辽宁石油化工大学学报

辽宁石油化工大学学报 ›› 2020, Vol. 40 ›› Issue (6): 78-84.DOI: 10.3969/j.issn.1672-6952.2020.06.014

• 信息与控制工程 • 上一篇    下一篇

基于小波和贝叶斯网络的智能建筑供配电系统故障诊断研究

刘晓琴1,2， 王晨旭1,2， 孙海军1,2， 王千3   

  1. 1.辽宁石油化工大学 信息与控制工程学院，辽宁 抚顺 113001； 2.辽宁石油化工大学 石油化工过程控制国家级实验教学示范中心，辽宁 抚顺 113001； 3.中国石油抚顺石化公司 热电厂，辽宁 抚顺 113008
  • 收稿日期:2019-06-21 修回日期:2020-06-05 出版日期:2020-12-25 发布日期:2020-12-30
  • 作者简介:刘晓琴(1975-)，女，博士，副教授，从事电网故障诊断的研究；E-mail：qinbuluoge@163.com。
  • 基金资助:
    辽宁省教育厅项目（L2017LFW009）。

Research on Fault Diagnosis of Power Supply and Distribution System in Intelligent Building Based on Wavelet and Bayesian Networks

Liu Xiaoqin 1,2， Wang Chenxu 1,2， Sun Haijun 1,2， Wang Qian3   

  1. 1.School of Information and Control Engineering，Liaoning Shihua University， Fushun Liaoning 113001，China； 2.National Experimental Teaching Demonstration Center of Petrochemical Process Control，Liaoning Shihua University， Fushun Liaoning 113001，China； 3.China Petroleum Fushun Petrochemical Company Thermal Power Plant， Fushun Liaoning 113008，China
  • Received:2019-06-21 Revised:2020-06-05 Published:2020-12-25 Online:2020-12-30

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摘要： 为提高智能建筑供配电系统故障诊断效率和精准度，提出了一种基于贝叶斯网络与小波变换的故障诊断方法。首先，从理论上对智能建筑供配电网络拓扑结构进行详细分析，然后利用小波变换原理对故障信息中的开关量和电气量进行筛选重组，最后运用贝叶斯网络对筛选重组后的故障信息进行建模分析，得出故障诊断结果。具体介绍了故障信息中电气量和开关量提取过程，针对现有智能建筑供配电系统的故障特点，给出了相应恢复策略，以IEEE⁃39多节点复杂电力故障系统为例进行仿真研究。结果表明，所提方法的故障诊断结果快速性和准确性更高。研究成果对智能建筑供配电网络的故障诊断研究工作具有重要参考价值。

关键词: 智能建筑, 故障诊断, 贝叶斯网络, 小波变换, 供配电系统

Abstract: In order to improve the efficiency and accuracy of fault diagnosis of power supply and distribution system in intelligent buildings, a fault diagnosis method based on Bayesian network and wavelet transform was proposed. Firstly, the topological structure of power supply and distribution network in intelligent buildings was analyzed in detail in theory. Secondly, the switching and electrical quantities in fault information were filtered and reorganized by wavelet transform. Finally, the fault information after the reorganization was modeled and analyzed by Bayesian network, and the fault diagnosis results were obtained. In this paper, the process of extracting electrical and switching quantities from fault information was introduced in detail. According to the fault characteristics of the existing intelligent building power supply and distribution system, the corresponding recovery strategy was given. IEEE⁃39 multi⁃node complex power fault system is taken as an example, the simulation results show fault diagnosis result of the proposed method is fast and accurate. The research results have important reference value for fault diagnosis research of intelligent building power supply and distribution network.

Key words: Intelligent building, Fault diagnosis, Bayesian network, Wavelet transform, Power supply and distribution system

引用本文

刘晓琴， 王晨旭， 孙海军， 王千. 基于小波和贝叶斯网络的智能建筑供配电系统故障诊断研究[J]. 辽宁石油化工大学学报, 2020, 40(6): 78-84.

Liu Xiaoqin， Wang Chenxu， Sun Haijun， Wang Qian. Research on Fault Diagnosis of Power Supply and Distribution System in Intelligent Building Based on Wavelet and Bayesian Networks[J]. Journal of Liaoning Petrochemical University, 2020, 40(6): 78-84.

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