Comprehensive Evaluation for Switchgear Based on the Normal Cloud Model and D-S Evidence Theory

JIA Yanan; LIU Dongming; SUI Huibin

doi:10.13296/j.1001-1609.hva.2017.09.042

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Comprehensive Evaluation for Switchgear Based on the Normal Cloud Model and D-S Evidence Theory

更新时间：2025-04-26

- Comprehensive Evaluation for Switchgear Based on the Normal Cloud Model and D-S Evidence Theory
- High VoltageApparatus Vol. 53, Issue 9, Pages: 247-252(2017)
- 作者机构：
  
  1. 山东大学电气工程学院
  2. 国网山东电力集团公司烟台供电公司
- 作者简介：
- 基金信息：
- DOI：10.13296/j.1001-1609.hva.2017.09.042
  CLC： TM591
- Published：2017
- 稿件说明：
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[1]贾亚楠,刘东明,随慧斌.基于正态云模型和D-S证据理论的开关柜运行状态综合评估[J].高压电器,2017,53(09):247-252.

JIA Yanan, LIU Dongming, SUI Huibin. Comprehensive Evaluation for Switchgear Based on the Normal Cloud Model and D-S Evidence Theory[J]. High voltageapparatus, 2017, 53(9): 247-252.
[1]贾亚楠,刘东明,随慧斌.基于正态云模型和D-S证据理论的开关柜运行状态综合评估[J].高压电器,2017,53(09):247-252. DOI： 10.13296/j.1001-1609.hva.2017.09.042.

JIA Yanan, LIU Dongming, SUI Huibin. Comprehensive Evaluation for Switchgear Based on the Normal Cloud Model and D-S Evidence Theory[J]. High voltageapparatus, 2017, 53(9): 247-252. DOI： 10.13296/j.1001-1609.hva.2017.09.042.

摘要

以高压开关柜为研究对象

提出了一种基于正态云模型和D-S证据理论的分层评估算法。通过对影响开关柜运行状态的指标进行聚类分层

运用正态云模型确定各指标与各评价等级的关联度

并利用改进的模糊算法进行第1层评估

在此基础上运用D-S证据理论对开关柜整体运行状态进行评估。采用KYN28A-12型号某开关柜的一次检测数据为例

通过实例验证证明了评估算法的有效性。该评估模型结构清晰

评价指标易于得到

为开关柜状态评估提供了一种新思路。

Abstract

Taking the high-voltage switchgear cabinet as the studying object

a hierarchical evaluation method based on normal cloud model and D-S evidential theory is proposed. By hierarchical clustering indicators of the switchgear status

normal cloud model is used to determine the correlation degree of indicators and evaluation level. Also the improved fuzzy algorithm is used to evaluate the first layer. On the basis

D-S evidential theory is used to assess the overall state of the switchgear. As an example

test data of the KYN28 A-12 model switchgear is analyzed. The simulation result demonstrates the effectiveness of the assessment algorithm. The assessment model is clear and doesn ’t need lots of historical data. It offers a new thinking to assess the operating state of the high-voltage switchgear.

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