Study on the reduction of identified parameters of the power load's aggregated motor model with the aim of sustaining its representability

Peng Wang, Yan Hong Wu, Guo Qiang Hu, Rui Ma, Hua Chun Yang, Ren Mu He

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Scopus citations

Abstract

There exists a fatal disadvantage for the integrated induction motor load model of too many parameters, which leads to the difficulty of finding optimal parameters set in the identification process. For this reason, a model parameter reduction method is proposed in this paper, which divides the above 14 parameters into two parts: one fixed parameters part ℘F and the other part ℘C: parameters needing identification. After defining of the representability of the load model, the criterion of rational division of ℘F and ℘C is set as remaining the representability of the load model. Thus the strategy of parameter reduction belongs to the optimization methods. For avoiding the large scale optimizing process, the reduction method of building priority sequence based on parameters' sensitivity is proposed. In the end, a case study is given out on the basis of certain power grid measurement data, which verified the simplicity and effectiveness of the proposed method.

Original languageEnglish
Title of host publicationProceedings - 2005 IEEE/PES Transmission and DistributionConference and Exhibition - Asia and Pacific
Pages1-4
Number of pages4
DOIs
StatePublished - 2005
Externally publishedYes
Event2005 IEEE/PES Transmission and DistributionConference and Exhibition - Asia and Pacific - Dalian, China
Duration: 15 Aug 200518 Aug 2005

Publication series

NameProceedings of the IEEE Power Engineering Society Transmission and Distribution Conference
Volume2005

Conference

Conference2005 IEEE/PES Transmission and DistributionConference and Exhibition - Asia and Pacific
Country/TerritoryChina
CityDalian
Period15/08/0518/08/05

Keywords

  • Integrated induction motor model
  • Load model
  • Reduction
  • Representability
  • Sensitivity

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