计算机科学 ›› 2022, Vol. 49 ›› Issue (11A): 210800209-5.doi: 10.11896/jsjkx.210800209

• 交叉&应用 • 上一篇    下一篇

基于FWA-PSO-MSVM的船舶区域配电电力系统故障诊断

高际航, 张艳   

  1. 上海海事大学物流工程学院电气自动化系 上海 201306
  • 出版日期:2022-11-10 发布日期:2022-11-21
  • 通讯作者: 高际航(18994236969@163.com)
  • 基金资助:
    上海市科技计划项目(20040501200)

Fault Diagnosis of Shipboard Zonal Distribution Power System Based on FWA-PSO-MSVM

GAO Ji-hang, ZHANG Yan   

  1. Electrical Automation Department of Logistics Engineering College,Shanghai Maritime University,Shanghai 201306,China
  • Online:2022-11-10 Published:2022-11-21
  • About author:GAO Ji-hang,born in 1997,master.Her main research interests include electric power system and automation.
  • Supported by:
    Shanghai Science and Technology Program(20040501200).

摘要: 故障发生会极大地影响船舶区域配电电力系统运行的安全性。为了保证船舶的安全运行,针对船舶区域配电电力系统中的10种短路故障,用MATLAB/Simulink建立船舶区域配电电力系统的电力系统仿真模型,利用SMOTE过采样对故障数据进行预处理。利用主成分分析(Principal Components Snalysis,PCA)提取出故障数据中的特征向量,并将其作为多分类支持向量机(Multiclass Support Vector Machine,MSVM)的输入进行故障诊断。为了优化诊断结果,提出烟花粒子群优化算法来优化MSVM的惩罚因子C和核函数参数γ,再与仅粒子群优化算法寻优后MSVM故障分类的结果进行对比。仿真验证结果表明,所提算法具有更高的故障分类准确率和精度。

关键词: 船舶区域配电电力系统, 主成分分析, 烟花算法, 粒子群优化算法, 多分类支持向量机, 故障诊断

Abstract: The occurrence of faults will greatly affect the safety of the shipboard zonal distribution power system.In order to ensure the safe operation of ships,10 types of short-circuit faults in the shipboard zonal distribution power system are focused in this paper.MATLAB/Simulink is used to establish the power system simulation model of the shipboard zonal distribution power system,SMOTE oversampling is adopted to preprocess the fault data.Taking the feature vector extracted by principal component analysis(PCA) in fault data as input of multi-class support vector machine(MSVM) for fault diagnosis.In order to optimize the diagnosis results,a firework particle swarm optimization algorithm is presented to optimize the penalty factor C and the kernel function parameter γ of MSVM,which is compared with the results of MSVM fault classification optimized only by the particle swarm optimization algorithm.Simulation results show that the proposed algorithm has higher fault classification accuracy and precision.

Key words: Shipboard zonal distribution power system, Principal component analysis, Firework algorithm, Particle swarm optimization algorithm, Multi-class support vector machine, Fault diagnosis

中图分类号: 

  • TP391.9
[1]ZHANG M,WANG R Q,CAI Z Y,et al.Phase partition and identification based on kernel entropy component analysis and multi-classsupport vector machines-fireworks algorithm for multi-phase batch process fault diagnosis [J].Transactions of the Institute of Measurement and Control,2020,42(12) :2324-2337.
[2]CHEN X Y,SHI W F,ZHOU J B,et al.Fault location of marine power system based on HHT and BP neural network[J].Marine Electric Technology,2018,38(6):26-30.
[3]DONG Z Y.Application research of Petri network technology in fault diagnosis of ship power system[J].Ship Science and Technology,2017,39(12):168-170.
[4]YAO G,PANG S,YING T,et al.VPSO-SVM based Open-Circuit Faults Diagnosis of Five-Phase Marine Current Generator Sets[J].Energies,2020,13(22):1-28.
[5]ZHANG H L,WANG C H.Short circuit recognition of shippower circuit based on data mining[J].Ship Science and Technology,2019,41(12):85-87.
[6]DONG Y,SHI W F,ZHANG W,et al.Modeling and Fault Simulation of Ship Integrated Power System[J].Marine Electric & Electronic Engineering,2017,37(8):64-68.
[7]CHEN Y,LI P,ZHANG Z J,et al..Online prediction model for power transmission line icing load based on PCA-GA-LSSVM[J].Power System Protection andControl,2019,47(10):110-119.
[8]CAI Y G,CHEN H R,QI Y H.Chaos fireworks algorithm to solve traveling salesman problem[J].Computer Science,2019,46(S1):85-88.
[9]LIU W,LI D K,XU C,et al.Channel allocation algorithm based on particle swarm optimization in emergency communication network [J].Computer Science,2021,48(5):277-282.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!