A Multidimensional Adaptive Entropy Cloud-Model-Based Evaluation Method for Grid-Related Actions

Xiaoling Chen, Weiwen Zhan, Xingrui Li, Jingkai Guo and Jianyou Zeng ()
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Xiaoling Chen: School of Art and Media, China University of Geosciences, Wuhan 430074, China
Weiwen Zhan: School of Mechanical Engineering and Electronic Information, China University of Geosciences, Wuhan 430074, China
Xingrui Li: School of Mechanical Engineering and Electronic Information, China University of Geosciences, Wuhan 430074, China
Jingkai Guo: School of Mechanical Engineering and Electronic Information, China University of Geosciences, Wuhan 430074, China
Jianyou Zeng: School of Art and Media, China University of Geosciences, Wuhan 430074, China

Energies, 2022, vol. 15, issue 22, 1-18

Abstract: Smart grid training system needs to evaluate actions during power grid operations in order to complete training for relevant personnel. The commonly used action evaluation methods are difficult for evaluating fine actions during power grid operations, and the evaluation results are subjective. The use of an effective method to evaluate the actions of the power grid operation is important for improving the smart grid training system, enhancing the skills of the trainers, and ensuring the personal safety of operators. This paper proposes a cloud attention mechanism and an evaluation method of grid-related actions based on a multidimensional adaptive entropy cloud model to complete the evaluation of fine actions in the grid’s operation process. Firstly, the OpenCV technique is used to obtain the data related to hand actions during grid operation and to extract the action features to complete the construction of multiscale date sets; then, the adaptive entropy weight matrix at different scales is constructed based on multiscale data sets using the cloud attention mechanism, and the basic cloud model is generated from original hand-action feature data; finally, the multidimensional adaptive entropy cloud model is constructed by the adaptive entropy weight matrix and the basic cloud model, and the multidimensional adaptive entropy cloud model obtained is compared with the multidimensional adaptive entropy cloud model generated based on the standard action features in the same space to obtain the evaluation level of the hand action. The results show that the evaluation method of grid-related actions based on the multidimensional adaptive entropy cloud model can solve the mutual mapping problem between quantitative indicators and qualitative evaluation results in the evaluation of grid operation processes relatively well, and it effectively solves the subjectivity of the weight assignment of evaluation indicators, which can be used for the evaluation of fine actions in the grid’s operation processes.

Keywords: power grid training; cloud model; cloud attention mechanism; action evaluation (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
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