The keys factor in making wind power one of the main power sources to meet the world’s growing energy demands is the reliability improvement of wind turbines(WTs).However,the eventuality of fault occurrence on WT com...The keys factor in making wind power one of the main power sources to meet the world’s growing energy demands is the reliability improvement of wind turbines(WTs).However,the eventuality of fault occurrence on WT com-ponents cannot be avoided,especially for doubly-fed induction generator(DFIG)based WTs,which are operating in severe environments.The maintenance need increases due to unexpected faults,which in turn leads to higher operating cost and poor reliability.Extensive investigation into DFIG internal fault detection techniques has been carried out in the last decade.This paper presents a detailed review of these techniques.It discusses the methods that can be used to detect internal electrical faults in a DFIG stator,rotor,or both.A novel sorting technique is presented which takes into consideration different parameters such as fault location,detection technique,and DFIG modelling.The main mathematical representation used to detect these faults is presented to allow an easier and faster under-standing of each method.In addition,a comparison is carried out in every section to illustrate the main differences,advantages,and disadvantages of every method and/or model.Some real monitoring systems available in the market are presented.Finally,recommendations for the challenges,future work,and main gaps in the field of internal faults in a DFIG are presented.This review is organized in a tutorial manner,to be an effective guide for future research for enhancing the reliability of DFIG-based WTs.展开更多
A non-destructive technique should be developed for performance analysis of mango fruits because the spongy tissue or internal defects could lower the quality of mango fruit and incur a lack of productivity.In this st...A non-destructive technique should be developed for performance analysis of mango fruits because the spongy tissue or internal defects could lower the quality of mango fruit and incur a lack of productivity.In this study,wavelength selection methods were proposed to identify the range of wavelengths for the classification of defected and healthy mango fruits.Feature selection methods were adopted here to achieve a significant selection of wavelengths.To measure the goodness of themodel,the datasetwas collected using the NIR(Near Infrared)spectroscopy with wavelength ranging from 673 nm–1900 nm.The classification was performed using Euclidean distance measure both in the original feature space and in FLD(Fisher's Linear Discriminant)transformed space.The experimental results showed that the lower range wavelength(673 nm–1100 nm)was the efficient wavelength for the detection of internal defects in mangoes.Further to express the effectiveness of the model,different feature selection techniques were investigated and found that the Fisher's criterion based technique appeared to be the best method for effective wavelength selection useful for classification of defected and healthy mango fruits.The optimal wavelengths were found in the range of 702.72 nm to 752.34 nm using Fisher's criterionwith a classification accuracy of 84.5%.This study showed that NIR systemis a useful technology for the automaticmango fruit assessmentwhich has the potential to be used for internal defects in online sorting,easily distinguishable by those who do not meet minimum quality requirements.展开更多
Adhesion molecule CD146 (100-130kDa) belongs to the immunoglobulin super family and it is originally identified as a biomarker for melanoma. Recently, CD146 is found as
文摘The keys factor in making wind power one of the main power sources to meet the world’s growing energy demands is the reliability improvement of wind turbines(WTs).However,the eventuality of fault occurrence on WT com-ponents cannot be avoided,especially for doubly-fed induction generator(DFIG)based WTs,which are operating in severe environments.The maintenance need increases due to unexpected faults,which in turn leads to higher operating cost and poor reliability.Extensive investigation into DFIG internal fault detection techniques has been carried out in the last decade.This paper presents a detailed review of these techniques.It discusses the methods that can be used to detect internal electrical faults in a DFIG stator,rotor,or both.A novel sorting technique is presented which takes into consideration different parameters such as fault location,detection technique,and DFIG modelling.The main mathematical representation used to detect these faults is presented to allow an easier and faster under-standing of each method.In addition,a comparison is carried out in every section to illustrate the main differences,advantages,and disadvantages of every method and/or model.Some real monitoring systems available in the market are presented.Finally,recommendations for the challenges,future work,and main gaps in the field of internal faults in a DFIG are presented.This review is organized in a tutorial manner,to be an effective guide for future research for enhancing the reliability of DFIG-based WTs.
文摘A non-destructive technique should be developed for performance analysis of mango fruits because the spongy tissue or internal defects could lower the quality of mango fruit and incur a lack of productivity.In this study,wavelength selection methods were proposed to identify the range of wavelengths for the classification of defected and healthy mango fruits.Feature selection methods were adopted here to achieve a significant selection of wavelengths.To measure the goodness of themodel,the datasetwas collected using the NIR(Near Infrared)spectroscopy with wavelength ranging from 673 nm–1900 nm.The classification was performed using Euclidean distance measure both in the original feature space and in FLD(Fisher's Linear Discriminant)transformed space.The experimental results showed that the lower range wavelength(673 nm–1100 nm)was the efficient wavelength for the detection of internal defects in mangoes.Further to express the effectiveness of the model,different feature selection techniques were investigated and found that the Fisher's criterion based technique appeared to be the best method for effective wavelength selection useful for classification of defected and healthy mango fruits.The optimal wavelengths were found in the range of 702.72 nm to 752.34 nm using Fisher's criterionwith a classification accuracy of 84.5%.This study showed that NIR systemis a useful technology for the automaticmango fruit assessmentwhich has the potential to be used for internal defects in online sorting,easily distinguishable by those who do not meet minimum quality requirements.
文摘Adhesion molecule CD146 (100-130kDa) belongs to the immunoglobulin super family and it is originally identified as a biomarker for melanoma. Recently, CD146 is found as
