The anode effect is a common failure in the aluminium electrolysis industry.If the anode effect cannot be accurately predicted,it will cause increased energy consumption,harmful gas generation and even equipment damag...The anode effect is a common failure in the aluminium electrolysis industry.If the anode effect cannot be accurately predicted,it will cause increased energy consumption,harmful gas generation and even equipment damage in the aluminium electrolysis.In this paper,an anode effect prediction framework using multi-model merging based on deep learning technology is proposed.Different models are used to process aluminium electrolysis cell condition parameters with high dimensions and different characteristics,and hidden key fault information is deeply mined.A stacked denoising autoencoder is utilized to denoise and extract features from a large number of longperiod parameter data.A long short-term memory network is implemented to identify the intrinsic links between the realtime voltage and current time series and the anode effect.By setting the model time step,the anode effect can be predicted precisely in advance,and the proposed method has good robustness and generalization.Moreover,the traditional Adam algorithm is improved,which enhances the performance and convergence speed of the model.The experimental results show that the classification accuracy and F1score of the model are 97.14% and 0.9579%,respectively.The prediction time can reach 15 min.展开更多
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financially supported by the General Program of National Natural Science Foundation of China(No.62373069)the Major Projects for Technological Transformation(No.H20201555)Chongqing Talent Innovation and Entrepreneurship Demonstration Team Project (No.CQYC202203091061)。
文摘The anode effect is a common failure in the aluminium electrolysis industry.If the anode effect cannot be accurately predicted,it will cause increased energy consumption,harmful gas generation and even equipment damage in the aluminium electrolysis.In this paper,an anode effect prediction framework using multi-model merging based on deep learning technology is proposed.Different models are used to process aluminium electrolysis cell condition parameters with high dimensions and different characteristics,and hidden key fault information is deeply mined.A stacked denoising autoencoder is utilized to denoise and extract features from a large number of longperiod parameter data.A long short-term memory network is implemented to identify the intrinsic links between the realtime voltage and current time series and the anode effect.By setting the model time step,the anode effect can be predicted precisely in advance,and the proposed method has good robustness and generalization.Moreover,the traditional Adam algorithm is improved,which enhances the performance and convergence speed of the model.The experimental results show that the classification accuracy and F1score of the model are 97.14% and 0.9579%,respectively.The prediction time can reach 15 min.
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.
