In almost a century of its development,Chinese ethnology has gradually formed some significant academic paradigms.One of them is the"ethnic research paradigm"which concentrates on summarizing,organizing,and ...In almost a century of its development,Chinese ethnology has gradually formed some significant academic paradigms.One of them is the"ethnic research paradigm"which concentrates on summarizing,organizing,and detailing social and cultural facts using"ethnicity"as its primary unit.This paradigm sees ethnicity as its foundational unit of study.Meanwhile,the"inter-ethnic research paradigm"transcends singular ethnic groups,spotlighting the complex interactions between multiple ethnic groups.It views a multi-ethnic community or region as its core research field,underscoring the nuances of cultural exchange,overlap,and shared experiences.These two paradigms,while distinct,are both invaluable to Chinese ethnology.They don't replace each other but rather work synergistically.Pioneering ethnologist,Fei Xiaotong,masterfully merged these paradigms in his groundbreaking"pluralism and unity"theory,ushering in a refreshed framework for Chinese ethnological studies.This article delves into these foundational paradigms from a historical academic lens,aiming to further refine and enrich the scientific methodologies employed in Chinese ethnology.展开更多
Addressed to the N-k_(1)-k_(2) cascading outages,it is computationally burdensome for the reliable calculation of active and reactive power flows.This paper builds a comprehensive framework with three algorithms,inclu...Addressed to the N-k_(1)-k_(2) cascading outages,it is computationally burdensome for the reliable calculation of active and reactive power flows.This paper builds a comprehensive framework with three algorithms,including the distribution factor(DF),the Newton-Raphson(NR),and the first iteration of NR algorithm(termed as 1J).Classifiers are designed to determine whether the NR algorithm should be employed for accuracy.Classifier features are extracted upon the analytical error of 1J.As reactive power is partially considered in the 1J but neglected in the DF algorithm,the deviation between the solutions is taken as one crucial feature.The support vector machine(SVM)is then utilized for classifier training.As the deep integration of the causal inference and the statistical paradigm,this framework calculates active and reactive power flows rapidly,reliably,and robustly.The effectiveness and robustness are fully validated in three typical IEEE systems.展开更多
文摘In almost a century of its development,Chinese ethnology has gradually formed some significant academic paradigms.One of them is the"ethnic research paradigm"which concentrates on summarizing,organizing,and detailing social and cultural facts using"ethnicity"as its primary unit.This paradigm sees ethnicity as its foundational unit of study.Meanwhile,the"inter-ethnic research paradigm"transcends singular ethnic groups,spotlighting the complex interactions between multiple ethnic groups.It views a multi-ethnic community or region as its core research field,underscoring the nuances of cultural exchange,overlap,and shared experiences.These two paradigms,while distinct,are both invaluable to Chinese ethnology.They don't replace each other but rather work synergistically.Pioneering ethnologist,Fei Xiaotong,masterfully merged these paradigms in his groundbreaking"pluralism and unity"theory,ushering in a refreshed framework for Chinese ethnological studies.This article delves into these foundational paradigms from a historical academic lens,aiming to further refine and enrich the scientific methodologies employed in Chinese ethnology.
基金This work was supported by the China State Grid Corporation Project of the Key Technologies of Power Grid Proactive Support for Energy Transition(No.5100-202040325A-0-0-00).
文摘Addressed to the N-k_(1)-k_(2) cascading outages,it is computationally burdensome for the reliable calculation of active and reactive power flows.This paper builds a comprehensive framework with three algorithms,including the distribution factor(DF),the Newton-Raphson(NR),and the first iteration of NR algorithm(termed as 1J).Classifiers are designed to determine whether the NR algorithm should be employed for accuracy.Classifier features are extracted upon the analytical error of 1J.As reactive power is partially considered in the 1J but neglected in the DF algorithm,the deviation between the solutions is taken as one crucial feature.The support vector machine(SVM)is then utilized for classifier training.As the deep integration of the causal inference and the statistical paradigm,this framework calculates active and reactive power flows rapidly,reliably,and robustly.The effectiveness and robustness are fully validated in three typical IEEE systems.
