Random and fluctuating wind speeds make it difficult to stabilize the wind-power output,which complicates the execution of wind-farm control systems and increases the response frequency.In this study,a novel predictio...Random and fluctuating wind speeds make it difficult to stabilize the wind-power output,which complicates the execution of wind-farm control systems and increases the response frequency.In this study,a novel prediction model for ultrashort-term wind-speed prediction in wind farms is developed by combining a deep belief network,the Elman neural network,and the Hilbert-Huang transform modified using an improved particle swarm optimization algorithm.The experimental results show that the prediction results of the proposed deep neural network is better than that of shallow neural networks.Although the complexity of the model is high,the accuracy of wind-speed prediction and stability are also high.The proposed model effectively improves the accuracy of ultrashort-term wind-speed forecasting in wind farms.展开更多
With the advancement of computer and mathematical techniques,significant progress has been made in the 3D modeling of foundation piles.Existing methods include the 3D semi-analytical model for non-destructive low-stra...With the advancement of computer and mathematical techniques,significant progress has been made in the 3D modeling of foundation piles.Existing methods include the 3D semi-analytical model for non-destructive low-strain integrity assessment of large-diameter thin-walled pipe piles and the 3D soil-pile dynamic interaction model.However,these methods have complex analysis procedures and substantial limitations.This paper introduces an innovative and streamlined 3D imaging technique tailored for the detection of pile damage.The approach harnesses the power of an eight-channel ring array transducer to capture internal reflection signals within foundation piles.The acquired signals are subsequently processed using the Hilbert-Huang Transform(HHT),a robust analytical tool known for its effectiveness in handling non-stationary signals.Through the development of a sophisticated multi-channel ring array imaging algorithm,this technique empowers engineers and researchers to identify various pile defects,including their specific type,precise location,and obtain detailed 3D imaging representations.The findings of this research offer a valuable blend of theoretical insights and practical guidance,significantly advancing the state-of-the-art in the realm of concrete pile integrity inspection.By simplifying and enhancing the assessment process,this innovative approach not only addresses the complexities of existing methods but also contributes to the overall safety and reliability of concrete engineering structures.展开更多
基金This study was supported by the Research and Application of Key Technologies in the Design of Large Onshore Smart Wind Power Base(Grant No.XBY-ZDKJ-2020-05)the Scientific Research Project of the China Electric Power Construction Corporation:Research and Application of Key Technologies in the Design of an Onshore Smart Wind Power Base(Grant No.DJ-ZDXM-2020-52)+2 种基金the Danish Energy Agency(Grant No.64013-0405)the Fundamental Research Funds for the Central Universities(Grant No.B210201018)the Jiangsu Province Policy Guidance Program(Grant No.BZ2021019).
文摘Random and fluctuating wind speeds make it difficult to stabilize the wind-power output,which complicates the execution of wind-farm control systems and increases the response frequency.In this study,a novel prediction model for ultrashort-term wind-speed prediction in wind farms is developed by combining a deep belief network,the Elman neural network,and the Hilbert-Huang transform modified using an improved particle swarm optimization algorithm.The experimental results show that the prediction results of the proposed deep neural network is better than that of shallow neural networks.Although the complexity of the model is high,the accuracy of wind-speed prediction and stability are also high.The proposed model effectively improves the accuracy of ultrashort-term wind-speed forecasting in wind farms.
基金supported by China Scholarship Council(No.202008320084)the National Natural Science Foundation of China(Nos.11872191 and 11702118)Foreign Specialist Project of Ministry of Science and Technology(DL2022014011L).
文摘With the advancement of computer and mathematical techniques,significant progress has been made in the 3D modeling of foundation piles.Existing methods include the 3D semi-analytical model for non-destructive low-strain integrity assessment of large-diameter thin-walled pipe piles and the 3D soil-pile dynamic interaction model.However,these methods have complex analysis procedures and substantial limitations.This paper introduces an innovative and streamlined 3D imaging technique tailored for the detection of pile damage.The approach harnesses the power of an eight-channel ring array transducer to capture internal reflection signals within foundation piles.The acquired signals are subsequently processed using the Hilbert-Huang Transform(HHT),a robust analytical tool known for its effectiveness in handling non-stationary signals.Through the development of a sophisticated multi-channel ring array imaging algorithm,this technique empowers engineers and researchers to identify various pile defects,including their specific type,precise location,and obtain detailed 3D imaging representations.The findings of this research offer a valuable blend of theoretical insights and practical guidance,significantly advancing the state-of-the-art in the realm of concrete pile integrity inspection.By simplifying and enhancing the assessment process,this innovative approach not only addresses the complexities of existing methods but also contributes to the overall safety and reliability of concrete engineering structures.
