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
Under the context of global climate change,the frequent occurrence of strong winds in Guyuan has significantly hindered the development of local facility agriculture.Using hourly meteorological data from the Sanying N...Under the context of global climate change,the frequent occurrence of strong winds in Guyuan has significantly hindered the development of local facility agriculture.Using hourly meteorological data from the Sanying National Station and the Guyuan Greenhouse Station between April 2024 and April 2025,this study employed machine learning methods to develop wind speed prediction models based on BP neural network,support vector machine,and random forest(referred to as BP,SVM,and RF models),aiming to provide references for local disaster prevention and mitigation.The results indicate that:1)Wind speed at the Guyuan Greenhouse Station exhibits the strongest correlation with that at the National Station(0.489-0.595),followed by temperature and 24-hour precipitation(0.116-0.336).2)The mean absolute error(MAE)of the BP,RF,and SVM models at all heights is below 1.5 m/s,the root mean square error(RMSE)is under 2.1 m/s,and the forecast accuracy(FA)exceeds 75%,indicating satisfactory model performance.Compared to 3 m,the MAE and RMSE of 0.5 m are larger,while the FA is smaller.This indicates that the wind speed of 0.5 m is close to the ground,and is more affected by surface roughness and turbulence effects,resulting in greater randomness and making the model more difficult.3)Based on case analyses of May 10 and May 1,2024,the overall simulation performance ranks as“RF model>SVM model>BP model”;however,the SVM model demonstrates higher accuracy in simulating strong wind events.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
Multi-phase machines are so attractive for electrical machine designers because of their valuable advantages such as high reliability and fault tolerant ability.Meanwhile,fractional slot concentrated windings(FSCW)are...Multi-phase machines are so attractive for electrical machine designers because of their valuable advantages such as high reliability and fault tolerant ability.Meanwhile,fractional slot concentrated windings(FSCW)are well known because of short end winding length,simple structure,field weakening sufficiency,fault tolerant capability and higher slot fill factor.The five-phase machines equipped with FSCW,are very good candidates for the purpose of designing motors for high reliable applications,like electric cars,major transporting buses,high speed trains and massive trucks.But,in comparison to the general distributed windings,the FSCWs contain high magnetomotive force(MMF)space harmonic contents,which cause unwanted effects on the machine ability,such as localized iron saturation and core losses.This manuscript introduces several new five-phase fractional slot winding layouts,by the means of slot shifting concept in order to design the new types of synchronous reluctance motors(SynRels).In order to examine the proposed winding’s performances,three sample machines are designed as case studies,and analytical study and finite element analysis(FEA)is used for validation.展开更多
This paper proposes the application of high-voltage stator-cable windings in superconducting machines,based on the characteristics of strong magnetic fields and large air gaps.Cross-linked polyethylene cable winding c...This paper proposes the application of high-voltage stator-cable windings in superconducting machines,based on the characteristics of strong magnetic fields and large air gaps.Cross-linked polyethylene cable winding can be employed to achieve a rated voltage of 35 kV in direct-current(DC)-field superconducting machines,thereby enabling a direct connection between the superconducting machine and the power grid,eliminating the need for transformers.We first,through finite element analysis,demonstrate that the proposed high-voltage high-temperature superconducting machine not only meets the requirement of a 35 kV-rated voltage,but also exhibits minimal flux leakage,torque fluctuation,and harmonic distortion.We then compare three candidate types to discuss the tradeoff between the multi-group superconducting field winding arrangement and machine performances.We propose inverted trapezoidal superconducting field winding as a promising candidate,because it has minimal superconductivity material usage,the largest safety margin for the superconducting coils(SCs),low thrust ripple,and low total harmonic distortion with the desired 35 kV-rated voltage.Finally,through large-scale design parameter sweeping,we show how we selected the optimal parameters for field winding and validated them by the finite element method.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
Support vector machines and a Kalman-like observer are used for fault detection and isolation in a variable speed horizontalaxis wind turbine composed of three blades and a full converter. The support vector approach ...Support vector machines and a Kalman-like observer are used for fault detection and isolation in a variable speed horizontalaxis wind turbine composed of three blades and a full converter. The support vector approach is data-based and is therefore robust to process knowledge. It is based on structural risk minimization which enhances generalization even with small training data set and it allows for process nonlinearity by using flexible kernels. In this work, a radial basis function is used as the kernel. Different parts of the process are investigated including actuators and sensors faults. With duplicated sensors, sensor faults in blade pitch positions,generator and rotor speeds can be detected. Faults of type stuck measurements can be detected in 2 sampling periods. The detection time of offset/scaled measurements depends on the severity of the fault and on the process dynamics when the fault occurs. The converter torque actuator fault can be detected within 2 sampling periods. Faults in the actuators of the pitch systems represents a higher difficulty for fault detection which is due to the fact that such faults only affect the transitory state(which is very fast) but not the final stationary state. Therefore, two methods are considered and compared for fault detection and isolation of this fault: support vector machines and a Kalman-like observer. Advantages and disadvantages of each method are discussed. On one hand, support vector machines training of transitory states would require a big amount of data in different situations, but the fault detection and isolation results are robust to variations in the input/operating point. On the other hand, the observer is model-based, and therefore does not require training, and it allows identification of the fault level, which is interesting for fault reconfiguration. But the observability of the system is ensured under specific conditions, related to the dynamics of the inputs and outputs. The whole fault detection and isolation scheme is evaluated using a wind turbine benchmark with a real sequence of wind speed.展开更多
Determination of the aerodynamic configuration of wake is the key to analysis and evaluation of the rotor aerodynamic characteristics of a horizontal-axis wind turbine. According to the aerodynamic configuration, the ...Determination of the aerodynamic configuration of wake is the key to analysis and evaluation of the rotor aerodynamic characteristics of a horizontal-axis wind turbine. According to the aerodynamic configuration, the real magnitude and direction of the onflow velocity at the rotor blade can be determined, and subsequently, the aerodynamic force on the rotor can be determined. The commonly employed wake aerodynamic models are of the cylindrical form instead of the actual expanding one. This is because the influence of the radial component of the induced velocity on the wake configuration is neglected. Therefore, this model should be called a "linear model". Using this model means that the induced velocities at the rotor blades and aerodynamic loads on them would be inexact. An approximately accurate approach is proposed in this paper to determine the so-called "nonlinear" wake aerodynamic configuration by means of the potential theory, where the influence of all three coordinate components of the induced velocity on wake aerodynamic configuration is taken into account to obtain a kind of expanding wake that approximately looks like an actual one. First, the rotor aerodynamic model composed of axial (central), bound, and trailing vortexes is established with the help of the finite aspect wing theory. Then, the Biot-Savart formula for the potential flow theory is used to derive a set of integral equations to evaluate the three components of the induced velocity at any point within the wake. The numerical solution to the integral equations is found, and the loci of all elementary trailing vortex filaments behind the rotor are determined thereafter. Finally, to formulate an actual wind turbine rotor, using the nonlinear wake model, the induced velocity everywhere in the wake, especially that at the rotor blade, is obtained in the case of various tip speed ratios and compared with the wake boundary in a neutral atmospheric boundary layer. Hereby, some useful and referential conclusions are offered for the aerodynamic computation and design of the rotor of the horizontal-axis wind turbine.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
Winding is an important part of the electrical machine and plays a key role in reliability.In this paper,the reliability of multiphase winding structure in permanent magnet machines is evaluated based on the Markov mo...Winding is an important part of the electrical machine and plays a key role in reliability.In this paper,the reliability of multiphase winding structure in permanent magnet machines is evaluated based on the Markov model.The mean time to failure is used to compare the reliability of different windings structure.The mean time to failure of multiphase winding is derived in terms of the underlying parameters.The mean time to failure of winding is affected by the number of phases,the winding failure rate,the fault-tolerant mechanism success probability,and the state transition success probability.The influence of the phase number,winding distribution types,multi three-phase structure,and fault-tolerant mechanism success probability on the winding reliability is investigated.The results of reliability analysis lay the foundation for the reliability design of permanent magnet machines.展开更多
Renewable energy has garnered attention due to the need for sustainable energy sources.Wind power has emerged as an alternative that has contributed to the transition towards cleaner energy.As the importance of wind e...Renewable energy has garnered attention due to the need for sustainable energy sources.Wind power has emerged as an alternative that has contributed to the transition towards cleaner energy.As the importance of wind energy grows,it can be crucial to provide forecasts that optimize its performance potential.Artificial intelligence(AI)methods have risen in prominence due to how well they can handle complicated systems while enhancing the accuracy of prediction.This study explored the area of AI to predict wind-energy production at a wind farm in Yalova,Turkey,using four different AI approaches:support vector machines(SVMs),decision trees,adaptive neuro-fuzzy inference systems(ANFIS)and artificial neural networks(ANNs).Wind speed and direction were considered as essential input parameters,with wind energy as the target parameter,and models are thoroughly evaluated using metrics such as the mean absolute percentage error(MAPE),coefficient of determination(R~2),and mean absolute error(MAE).The findings accentuate the superior performance of the SVM,which delivered the lowest MAPE(2.42%),the highest R~2(0.95),and the lowest MAE(71.21%)compared with actual values,while ANFIS was less effective in this context.The main aim of this comparative analysis was to rank the models to move to the next step in improving the least efficient methods by combining them with optimization algorithms,such as metaheuristic algorithms.展开更多
We propose a novel machine learning approach to reconstruct meshless surface wind speed fields,i.e.,to reconstruct the surface wind speed at any location,based on meteorological background fields and geographical info...We propose a novel machine learning approach to reconstruct meshless surface wind speed fields,i.e.,to reconstruct the surface wind speed at any location,based on meteorological background fields and geographical information.The random forest method is selected to develop the machine learning data reconstruction model(MLDRM-RF)for wind speeds over Beijing from 2015-19.We use temporal,geospatial attribute and meteorological background field features as inputs.The wind speed field can be reconstructed at any station in the region not used in the training process to cross-validate model performance.The evaluation considers the spatial distribution of and seasonal variations in the root mean squared error(RMSE)of the reconstructed wind speed field across Beijing.The average RMSE is 1.09 m s^(−1),considerably smaller than the result(1.29 m s^(−1))obtained with inverse distance weighting(IDW)interpolation.Finally,we extract the important feature permutations by the method of mean decrease in impurity(MDI)and discuss the reasonableness of the model prediction results.MLDRM-RF is a reasonable approach with excellent potential for the improved reconstruction of historical surface wind speed fields with arbitrary grid resolutions.Such a model is needed in many wind applications,such as wind energy and aviation safety assessments.展开更多
A special winding machine with high accuracy has just been developed and applied to the construction of HT-7U Tokamak. It is one of the critical facilities for R & D of HT-7U construction. The machine mainly consi...A special winding machine with high accuracy has just been developed and applied to the construction of HT-7U Tokamak. It is one of the critical facilities for R & D of HT-7U construction. The machine mainly consists of five parts, including a CICC pay-off spool, a fourroller correcting assembly, a four-roller forming/bending assembly, a continuous winding structure and a CNC control system with three-axis AC servo motors. The facility is used for Cable in Conduit Conductor (CICC) magnet fabrication of HT-7U. The main requirements of the winding machine are: continuous winding to reduce joints inside the coils; pre-forming CICC conductor to avoid winding with tension; suitable for all TF & PF coils of various coil shapes and within the dimension limit; improving the configuration tolerance and the special flatness of the CICC conductor. This paper emphasizes on the design and fabrication of the special winding machine for HT-7U. Some analyses and techniques in winding process for trial D-shape coil are also presented.展开更多
Flexible continuous plastic films are used to produce various products, including optical films and packaging materials, because plastic film is suited to use in mass production manufacturing processes. Generally, the...Flexible continuous plastic films are used to produce various products, including optical films and packaging materials, because plastic film is suited to use in mass production manufacturing processes. Generally, the web handling process is applied to convey the plastic film, which is ultimately rewound into a roll using a rewinder. In this case, wrinkles, slippage and other defects may occur if the rewinding conditions are inadequate. In this paper, the authors explain the development of a rewinder system that prevents wound roll defects—primarily starring and telescoping. The system is able to prevent such defects by optimizing the rewinding conditions of tension and nip-load. Based on the optimum design technique, the tension and nip-load are calculated using a 32-bit personal computer. Our experiments have also empirically shown that this rewinder system can prevent roll defects when applying optimized tension and nip-load. Additionally, inexperienced operators can control this system easily.展开更多
This paper reviews the performances of some newly developed reluctance machines with different winding configurations,excitation methods,stator and rotor structures,and slot/pole number combinations.Both the double la...This paper reviews the performances of some newly developed reluctance machines with different winding configurations,excitation methods,stator and rotor structures,and slot/pole number combinations.Both the double layer conventional(DLC-),double layer mutually-coupled(DLMC),single layer conventional(SLC-),and single layer mutually-coupled(SLMC-),as well as fully-pitched(FP)winding configurations have been considered for both rectangular wave and sinewave excitations.Different conduction angles such as unipolar120°elec.,unipolar/bipolar180°elec.,bipolar240°elec.and bipolar360°elec.have been adopted and the most appropriate conduction angles have been obtained for the SRMs with different winding configurations.In addition,with appropriate conduction angles,the 12-slot/14-pole SRMs with modular stator structure is found to produce similar average torque,but lower torque ripple and iron loss when compared to non-modular 12-slot/8-pole SRMs.With sinewave excitation,the doubly salient synchronous reluctance machines with the DLMC winding can produce the highest average torque at high currents and achieve the highest peak efficiency as well.In order to compare with the conventional synchronous reluctance machines(SynRMs)having flux barriers inside the rotor,the appropriate rotor topologies to obtain the maximum average torque have been investigated for different winding configurations and slot/pole number combinations.Furthermore,some prototypes have been built with different winding configurations,stator structures,and slot/pole combinations to validate the predictions.展开更多
Maintenance operations have a critical influence on power gen-eration by wind turbines(WT).Advanced algorithms must analyze large volume of data from condition monitoring systems(CMS)to determine the actual working co...Maintenance operations have a critical influence on power gen-eration by wind turbines(WT).Advanced algorithms must analyze large volume of data from condition monitoring systems(CMS)to determine the actual working conditions and avoid false alarms.This paper proposes different support vector machine(SVM)algorithms for the prediction and detection of false alarms.K-Fold cross-validation(CV)is applied to evaluate the classification reliability of these algorithms.Supervisory Control and Data Acquisition(SCADA)data from an operating WT are applied to test the proposed approach.The results from the quadratic SVM showed an accuracy rate of 98.6%.Misclassifications from the confusion matrix,alarm log and maintenance records are analyzed to obtain quantitative information and determine if it is a false alarm.The classifier reduces the number of false alarms called misclassifications by 25%.These results demonstrate that the proposed approach presents high reliability and accuracy in false alarm identification.展开更多
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.
基金supported by Ningxia Natural Science Foundation Project(2023AAC02088)Liangshan Prefecture 2023 Annual Science and Technology Planning Project(23ZDYF0182).
文摘Under the context of global climate change,the frequent occurrence of strong winds in Guyuan has significantly hindered the development of local facility agriculture.Using hourly meteorological data from the Sanying National Station and the Guyuan Greenhouse Station between April 2024 and April 2025,this study employed machine learning methods to develop wind speed prediction models based on BP neural network,support vector machine,and random forest(referred to as BP,SVM,and RF models),aiming to provide references for local disaster prevention and mitigation.The results indicate that:1)Wind speed at the Guyuan Greenhouse Station exhibits the strongest correlation with that at the National Station(0.489-0.595),followed by temperature and 24-hour precipitation(0.116-0.336).2)The mean absolute error(MAE)of the BP,RF,and SVM models at all heights is below 1.5 m/s,the root mean square error(RMSE)is under 2.1 m/s,and the forecast accuracy(FA)exceeds 75%,indicating satisfactory model performance.Compared to 3 m,the MAE and RMSE of 0.5 m are larger,while the FA is smaller.This indicates that the wind speed of 0.5 m is close to the ground,and is more affected by surface roughness and turbulence effects,resulting in greater randomness and making the model more difficult.3)Based on case analyses of May 10 and May 1,2024,the overall simulation performance ranks as“RF model>SVM model>BP model”;however,the SVM model demonstrates higher accuracy in simulating strong wind events.
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Multi-phase machines are so attractive for electrical machine designers because of their valuable advantages such as high reliability and fault tolerant ability.Meanwhile,fractional slot concentrated windings(FSCW)are well known because of short end winding length,simple structure,field weakening sufficiency,fault tolerant capability and higher slot fill factor.The five-phase machines equipped with FSCW,are very good candidates for the purpose of designing motors for high reliable applications,like electric cars,major transporting buses,high speed trains and massive trucks.But,in comparison to the general distributed windings,the FSCWs contain high magnetomotive force(MMF)space harmonic contents,which cause unwanted effects on the machine ability,such as localized iron saturation and core losses.This manuscript introduces several new five-phase fractional slot winding layouts,by the means of slot shifting concept in order to design the new types of synchronous reluctance motors(SynRels).In order to examine the proposed winding’s performances,three sample machines are designed as case studies,and analytical study and finite element analysis(FEA)is used for validation.
基金State Grid Corporation of China R&D Project(No.5500-202319193A-1-1-ZN).
文摘This paper proposes the application of high-voltage stator-cable windings in superconducting machines,based on the characteristics of strong magnetic fields and large air gaps.Cross-linked polyethylene cable winding can be employed to achieve a rated voltage of 35 kV in direct-current(DC)-field superconducting machines,thereby enabling a direct connection between the superconducting machine and the power grid,eliminating the need for transformers.We first,through finite element analysis,demonstrate that the proposed high-voltage high-temperature superconducting machine not only meets the requirement of a 35 kV-rated voltage,but also exhibits minimal flux leakage,torque fluctuation,and harmonic distortion.We then compare three candidate types to discuss the tradeoff between the multi-group superconducting field winding arrangement and machine performances.We propose inverted trapezoidal superconducting field winding as a promising candidate,because it has minimal superconductivity material usage,the largest safety margin for the superconducting coils(SCs),low thrust ripple,and low total harmonic distortion with the desired 35 kV-rated voltage.Finally,through large-scale design parameter sweeping,we show how we selected the optimal parameters for field winding and validated them by the finite element method.
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.
文摘Support vector machines and a Kalman-like observer are used for fault detection and isolation in a variable speed horizontalaxis wind turbine composed of three blades and a full converter. The support vector approach is data-based and is therefore robust to process knowledge. It is based on structural risk minimization which enhances generalization even with small training data set and it allows for process nonlinearity by using flexible kernels. In this work, a radial basis function is used as the kernel. Different parts of the process are investigated including actuators and sensors faults. With duplicated sensors, sensor faults in blade pitch positions,generator and rotor speeds can be detected. Faults of type stuck measurements can be detected in 2 sampling periods. The detection time of offset/scaled measurements depends on the severity of the fault and on the process dynamics when the fault occurs. The converter torque actuator fault can be detected within 2 sampling periods. Faults in the actuators of the pitch systems represents a higher difficulty for fault detection which is due to the fact that such faults only affect the transitory state(which is very fast) but not the final stationary state. Therefore, two methods are considered and compared for fault detection and isolation of this fault: support vector machines and a Kalman-like observer. Advantages and disadvantages of each method are discussed. On one hand, support vector machines training of transitory states would require a big amount of data in different situations, but the fault detection and isolation results are robust to variations in the input/operating point. On the other hand, the observer is model-based, and therefore does not require training, and it allows identification of the fault level, which is interesting for fault reconfiguration. But the observability of the system is ensured under specific conditions, related to the dynamics of the inputs and outputs. The whole fault detection and isolation scheme is evaluated using a wind turbine benchmark with a real sequence of wind speed.
基金Project supported by the National Basic Research Program of China(No.2014CB046201)the National Natural Science Foundation of China(Nos.51766009,51566011,and 51479114)
文摘Determination of the aerodynamic configuration of wake is the key to analysis and evaluation of the rotor aerodynamic characteristics of a horizontal-axis wind turbine. According to the aerodynamic configuration, the real magnitude and direction of the onflow velocity at the rotor blade can be determined, and subsequently, the aerodynamic force on the rotor can be determined. The commonly employed wake aerodynamic models are of the cylindrical form instead of the actual expanding one. This is because the influence of the radial component of the induced velocity on the wake configuration is neglected. Therefore, this model should be called a "linear model". Using this model means that the induced velocities at the rotor blades and aerodynamic loads on them would be inexact. An approximately accurate approach is proposed in this paper to determine the so-called "nonlinear" wake aerodynamic configuration by means of the potential theory, where the influence of all three coordinate components of the induced velocity on wake aerodynamic configuration is taken into account to obtain a kind of expanding wake that approximately looks like an actual one. First, the rotor aerodynamic model composed of axial (central), bound, and trailing vortexes is established with the help of the finite aspect wing theory. Then, the Biot-Savart formula for the potential flow theory is used to derive a set of integral equations to evaluate the three components of the induced velocity at any point within the wake. The numerical solution to the integral equations is found, and the loci of all elementary trailing vortex filaments behind the rotor are determined thereafter. Finally, to formulate an actual wind turbine rotor, using the nonlinear wake model, the induced velocity everywhere in the wake, especially that at the rotor blade, is obtained in the case of various tip speed ratios and compared with the wake boundary in a neutral atmospheric boundary layer. Hereby, some useful and referential conclusions are offered for the aerodynamic computation and design of the rotor of the horizontal-axis wind turbine.
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Winding is an important part of the electrical machine and plays a key role in reliability.In this paper,the reliability of multiphase winding structure in permanent magnet machines is evaluated based on the Markov model.The mean time to failure is used to compare the reliability of different windings structure.The mean time to failure of multiphase winding is derived in terms of the underlying parameters.The mean time to failure of winding is affected by the number of phases,the winding failure rate,the fault-tolerant mechanism success probability,and the state transition success probability.The influence of the phase number,winding distribution types,multi three-phase structure,and fault-tolerant mechanism success probability on the winding reliability is investigated.The results of reliability analysis lay the foundation for the reliability design of permanent magnet machines.
文摘Renewable energy has garnered attention due to the need for sustainable energy sources.Wind power has emerged as an alternative that has contributed to the transition towards cleaner energy.As the importance of wind energy grows,it can be crucial to provide forecasts that optimize its performance potential.Artificial intelligence(AI)methods have risen in prominence due to how well they can handle complicated systems while enhancing the accuracy of prediction.This study explored the area of AI to predict wind-energy production at a wind farm in Yalova,Turkey,using four different AI approaches:support vector machines(SVMs),decision trees,adaptive neuro-fuzzy inference systems(ANFIS)and artificial neural networks(ANNs).Wind speed and direction were considered as essential input parameters,with wind energy as the target parameter,and models are thoroughly evaluated using metrics such as the mean absolute percentage error(MAPE),coefficient of determination(R~2),and mean absolute error(MAE).The findings accentuate the superior performance of the SVM,which delivered the lowest MAPE(2.42%),the highest R~2(0.95),and the lowest MAE(71.21%)compared with actual values,while ANFIS was less effective in this context.The main aim of this comparative analysis was to rank the models to move to the next step in improving the least efficient methods by combining them with optimization algorithms,such as metaheuristic algorithms.
基金supported by the Strategic Priority Research Program of the Chinese Academy of Sciences(Grant No.XDA19030402)the Key Special Projects for International Cooperation in Science and Technology Innovation between Governments(Grant No.2017YFE0133600the Beijing Municipal Natural Science Foundation Youth Project 8214066:Application Research of Beijing Road Visibility Prediction Based on Machine Learning Methods.
文摘We propose a novel machine learning approach to reconstruct meshless surface wind speed fields,i.e.,to reconstruct the surface wind speed at any location,based on meteorological background fields and geographical information.The random forest method is selected to develop the machine learning data reconstruction model(MLDRM-RF)for wind speeds over Beijing from 2015-19.We use temporal,geospatial attribute and meteorological background field features as inputs.The wind speed field can be reconstructed at any station in the region not used in the training process to cross-validate model performance.The evaluation considers the spatial distribution of and seasonal variations in the root mean squared error(RMSE)of the reconstructed wind speed field across Beijing.The average RMSE is 1.09 m s^(−1),considerably smaller than the result(1.29 m s^(−1))obtained with inverse distance weighting(IDW)interpolation.Finally,we extract the important feature permutations by the method of mean decrease in impurity(MDI)and discuss the reasonableness of the model prediction results.MLDRM-RF is a reasonable approach with excellent potential for the improved reconstruction of historical surface wind speed fields with arbitrary grid resolutions.Such a model is needed in many wind applications,such as wind energy and aviation safety assessments.
文摘A special winding machine with high accuracy has just been developed and applied to the construction of HT-7U Tokamak. It is one of the critical facilities for R & D of HT-7U construction. The machine mainly consists of five parts, including a CICC pay-off spool, a fourroller correcting assembly, a four-roller forming/bending assembly, a continuous winding structure and a CNC control system with three-axis AC servo motors. The facility is used for Cable in Conduit Conductor (CICC) magnet fabrication of HT-7U. The main requirements of the winding machine are: continuous winding to reduce joints inside the coils; pre-forming CICC conductor to avoid winding with tension; suitable for all TF & PF coils of various coil shapes and within the dimension limit; improving the configuration tolerance and the special flatness of the CICC conductor. This paper emphasizes on the design and fabrication of the special winding machine for HT-7U. Some analyses and techniques in winding process for trial D-shape coil are also presented.
文摘Flexible continuous plastic films are used to produce various products, including optical films and packaging materials, because plastic film is suited to use in mass production manufacturing processes. Generally, the web handling process is applied to convey the plastic film, which is ultimately rewound into a roll using a rewinder. In this case, wrinkles, slippage and other defects may occur if the rewinding conditions are inadequate. In this paper, the authors explain the development of a rewinder system that prevents wound roll defects—primarily starring and telescoping. The system is able to prevent such defects by optimizing the rewinding conditions of tension and nip-load. Based on the optimum design technique, the tension and nip-load are calculated using a 32-bit personal computer. Our experiments have also empirically shown that this rewinder system can prevent roll defects when applying optimized tension and nip-load. Additionally, inexperienced operators can control this system easily.
文摘This paper reviews the performances of some newly developed reluctance machines with different winding configurations,excitation methods,stator and rotor structures,and slot/pole number combinations.Both the double layer conventional(DLC-),double layer mutually-coupled(DLMC),single layer conventional(SLC-),and single layer mutually-coupled(SLMC-),as well as fully-pitched(FP)winding configurations have been considered for both rectangular wave and sinewave excitations.Different conduction angles such as unipolar120°elec.,unipolar/bipolar180°elec.,bipolar240°elec.and bipolar360°elec.have been adopted and the most appropriate conduction angles have been obtained for the SRMs with different winding configurations.In addition,with appropriate conduction angles,the 12-slot/14-pole SRMs with modular stator structure is found to produce similar average torque,but lower torque ripple and iron loss when compared to non-modular 12-slot/8-pole SRMs.With sinewave excitation,the doubly salient synchronous reluctance machines with the DLMC winding can produce the highest average torque at high currents and achieve the highest peak efficiency as well.In order to compare with the conventional synchronous reluctance machines(SynRMs)having flux barriers inside the rotor,the appropriate rotor topologies to obtain the maximum average torque have been investigated for different winding configurations and slot/pole number combinations.Furthermore,some prototypes have been built with different winding configurations,stator structures,and slot/pole combinations to validate the predictions.
基金supported financially by the Ministerio de Ciencia e Innovación(Spain)and the European Regional Development Fund under the Research Grant WindSound Project(Ref.:PID2021-125278OB-I00).
文摘Maintenance operations have a critical influence on power gen-eration by wind turbines(WT).Advanced algorithms must analyze large volume of data from condition monitoring systems(CMS)to determine the actual working conditions and avoid false alarms.This paper proposes different support vector machine(SVM)algorithms for the prediction and detection of false alarms.K-Fold cross-validation(CV)is applied to evaluate the classification reliability of these algorithms.Supervisory Control and Data Acquisition(SCADA)data from an operating WT are applied to test the proposed approach.The results from the quadratic SVM showed an accuracy rate of 98.6%.Misclassifications from the confusion matrix,alarm log and maintenance records are analyzed to obtain quantitative information and determine if it is a false alarm.The classifier reduces the number of false alarms called misclassifications by 25%.These results demonstrate that the proposed approach presents high reliability and accuracy in false alarm identification.
