The Switch Cabinet Status Evaluation Model Based on Dynamic Evidence Theory

Chen, Pan; Ma, Tao; Xiang, Wenping; Fan, Chuan; Li, Chengcan; Hu, Xiaoxu

doi:10.1109/icpds51559.2020.9332519

Cited by 2 publications

(2 citation statements)

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“…Liao et al deduced the cable joints' temperature based on a BP neural network to evaluate the status, indicating that the status of cable joints can be obtained by measuring the temperature through the infrared window [9]. Chen et al proposed a status evaluation method based on edge computing and dynamic evidence theory [10]. Some of the existing studies use only a single electrical quantity to realize the overall status evaluation of the switchgear, and some studies do not fully utilize existing multi-dimensional data to conduct comprehensive data mining and analysis.…”

Section: Introductionmentioning

confidence: 99%

Active alarm method for switchgear status based on K-means clustering and multi-dimensional feature quantities

Zhou,

Zhi,

Shang

et al. 2024

J. Phys.: Conf. Ser.

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Switchgear is the critical equipment of the distribution network, and its status evaluation is of great significance to improve the reliability of the distribution network. Addressing the issues of high subjectivity and low accuracy in current methods of status evaluation, we propose an active alarm method for switchgear status based on K-means clustering and multi-dimensional feature quantities. We configure multiple sensors at different locations inside the switchgear to monitor and obtain multi-source data based on IoT sensing technology. We establish a multi-dimensional status dataset of the switchgear and analyze its fluctuation. We acquire the relationship between the number of clusters K and the sum of squares of errors by clustering the obtained multi-dimensional dataset of switchgear through the K-means clustering status evaluation algorithm. We also calculate the sum of squares of errors under different clusters and the optimal number of clustering levels. Finally, we combine a set of switchgear operation data to obtain the switchgear health status by cluster analysis, which verifies the effectiveness of the evaluation method.

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Section: Introductionmentioning

confidence: 99%

Active alarm method for switchgear status based on K-means clustering and multi-dimensional feature quantities

Zhou,

Zhi,

Shang

et al. 2024

J. Phys.: Conf. Ser.

View full text Add to dashboard Cite

show abstract

“…(Kim et al, 2019). Bayesian Networks require the satisfaction of many conditional attributes for use, which is not conducive to practical engineering applications (Chen et al, 2020). ELM has fast training speed, but its robustness is poor (Faiz and Soleimani, 2017;Fang et al, 2023), which cannot meet the requirements for long-term stable diagnosis.…”

Section: Introductionmentioning

confidence: 99%

A novel switchgear state assessment framework based on improved fuzzy C-means clustering method with deep belief network

Xiao,

Guo,

Yang

et al. 2023

Front. Energy Res.

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Due to the problems such as fuzzy state assessment grading boundaries, the recognition accuracy is low when using traditional fuzzy techniques to grade the switchgear state. To address this problem, this paper proposes a switchgear state assessment and grading method based on deep belief network (DBN) and improved fuzzy C-means clustering (IFCM). Firstly, the switchgear state information data are processed by normalization method; then the feature parameters are extracted from the switchgear state information data by using DBN, and finally the extracted feature parameters are categorised according to the condition of switchgear equipment through clustering using IFCM. The experimental results show that the accuracy of the method in assessing the switchgear state under small sample conditions reaches 94, which exceeds the accuracy of other switchgear state assessment grading methods currently in use.

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The Switch Cabinet Status Evaluation Model Based on Dynamic Evidence Theory

Cited by 2 publications

References 1 publication

Active alarm method for switchgear status based on K-means clustering and multi-dimensional feature quantities

Active alarm method for switchgear status based on K-means clustering and multi-dimensional feature quantities

A novel switchgear state assessment framework based on improved fuzzy C-means clustering method with deep belief network

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