Overvoltage Feature Extraction and Classification Based on Sparse Automatic Coding

LIU Mingjun; ZHANG Han; XIONG Hao; ZHANG Huangjing; CHEN Tie; SIMA Wenxia; HUANG Ming; TANG Juan

doi:10.13296/j.1001-1609.hva.2019.10.012

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Overvoltage Feature Extraction and Classification Based on Sparse Automatic Coding

更新时间：2025-04-26

- Overvoltage Feature Extraction and Classification Based on Sparse Automatic Coding
- High VoltageApparatus Vol. 55, Issue 10, Pages: 70-75(2019)
- 作者机构：
  
  1. 国网重庆市电力公司检修分公司
  2. 重庆大学输配电装备及系统安全与新技术国家重点实验室
- 作者简介：
- 基金信息：
- DOI：10.13296/j.1001-1609.hva.2019.10.012
  CLC： TM76
- Published：2019
- 稿件说明：
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[1]刘明军,张涵,熊浩,张煌竟,陈铁,司马文霞,黄敏,唐娟.基于稀疏自编码的过电压特征提取与分类方法[J].高压电器,2019,55(10):70-75.

LIU Mingjun, ZHANG Han, XIONG Hao, et al. Overvoltage Feature Extraction and Classification Based on Sparse Automatic Coding[J]. High voltageapparatus, 2019, 55(10): 70-75.
[1]刘明军,张涵,熊浩,张煌竟,陈铁,司马文霞,黄敏,唐娟.基于稀疏自编码的过电压特征提取与分类方法[J].高压电器,2019,55(10):70-75. DOI： 10.13296/j.1001-1609.hva.2019.10.012.

LIU Mingjun, ZHANG Han, XIONG Hao, et al. Overvoltage Feature Extraction and Classification Based on Sparse Automatic Coding[J]. High voltageapparatus, 2019, 55(10): 70-75. DOI： 10.13296/j.1001-1609.hva.2019.10.012.

摘要

随着智能电网的发展

识别和处理各种类型的过电压和故障具有越来越重要的意义。文中提出了一种基于稀疏自编码的过电压智能分类识别框架

依靠多层自编码器

实现了电力系统中实测铁磁谐振过电压波形的特征自提取

然后利用Softmax分类器完成精确分类

调节模型参数实现最优分类结果。该框架可应用到实际应用中

为建立过电压智能分类识别系统提供了一个全新的思路与方法。

Abstract

With the development of the smart grid

it is more and more important to identify and deal with interference factors of various types of overvoltage

fault and power quality. In this paper

we present a significant unsupervised practical overvoltage identification system based on stacked autoencoders. The overvoltage waveforms applied in this experiment are collected from the online monitoring system in a substation. This unsupervised practical overvoltage identification system overcomes the problem that the method using manually designed overvoltage features is of low efficiency and unstable. The proposed method can applied in field and provides a new method for overvoltage classification.

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