电流迁移态序列特征模型与获取方法研究

张慧; 刘帅; 杨泽丞; 王萍; 程红梅; 张振亚

doi:10.13568/j.cnki.651094.651316.2023.06.29.0001

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电流迁移态序列特征模型与获取方法研究

更新时间：2026-01-21

- 电流迁移态序列特征模型与获取方法研究
- Journal of Xinjiang University (Natural Science Edition in Chinese and English) Vol. 41, Issue 1, Pages: 37-51(2024)
- 作者机构：
  
  1. 智能建筑与建筑节能安徽省重点实验室安徽建筑大学
  2. 安徽建筑大学电子与信息工程学院
  3. 安徽建筑大学经济与管理学院
- 作者简介：
- 基金信息：
- DOI：10.13568/j.cnki.651094.651316.2023.06.29.0001
  CLC： TM73
- Published：2024
- 稿件说明：
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[1]张慧,刘帅,杨泽丞,等.电流迁移态序列特征模型与获取方法研究[J].新疆大学学报(自然科学版)(中英文),2024,41(01):37-51.
[1]张慧,刘帅,杨泽丞,等.电流迁移态序列特征模型与获取方法研究[J].新疆大学学报(自然科学版)(中英文),2024,41(01):37-51. DOI： 10.13568/j.cnki.651094.651316.2023.06.29.0001.

DOI：10.13568/j.cnki.651094.651316.2023.06.29.0001.

摘要

基于使用智能插座对电路中的电流高频次监测、获得用户用电态势数据，对电流迁移态序列、电流迁移态序列特征的一元回归模型进行了研究．基于电流稳态序列ε片段、电流稳态序列片段，设计了电流迁移态序列的构造方法，提出使用一元回归模型描述每一个电流迁移态序列，实现将长度不定的电流迁移态序列到维数固定的电流迁移态序列特征空间的映射．进一步，设计了基于微环境的粒子群优化算法（Microenvironment based Particle Swarm Optimization

MPSO），实现了电流迁移态序列一元回归特征的优化．实验表明：使用所提电流迁移态序列特征进行电器状态识别，平均可以达到97.93%的准确率，且相较PSO算法与CAPSO算法，MPSO算法在使用较少的粒子数达到与这两种算法一致精度的同时，使用时间显著降低．

Abstract

In this paper

based on the high-frequency monitoring of current in the circuit via smart sockets and electrical situation data of the customer obtained

the current transition sequence and the univariate regression model for the feature of the current transition sequence are proposed. Based on the ε-fragment for the current steady sequence and fragment for the current stable sequence

a mining method for current transition sequence discovery is designed. And further

a univariate regression model is proposed to describe each current transition sequence. And with the univariate regression model

each current transition sequence in variable length is mapped into the feature space for the current transition sequence with fixed dimension. Furthermore

a microenvironmentbased particle swarm optimization(MPSO) algorithm is given to optimize the univariate regression features for the current transition sequences. The experimental results show that using the current transition sequence features proposed in this paper for electrical device state recognition can achieve an average accuracy of 97.93%. Compared with the PSO algorithm and CAPSO algorithm

the MPSO algorithm used in this paper achieves the same accuracy with fewer particles and significantly reduces usage time.

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