Assessment of Gas Insulation State in GIS Equipment Based on Relevance Vector Machine

SONG Renjie; ZHAO Meng; GUAN Xiaozhuo; DU Keqiong; LIU Jing

doi:10.13296/j.1001-1609.hva.2016.12.039

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Assessment of Gas Insulation State in GIS Equipment Based on Relevance Vector Machine

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- Assessment of Gas Insulation State in GIS Equipment Based on Relevance Vector Machine
- High VoltageApparatus Vol. 52, Issue 12, Pages: 237-243(2016)
- 作者机构：
  
  1. 东北电力大学信息工程学院
  2. 国网吉林省电力有限公司吉林供电公司信息通信分公司
- 作者简介：
- 基金信息：
- DOI：10.13296/j.1001-1609.hva.2016.12.039
  CLC： TM855
- Published：2016
- 稿件说明：
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[1]宋人杰,赵萌,关潇卓,杜轲琼,刘静.基于相关向量机的GIS气体绝缘状态评估[J].高压电器,2016,52(12):237-243.

SONG Renjie, ZHAO Meng, GUAN Xiaozhuo, et al. Assessment of Gas Insulation State in GIS Equipment Based on Relevance Vector Machine[J]. High voltageapparatus, 2016, 52(12): 237-243.
[1]宋人杰,赵萌,关潇卓,杜轲琼,刘静.基于相关向量机的GIS气体绝缘状态评估[J].高压电器,2016,52(12):237-243. DOI： 10.13296/j.1001-1609.hva.2016.12.039.

SONG Renjie, ZHAO Meng, GUAN Xiaozhuo, et al. Assessment of Gas Insulation State in GIS Equipment Based on Relevance Vector Machine[J]. High voltageapparatus, 2016, 52(12): 237-243. DOI： 10.13296/j.1001-1609.hva.2016.12.039.

摘要

文中提出了基于多参量的GIS设备气体绝缘状态评估方法。该方法首先对影响GIS设备绝缘状况的因素进行了详细的分析

确定影响该气体绝缘状态的主要因素。然后利用气体组成成分、温度、湿度、压力、纯度作为因素集

对其进行绝缘评估。最后

采用一对一的分类方法建立基于相关向量机的GIS设备气体绝缘状态评估模型

并将GIS设备气体的绝缘状态分为"正常"、"注意"、"异常"和"故障"4类

利用训练样本对评估模型进行训练

进而实现GIS设备气体绝缘状态的评估。实例分析表明

相对传统方法

该方法在GIS设备气体的绝缘状态分类中具有较高的准确率。

Abstract

A multi-parameter assessment method of the gas insulation state in GIS equipment is proposed. Factors affecting the insulation conditions of GIS equipment are analyzed in detail

and the most influencing factors determined. Assessment of insulation using a parameter set of gas composition

temperature

humidity

pressure and purity is carried out. Finally

a one-on-one（ORO）classification method is used to establish a GIS insulation state model based on a relevance vector machine（RVM）. The GIS gas insulation condition can be divided into four categories corresponding to "normal"

"attention"

"abnormal"and "malfunction". The model is trained with training samples before it is used for the evaluation of gas insulated equipment state. A use case analysis shows that the method has better accuracy in the classification of insulation state of the GIS equipment.

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