encounters problems such as local optima,overfitting,and not convergence due to inconsistent learning rates during the optimization process.To mitigate these issues,the improved algorithm incorporates Warmup technology and cosine annealing strategies with traditional Adam algorithm,that allows for a gradual increase in the learning rate,ensuring stability in the preliminary phases of training.Concurrently,the improved algorithm employs a cosine annealing strategy to dynamically tweak the learning rate.This not only counters the local optimization issues to some degree but also bolsters the generalization ability of the model.When applied on the dust detection on the surface of solar photovoltaic panels,this improved algorithm exhibited superior convergence and training accuracy on the surface dust detection dataset of solar photovoltaic panels in comparison to the standard Adam method.Remarkably,it displayed noteworthy improvements within three distinct neural network frameworks:ResNet-18,VGG-16,and MobileNetV2,thereby attesting to the effectiveness of the novel algorithm.These findings hold significant promise and potential applications in the field of surface dust detection of solar photovoltaic panels.These research results will create economic benefits for enterprises and individuals,and are an important strategic development direction for the country.

An Overview of Passive and Active Dust Detection Methods Using Satellite Measurements

In this paper,the methods to detect dust based on passive and active measurements from satellites have been summarized.These include the visible and infrared（VIR） method,thermal infrared（TIR） method,microwave polarized index（MPI） method,active lidar-based method,and combined lidar and infrared measurement（CLIM） method.The VIR method can identify dust during daytime.Using measurements at wavelengths of 8.5,11.0,and 12.0 fan,the TIR method can distinguish dust from other types of aerosols and cloud,and identify the occurrence of dust over bright surfaces and during night.Since neither the VIR nor the TIR method can penetrate ice clouds,they cannot detect dust beneath ice clouds.The MPI method,however,can identify about 85%of the dust beneath ice clouds.Meanwhile,the active lidar-based method,which uses the Cloud-Aerosol Lidar with Orthogonal Polarization（CALIOP） data and five-dimensional probability distribution functions,can provide very high-resolution vertical profiles of dust aerosols.Nonetheless,as the signals from dense dust and thin clouds are similar in the CALIOP measurements,the lidar-based method may fail to distinguish between them,especially over dust source regions.To address this issue,the CLIM method was developed,which takes the advantages of both TIR measurements（to discriminate between ice cloud and dense dust layers） and lidar measurements（to detect thin dust and water cloud layers）.The results obtained by using the new CLIM method show that the ratio of dust misclassification has been significantly reduced.Finally,a concept module for an integrated multi-satellites dust detection system was proposed to overcome some of the weaknesses inherent in the single-sensor dust detection.

Discrimination and Validation of Clouds and Dust Aerosol Layers over the Sahara Desert with Combined CALIOP and IIR Measurements

This study validates a method for discriminating between daytime clouds and dust aerosol layers over the Sahara Desert that uses a combination of active CALIOP（Cloud-Aerosol Lidar with Orthogonal Polarization） and passive IIR（Infrared Imaging Radiometer） measurements;hereafter,the CLIM method.The CLIM method reduces misclassification of dense dust aerosol layers in the Sahara region relative to other techniques.When evaluated against a suite of simultaneous measurements from CALIPSO（Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations）,CloudSat,and the MODIS（Moderate-resolution Imaging Spectroradiometer）,the misclassification rate for dust using the CLIM technique is 1.16%during boreal spring 2007.This rate is lower than the misclassification rates for dust using the cloud aerosol discriminations performed for version 2（V2-CAD;16.39%） or version 3（V3-CAD;2.01%） of the CALIPSO data processing algorithm.The total identification errors for data from in spring 2007 are 13.46%for V2-CAD,3.39%for V3-CAD,and 1.99%for CLIM.These results indicate that CLIM and V3-CAD are both significantly better than V2-CAD for discriminating between clouds and dust aerosol layers.Misclassifications by CLIM in this region are mainly limited to mixed cloud-dust aerosol layers.V3-CAD sometimes misidentifies low-level aerosol layers adjacent to the surface as thin clouds,and sometimes fails to detect thin clouds entirely.The CLIM method is both simple and fast,and may be useful as a reference for testing or validating other discrimination techniques and methods.