In this work, the authors propose the study of a wind speed variable based on the DFAM （double fed asynchronous machine）. The model of the turbine is drawn from the classical equations describing the operation of a ...In this work, the authors propose the study of a wind speed variable based on the DFAM （double fed asynchronous machine）. The model of the turbine is drawn from the classical equations describing the operation of a variable wind speed. The torque generated by the turbine is applied to the DFAM directly connected on the network side and the stator via a bidirectional converter side rotor. This configuration allows velocity variations of ±30% around the synchronous speed and the converter is then sized to one third of the rated power of the machine. The DFAM is controlled by a control vector ensuring operation of the wind turbine power coefficient maximum.展开更多
The direct torque control of the dual star induction motor(DTC-DSIM) using conventional PI controllers is characterized by unsatisfactory performance, such as high ripples of torque and flux, and sensitivity to parame...The direct torque control of the dual star induction motor(DTC-DSIM) using conventional PI controllers is characterized by unsatisfactory performance, such as high ripples of torque and flux, and sensitivity to parametric variations. Among the most evoked control strategies adopted in this field to overcome these drawbacks presented in classical drive, it is worth mentioning the use of the second order sliding mode control(SOSMC) based on the super twisting algorithm(STA) combined with the fuzzy logic control(FSOSMC). In order to realize the optimal control performance, the FSOSMC parameters are adjusted using an optimization algorithm based on the genetic algorithm(GA). The performances of the envisaged control scheme, called G-FSOSMC, are investigated against G-SOSMC, G-PI and BBO-FSOSMC algorithms. The proposed controller scheme is efficient in reducing the torque and flux ripples, and successfully suppresses chattering. The effects of parametric uncertainties do not affect system performance.展开更多
The global COVID-19 pandemic has severely impacted human health and socioeconomic development,posing an enormous public health challenge.Extensive research has been conducted into the relationship between environmenta...The global COVID-19 pandemic has severely impacted human health and socioeconomic development,posing an enormous public health challenge.Extensive research has been conducted into the relationship between environmental factors and the transmission of COVID-19.However,numerous factors influence the development of pandemic outbreaks,and the presence of confounding effects on the mechanism of action complicates the assessment of the role of environmental factors in the spread of COVID-19.Direct estimation of the role of environmental factors without removing the confounding effects will be biased.To overcome this critical problem,we developed a Double Machine Learning(DML)causal model to estimate the debiased causal effects of the influencing factors in the COVID-19 outbreaks in Chinese cities.Comparative experiments revealed that the traditional multiple linear regression model overestimated the impact of environmental factors.Environmental factors are not the dominant cause of widespread outbreaks in China in 2022.In addition,by further analyzing the causal effects of environmental factors,it was verified that there is significant heterogeneity in the role of environmental factors.The causal effect of environmental factors on COVID-19 changes with the regional environment.It is therefore recommended that when exploring the mechanisms by which environmental factors influence the spread of epidemics,confounding factors must be handled carefully in order to obtain clean quantitative results.This study offers a more precise representation of the impact of environmental factors on the spread of the COVID-19 pandemic,as well as a framework for more accurately quantifying the factors influencing the outbreak.展开更多
文摘In this work, the authors propose the study of a wind speed variable based on the DFAM （double fed asynchronous machine）. The model of the turbine is drawn from the classical equations describing the operation of a variable wind speed. The torque generated by the turbine is applied to the DFAM directly connected on the network side and the stator via a bidirectional converter side rotor. This configuration allows velocity variations of ±30% around the synchronous speed and the converter is then sized to one third of the rated power of the machine. The DFAM is controlled by a control vector ensuring operation of the wind turbine power coefficient maximum.
基金supported by The Shaanxi Province Industrial Research Projects(2012K10-10)2015GY068 project categories:Industrial Technology Research of Shaanxi Province project name:The key technology research on coal sampling manipulator full face high speed sampling
基金Project supported by the LEB Research LaboratoryDepartment of Electrical Engineering,University of Batna 2, Algeria。
文摘The direct torque control of the dual star induction motor(DTC-DSIM) using conventional PI controllers is characterized by unsatisfactory performance, such as high ripples of torque and flux, and sensitivity to parametric variations. Among the most evoked control strategies adopted in this field to overcome these drawbacks presented in classical drive, it is worth mentioning the use of the second order sliding mode control(SOSMC) based on the super twisting algorithm(STA) combined with the fuzzy logic control(FSOSMC). In order to realize the optimal control performance, the FSOSMC parameters are adjusted using an optimization algorithm based on the genetic algorithm(GA). The performances of the envisaged control scheme, called G-FSOSMC, are investigated against G-SOSMC, G-PI and BBO-FSOSMC algorithms. The proposed controller scheme is efficient in reducing the torque and flux ripples, and successfully suppresses chattering. The effects of parametric uncertainties do not affect system performance.
基金supported by the Self-supporting Program of Guangzhou Laboratory(SRPG22-007)the National Key Research and Development Program of China(2023YFC3503400)the Gansu Province Intellectual Property Project under Grant(22ZSCQD02).
文摘The global COVID-19 pandemic has severely impacted human health and socioeconomic development,posing an enormous public health challenge.Extensive research has been conducted into the relationship between environmental factors and the transmission of COVID-19.However,numerous factors influence the development of pandemic outbreaks,and the presence of confounding effects on the mechanism of action complicates the assessment of the role of environmental factors in the spread of COVID-19.Direct estimation of the role of environmental factors without removing the confounding effects will be biased.To overcome this critical problem,we developed a Double Machine Learning(DML)causal model to estimate the debiased causal effects of the influencing factors in the COVID-19 outbreaks in Chinese cities.Comparative experiments revealed that the traditional multiple linear regression model overestimated the impact of environmental factors.Environmental factors are not the dominant cause of widespread outbreaks in China in 2022.In addition,by further analyzing the causal effects of environmental factors,it was verified that there is significant heterogeneity in the role of environmental factors.The causal effect of environmental factors on COVID-19 changes with the regional environment.It is therefore recommended that when exploring the mechanisms by which environmental factors influence the spread of epidemics,confounding factors must be handled carefully in order to obtain clean quantitative results.This study offers a more precise representation of the impact of environmental factors on the spread of the COVID-19 pandemic,as well as a framework for more accurately quantifying the factors influencing the outbreak.
