Filter capacitors play an important role in altern-ating current(AC)-line filtering for stabilizing voltage,sup-pressing harmonics,and improving power quality.However,traditional aluminum electrolytic capacitors(AECs)...Filter capacitors play an important role in altern-ating current(AC)-line filtering for stabilizing voltage,sup-pressing harmonics,and improving power quality.However,traditional aluminum electrolytic capacitors(AECs)suffer from a large size,short lifespan,low power density,and poor reliability,which limits their use.In contrast,ultrafast supercapacitors(SCs)are ideal for replacing commercial AECs because of their extremely high power densities,fast charging and discharging,and excellent high-frequency re-sponse.We review the design principles and key parameters for ultrafast supercapacitors and summarize research pro-gress in recent years from the aspects of electrode materials,electrolytes,and device configurations.The preparation,structures,and frequency response performance of electrode materials mainly consisting of carbon materials such as graphene and carbon nanotubes,conductive polymers,and transition metal compounds,are focused on.Finally,future research directions for ultrafast SCs are suggested.展开更多
A novel configuration performance prediction approach with combination of principal component analysis(PCA) and support vector machine(SVM) was proposed.This method can estimate the performance parameter values of a n...A novel configuration performance prediction approach with combination of principal component analysis(PCA) and support vector machine(SVM) was proposed.This method can estimate the performance parameter values of a newly configured product through soft computing technique instead of practical test experiments,which helps to evaluate whether or not the product variant can satisfy the customers' individual requirements.The PCA technique was used to reduce and orthogonalize the module parameters that affect the product performance.Then,these extracted features were used as new input variables in SVM model to mine knowledge from the limited existing product data.The performance values of a newly configured product can be predicted by means of the trained SVM models.This PCA-SVM method can ensure that the performance prediction is executed rapidly and accurately,even under the small sample conditions.The applicability of the proposed method was verified on a family of plate electrostatic precipitators.展开更多
文摘Filter capacitors play an important role in altern-ating current(AC)-line filtering for stabilizing voltage,sup-pressing harmonics,and improving power quality.However,traditional aluminum electrolytic capacitors(AECs)suffer from a large size,short lifespan,low power density,and poor reliability,which limits their use.In contrast,ultrafast supercapacitors(SCs)are ideal for replacing commercial AECs because of their extremely high power densities,fast charging and discharging,and excellent high-frequency re-sponse.We review the design principles and key parameters for ultrafast supercapacitors and summarize research pro-gress in recent years from the aspects of electrode materials,electrolytes,and device configurations.The preparation,structures,and frequency response performance of electrode materials mainly consisting of carbon materials such as graphene and carbon nanotubes,conductive polymers,and transition metal compounds,are focused on.Finally,future research directions for ultrafast SCs are suggested.
基金Project(9140A18010210KG01) supported by the Departmental Pre-Research Fund of China
文摘A novel configuration performance prediction approach with combination of principal component analysis(PCA) and support vector machine(SVM) was proposed.This method can estimate the performance parameter values of a newly configured product through soft computing technique instead of practical test experiments,which helps to evaluate whether or not the product variant can satisfy the customers' individual requirements.The PCA technique was used to reduce and orthogonalize the module parameters that affect the product performance.Then,these extracted features were used as new input variables in SVM model to mine knowledge from the limited existing product data.The performance values of a newly configured product can be predicted by means of the trained SVM models.This PCA-SVM method can ensure that the performance prediction is executed rapidly and accurately,even under the small sample conditions.The applicability of the proposed method was verified on a family of plate electrostatic precipitators.
