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...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.展开更多
基金supported by Basic Research Project for Higher Education Institutions of Liaoning Provincial Department of Education(General Project)Shenyang University of Technology+4 种基金Research on optimization design of fan cone angle based on fluid physics methodsProject number:LJKZ0159Liaoning Provincial Education Science 14th Five Year Plan,Research on the Construction of New Artificial Intelligence Technology and High Quality Education Service Supply System,2023–2025,Project Number:JG22DB488Ministry of Education's"Chunhui Plan",Research on Optimization Model and Algorithm for Microgrid Energy Scheduling Based on Biological Behavior,Project Number:202200209Basic Research Project of Liaoning Provincial Department of Education"Training and Application of Multimodal Deep Neural Network Models for Vertical Fields"Project Number:JYTMS20231160.
文摘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.
