The S38C railway axle undergoes induction hardening,resulting in a gradient-distributed microstructure and mechanical properties.The accurate identification of gradient-distributed plastic parameters for the S38C axle...The S38C railway axle undergoes induction hardening,resulting in a gradient-distributed microstructure and mechanical properties.The accurate identification of gradient-distributed plastic parameters for the S38C axle remains a challenging task.To tackle this challenge,the present study proposes a novel approach for identifying the gradient-distributed plastic parameters for the S38C axle by integrating nano-indentation techniques with the machine learning method.Firstly,nano-indentation tests are conducted along the radial direction of the S38C axle to obtain the gradient-distributed load-displacement curves,nano-hardness,and elastic modulus.Subsequently,the dimensionless analysis is performed to obtain the representative stress,strain,and yield stress from load-displacement curves.These parameters are then incorporated into the machine learning method as physical information to identify the gradient-distributed plastic parameters of the S38C axle.The results indicate that the proposed method based on the physics-informed neural network and multi-fidelity neural network successfully identifies the gradient-distributed plastic parameters of the S38C axles and demonstrates superior prediction accuracy and generalization compared with the purely data-driven machine learning method.展开更多
The rectangular wire winding AC electrical machine has drawn extensive attention due to their high slot fill factor,good heat dissipation,strong rigidity and short end-windings,which can be potential candidates for so...The rectangular wire winding AC electrical machine has drawn extensive attention due to their high slot fill factor,good heat dissipation,strong rigidity and short end-windings,which can be potential candidates for some traction application so as to enhance torque density,improve efficiency,decrease vibration and weaken noise,etc.In this paper,based on the complex process craft and the electromagnetic performance,a comprehensive and systematical overview on the rectangular wire windings AC electrical machine is introduced.According to the process craft,the different type of the rectangular wire windings,the different inserting direction of the rectangular wire windings and the insulation structure have been compared and analyzed.Furthermore,the detailed rectangular wire windings connection is researched and the general design guideline has been concluded.Especially,the performance of rectangular wire windings AC machine has been presented,with emphasis on the measure of improving the bigger AC copper losses at the high speed condition due to the distinguished proximity and skin effects.Finally,the future trend of the rectangular wire windings AC electrical machine is prospected.展开更多
The inductances in d-q axis have an important influence on the behavior of PMSM （PM （permanent-magnet） synchronous machines）. Their calculation is fundamental not only to evaluate the performance such as torque an...The inductances in d-q axis have an important influence on the behavior of PMSM （PM （permanent-magnet） synchronous machines）. Their calculation is fundamental not only to evaluate the performance such as torque and field weakening capability but also to design the control system to maximize performance and power factor. This paper presents a study of inductance in the d-q axis for buried （i.e., IPMSM （interior） PM Synchronous Machines）. This study is achieved using 2-D （two-dimensional） FEM （finite-element method） and Park＇s transformation.展开更多
This study explores the initiation mechanisms of convective wind events,emphasizing their variability across different atmospheric circulation patterns.Historically,the inadequate feature categorization within multi-f...This study explores the initiation mechanisms of convective wind events,emphasizing their variability across different atmospheric circulation patterns.Historically,the inadequate feature categorization within multi-faceted forecast models has led to suboptimal forecast efficacy,particularly for events in dynamically weak forcing conditions during the warm season.To improve the prediction accuracy of convective wind events,this research introduces a novel approach that combines machine learning techniques to identify varying meteorological flow regimes.Convective winds(CWs)are defined as wind speeds reaching or exceeding 17.2 m s^(-1)and severe convective winds(SCWs)as speeds surpassing 24.5 m s^(-1).This study examines the spatial and temporal distribution of CW and SCW events from 2013 to 2021 and their circulation dynamics associated with three primary flow regimes:cold air advection,warm air advection,and quasibarotropic conditions.Key circulation features are used as input variables to construct an effective weather system pattern recognition model.This model employs an Adaptive Boosting(AdaBoost)algorithm combined with Random Under-Sampling(RUS)to address the class imbalance issue,achieving a recognition accuracy of 90.9%.Furthermore,utilizing factor analysis and Support Vector Machine(SVM)techniques,three specialized and independent probabilistic prediction models are developed based on the variance in predictor distributions across different flow regimes.By integrating the type of identification model with these prediction models,an enhanced comprehensive model is constructed.This advanced model autonomously identifies flow types and accordingly selects the most appropriate prediction model.Over a three-year validation period,this improved model outperformed the initially unclassified model in terms of prediction accuracy.Notably,for CWs and SCWs,the maximum Peirce Skill Score(PSS)increased from 0.530 and 0.702 to 0.628 and 0.726,respectively,and the corresponding maximum Threat Score(TS)improved from 0.087 and 0.024 to 0.120 and 0.026.These improvements were significant across all samples,with the cold air advection type showing the greatest enhancement due to the significant spatial variability of each factor.Additionally,the model improved forecast precision by prioritizing thermal factors,which played a key role in modulating false alarm rates in warm air advection and quasi-barotropic flow regimes.The results confirm the critical contribution of circulation feature recognition and segmented modeling to enhancing the adaptability and predictive accuracy of weather forecast models.展开更多
Ensuring the reliability of wind energy as a dependable source requires overcoming challenges posed by the inherent volatility and stochastic nature of wind patterns.Long-term forecasting provides strategic advantages...Ensuring the reliability of wind energy as a dependable source requires overcoming challenges posed by the inherent volatility and stochastic nature of wind patterns.Long-term forecasting provides strategic advantages in managing energy generation projects,enabling the development of effective portfolio management strategies.The primary objective of this study was the development of forecasting methods to support strategic decision-making within the scope of wind energy operations,specifically targeting the PindaíWind Complex and its commercial dispatch.The study integrated Big Data analytics,data engineering,and computational techniques through the application of machine learning algorithms:including eXtreme Gradient Boosting,Multilayer Perceptron,Support Vector Regression,Ridge Regression,and Random Forests,aiming to generate forward-looking projections of the complex’s energy production for the year 2023.To this end,five supervised machine learning techniques were modeled and implemented.These techniques were grounded in their respective mathematical and structural formulations,and the empirical foundation for modeling was provided by historical power generation data from the PindaíWind Complex,combined with high-resolution realized and forecasted meteorological data retrieved via the Open-Meteo API.The models are trained using historical monthly generation data from the PindaíWind Complex,which has an installed capacity of 79.9 MW and is located in the northeastern region of Brazil,along with meteorological data from reanalysis models,such as air temperature,relative humidity,precipitation,surface pressure,wind speed at 10 m,wind speed at 100 m,and wind gusts.These methodologies are applied to forecast monthly wind generation for the year 2023,and the outputs are systematically compared using evaluation metrics to determine the most suitable modeling approach.The results highlight the superiority of the Multilayer Perceptron,Support Vector Regression,and eXtreme Gradient Boosting models,which achieved Kling-Gupta Efficiency(KGE)of 0.89,0.89,and 0.90,mean absolute scaled error(MASE)of 0.29,0.31,and 0.18,root mean square errors(RMSE)of 0.56,0.59,and 0.35,and mean absolute errors(MAE)of 0.48,0.52,and 0.29,respectively.展开更多
Fault sensing in wind turbine(WT)generator bearings is essential for ensuring reliability and holding down maintenance costs.Feeding raw sensor data to machine learning(ML)model often overlooks the enveloping interdep...Fault sensing in wind turbine(WT)generator bearings is essential for ensuring reliability and holding down maintenance costs.Feeding raw sensor data to machine learning(ML)model often overlooks the enveloping interdependencies between system elements.This study proposes a new hybrid method that combines the domain knowledge via knowledge graphs(KGs)and the traditional feature-based data.Incorporation of contextual relationships through construction of graph embedding methods,such as Node2Vec,can capture meaningful information,such as the relationships among key parameters(e.g.wind speed,rotor Revolutions Per Minute(RPM),and temperature)in the enriched feature representations.These node embeddings,when augmented with the original data,can be used to allow the model to learn and generalize better.As shown in results achieved on experimental data,the augmented ML model(with KG)is much better at predicting with the help of accuracy and error measure compared to traditional ML methods.Paired t-test analysis proves the statistical validity of this improvement.Moreover,graph-based feature importance increases the interpretability of the model and helps to uncover the structurally significant variables that are otherwise ignored by the common methods.The approach provides an excellent,knowledge-guided manner through which intelligent fault detection can be executed on WT systems.展开更多
Optimizing wind energy harvesting performance remains a significant challenge.Machine learning(ML)offers a promising approach for addressing this challenge.This study proposes an ML-based approach using the radial bas...Optimizing wind energy harvesting performance remains a significant challenge.Machine learning(ML)offers a promising approach for addressing this challenge.This study proposes an ML-based approach using the radial basis function neural network(RBFNN)and differential evolution(DE)to predict and optimize the structural parameters(the diameter of the spherical bluff body D,the total spring stiffness k,and the length of the piezoelectric cantilever beam L)of the wind energy harvester(WEH).The RBFNN model is trained with theoretical data and validated with wind tunnel experimental results,achieving the coefficient-of-determination scores R2of 97.8%and 90.3%for predicting the average output power Pavgand aero-electro-mechanical efficiencyηaem,respectively.The DE algorithm is used to identify the optimal parameter combinations for wind speeds U ranging from 2.5 m/s to 6.5 m/s.The maximum Pavgis achieved when D=57.5 mm,k=28.8 N/m,L=112.1 mm,and U=4.6 m/s,while the maximumηaemis achieved when D=52.7 mm,k=29.2 N/m,L=89.2 mm,and U=4.7 m/s.Compared with that of the non-optimized structure,the WEH performance is improved by 28.6%in P_(avg)and 19.1%inη_(aem).展开更多
Based on the multi-loop method, the rotating torque and speed of theinduction machine are analyzed. The fluctuating components of the torque and speed caused by rotorwinding faults are studied. The models for calculat...Based on the multi-loop method, the rotating torque and speed of theinduction machine are analyzed. The fluctuating components of the torque and speed caused by rotorwinding faults are studied. The models for calculating the fluctuating components are put forward.Simulation and computation results show that the rotor winding faults will cause electromagnetictorque and rotating speed to fluctuate; and fluctuating frequencies are the same and their magnitudewill increase with the rise of the severity of the faults. The load inertia affects the torque andspeed fluctuation, with the increase of inertia, the fluctuation of the torque will rise, while thecorresponding speed fluctuation will obviously decline.展开更多
基金supported by the National Key Research and Development Plan(Grant No.2022YFB3401901)the National Natural Science Foundation of China(Grant Nos.12192210,12192214,12072295,and 12222209)+1 种基金Independent Project of State Key Laboratory of Rail Transit Vehicle System(Grant No.2023TPL-T03)Fundamental Research Funds for the Central Universities(Grant No.2682023CG004).
文摘The S38C railway axle undergoes induction hardening,resulting in a gradient-distributed microstructure and mechanical properties.The accurate identification of gradient-distributed plastic parameters for the S38C axle remains a challenging task.To tackle this challenge,the present study proposes a novel approach for identifying the gradient-distributed plastic parameters for the S38C axle by integrating nano-indentation techniques with the machine learning method.Firstly,nano-indentation tests are conducted along the radial direction of the S38C axle to obtain the gradient-distributed load-displacement curves,nano-hardness,and elastic modulus.Subsequently,the dimensionless analysis is performed to obtain the representative stress,strain,and yield stress from load-displacement curves.These parameters are then incorporated into the machine learning method as physical information to identify the gradient-distributed plastic parameters of the S38C axle.The results indicate that the proposed method based on the physics-informed neural network and multi-fidelity neural network successfully identifies the gradient-distributed plastic parameters of the S38C axles and demonstrates superior prediction accuracy and generalization compared with the purely data-driven machine learning method.
基金This work was supported by the National Nature Science Foundation of China(NSFC)under Project 51607079.
文摘The rectangular wire winding AC electrical machine has drawn extensive attention due to their high slot fill factor,good heat dissipation,strong rigidity and short end-windings,which can be potential candidates for some traction application so as to enhance torque density,improve efficiency,decrease vibration and weaken noise,etc.In this paper,based on the complex process craft and the electromagnetic performance,a comprehensive and systematical overview on the rectangular wire windings AC electrical machine is introduced.According to the process craft,the different type of the rectangular wire windings,the different inserting direction of the rectangular wire windings and the insulation structure have been compared and analyzed.Furthermore,the detailed rectangular wire windings connection is researched and the general design guideline has been concluded.Especially,the performance of rectangular wire windings AC machine has been presented,with emphasis on the measure of improving the bigger AC copper losses at the high speed condition due to the distinguished proximity and skin effects.Finally,the future trend of the rectangular wire windings AC electrical machine is prospected.
文摘The inductances in d-q axis have an important influence on the behavior of PMSM （PM （permanent-magnet） synchronous machines）. Their calculation is fundamental not only to evaluate the performance such as torque and field weakening capability but also to design the control system to maximize performance and power factor. This paper presents a study of inductance in the d-q axis for buried （i.e., IPMSM （interior） PM Synchronous Machines）. This study is achieved using 2-D （two-dimensional） FEM （finite-element method） and Park＇s transformation.
基金Guangdong S&T Program(2024A1111120024)CMA Innovation and Development Fund(CXFZ2024J014)+3 种基金CMA Youth Innovation Team(CMA2024QN01)PRB Meteorological Open Research Fund(ZJLY202425-GD02)GBA Meteorological S&T Program(GHMA2024Y04)Guangzhou Meteorological Research Project(Z202401)。
文摘This study explores the initiation mechanisms of convective wind events,emphasizing their variability across different atmospheric circulation patterns.Historically,the inadequate feature categorization within multi-faceted forecast models has led to suboptimal forecast efficacy,particularly for events in dynamically weak forcing conditions during the warm season.To improve the prediction accuracy of convective wind events,this research introduces a novel approach that combines machine learning techniques to identify varying meteorological flow regimes.Convective winds(CWs)are defined as wind speeds reaching or exceeding 17.2 m s^(-1)and severe convective winds(SCWs)as speeds surpassing 24.5 m s^(-1).This study examines the spatial and temporal distribution of CW and SCW events from 2013 to 2021 and their circulation dynamics associated with three primary flow regimes:cold air advection,warm air advection,and quasibarotropic conditions.Key circulation features are used as input variables to construct an effective weather system pattern recognition model.This model employs an Adaptive Boosting(AdaBoost)algorithm combined with Random Under-Sampling(RUS)to address the class imbalance issue,achieving a recognition accuracy of 90.9%.Furthermore,utilizing factor analysis and Support Vector Machine(SVM)techniques,three specialized and independent probabilistic prediction models are developed based on the variance in predictor distributions across different flow regimes.By integrating the type of identification model with these prediction models,an enhanced comprehensive model is constructed.This advanced model autonomously identifies flow types and accordingly selects the most appropriate prediction model.Over a three-year validation period,this improved model outperformed the initially unclassified model in terms of prediction accuracy.Notably,for CWs and SCWs,the maximum Peirce Skill Score(PSS)increased from 0.530 and 0.702 to 0.628 and 0.726,respectively,and the corresponding maximum Threat Score(TS)improved from 0.087 and 0.024 to 0.120 and 0.026.These improvements were significant across all samples,with the cold air advection type showing the greatest enhancement due to the significant spatial variability of each factor.Additionally,the model improved forecast precision by prioritizing thermal factors,which played a key role in modulating false alarm rates in warm air advection and quasi-barotropic flow regimes.The results confirm the critical contribution of circulation feature recognition and segmented modeling to enhancing the adaptability and predictive accuracy of weather forecast models.
文摘Ensuring the reliability of wind energy as a dependable source requires overcoming challenges posed by the inherent volatility and stochastic nature of wind patterns.Long-term forecasting provides strategic advantages in managing energy generation projects,enabling the development of effective portfolio management strategies.The primary objective of this study was the development of forecasting methods to support strategic decision-making within the scope of wind energy operations,specifically targeting the PindaíWind Complex and its commercial dispatch.The study integrated Big Data analytics,data engineering,and computational techniques through the application of machine learning algorithms:including eXtreme Gradient Boosting,Multilayer Perceptron,Support Vector Regression,Ridge Regression,and Random Forests,aiming to generate forward-looking projections of the complex’s energy production for the year 2023.To this end,five supervised machine learning techniques were modeled and implemented.These techniques were grounded in their respective mathematical and structural formulations,and the empirical foundation for modeling was provided by historical power generation data from the PindaíWind Complex,combined with high-resolution realized and forecasted meteorological data retrieved via the Open-Meteo API.The models are trained using historical monthly generation data from the PindaíWind Complex,which has an installed capacity of 79.9 MW and is located in the northeastern region of Brazil,along with meteorological data from reanalysis models,such as air temperature,relative humidity,precipitation,surface pressure,wind speed at 10 m,wind speed at 100 m,and wind gusts.These methodologies are applied to forecast monthly wind generation for the year 2023,and the outputs are systematically compared using evaluation metrics to determine the most suitable modeling approach.The results highlight the superiority of the Multilayer Perceptron,Support Vector Regression,and eXtreme Gradient Boosting models,which achieved Kling-Gupta Efficiency(KGE)of 0.89,0.89,and 0.90,mean absolute scaled error(MASE)of 0.29,0.31,and 0.18,root mean square errors(RMSE)of 0.56,0.59,and 0.35,and mean absolute errors(MAE)of 0.48,0.52,and 0.29,respectively.
文摘Fault sensing in wind turbine(WT)generator bearings is essential for ensuring reliability and holding down maintenance costs.Feeding raw sensor data to machine learning(ML)model often overlooks the enveloping interdependencies between system elements.This study proposes a new hybrid method that combines the domain knowledge via knowledge graphs(KGs)and the traditional feature-based data.Incorporation of contextual relationships through construction of graph embedding methods,such as Node2Vec,can capture meaningful information,such as the relationships among key parameters(e.g.wind speed,rotor Revolutions Per Minute(RPM),and temperature)in the enriched feature representations.These node embeddings,when augmented with the original data,can be used to allow the model to learn and generalize better.As shown in results achieved on experimental data,the augmented ML model(with KG)is much better at predicting with the help of accuracy and error measure compared to traditional ML methods.Paired t-test analysis proves the statistical validity of this improvement.Moreover,graph-based feature importance increases the interpretability of the model and helps to uncover the structurally significant variables that are otherwise ignored by the common methods.The approach provides an excellent,knowledge-guided manner through which intelligent fault detection can be executed on WT systems.
基金Project supported by the National Key R&D Program of China(No.2021YFF0501001)the National Natural Science Foundation of China(Nos.52308315,51922046,and 52192661)+3 种基金the Research Funds of Huazhong University of Science and Technology(No.2023JCYJ014)the China Postdoctoral Science Foundation(No.2023M731206)the Research Funds of China Railway Siyuan Survey and Design Group Co.Ltd.(Nos.KY2023014S,KY2023126S,2021K085,2020K006,and 2020K172)the Autonomous Innovation Fund of Hubei Province of China(No.5003242027)。
文摘Optimizing wind energy harvesting performance remains a significant challenge.Machine learning(ML)offers a promising approach for addressing this challenge.This study proposes an ML-based approach using the radial basis function neural network(RBFNN)and differential evolution(DE)to predict and optimize the structural parameters(the diameter of the spherical bluff body D,the total spring stiffness k,and the length of the piezoelectric cantilever beam L)of the wind energy harvester(WEH).The RBFNN model is trained with theoretical data and validated with wind tunnel experimental results,achieving the coefficient-of-determination scores R2of 97.8%and 90.3%for predicting the average output power Pavgand aero-electro-mechanical efficiencyηaem,respectively.The DE algorithm is used to identify the optimal parameter combinations for wind speeds U ranging from 2.5 m/s to 6.5 m/s.The maximum Pavgis achieved when D=57.5 mm,k=28.8 N/m,L=112.1 mm,and U=4.6 m/s,while the maximumηaemis achieved when D=52.7 mm,k=29.2 N/m,L=89.2 mm,and U=4.7 m/s.Compared with that of the non-optimized structure,the WEH performance is improved by 28.6%in P_(avg)and 19.1%inη_(aem).
文摘Based on the multi-loop method, the rotating torque and speed of theinduction machine are analyzed. The fluctuating components of the torque and speed caused by rotorwinding faults are studied. The models for calculating the fluctuating components are put forward.Simulation and computation results show that the rotor winding faults will cause electromagnetictorque and rotating speed to fluctuate; and fluctuating frequencies are the same and their magnitudewill increase with the rise of the severity of the faults. The load inertia affects the torque andspeed fluctuation, with the increase of inertia, the fluctuation of the torque will rise, while thecorresponding speed fluctuation will obviously decline.
