2023
DOI: 10.1080/02533839.2023.2227877
|View full text |Cite
|
Sign up to set email alerts
|

Optimal design of variable suspension parameters for variable-gauge trains based on the improved CRITIC method

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1
1

Citation Types

0
0
0

Year Published

2023
2023
2024
2024

Publication Types

Select...
4

Relationship

0
4

Authors

Journals

citations
Cited by 4 publications
(2 citation statements)
references
References 17 publications
0
0
0
Order By: Relevance
“…In the CRITIC method, the objective weight of each indicator is calculated by the amount of information contained in the indicator data, which is expressed by the standard deviation and correlation coefficient between indicators. As an improvement of the entropy weight method, it fully expresses the volatility and conflict between indicators and has strong engineering practical value [33]. Therefore, in this paper, the CRITIC method is adopted to further quantify the above indicators to derive the evaluation scores of the PV hosting capacity of the distribution system under typical operating scenarios, and its specific calculation steps are described below:…”
Section: Critic Methodsmentioning
confidence: 99%
“…In the CRITIC method, the objective weight of each indicator is calculated by the amount of information contained in the indicator data, which is expressed by the standard deviation and correlation coefficient between indicators. As an improvement of the entropy weight method, it fully expresses the volatility and conflict between indicators and has strong engineering practical value [33]. Therefore, in this paper, the CRITIC method is adopted to further quantify the above indicators to derive the evaluation scores of the PV hosting capacity of the distribution system under typical operating scenarios, and its specific calculation steps are described below:…”
Section: Critic Methodsmentioning
confidence: 99%
“…An optimal Latin hypercube design ensures that the selected sample points are uniformly distributed in the highdimensional space defined by the six input parameters, allowing for a more comprehensive study of sample point combinations and yielding better experimental results with fewer samples. Thus, an optimal Latin hypercube design was used for data sampling in this study [27].…”
Section: Process Of Analysismentioning
confidence: 99%