Fast and reliable estimation of composite load model parameters using analytical similarity of parameter sensitivity

Jae Kyeong Kim, Kyungsung An, Jin Ma, Jeonghoon Shin, Kyung Bin Song, Jung Do Park, Jung Wook Park, Kyeon Hur

Research output: Contribution to journalArticle

27 Citations (Scopus)

Abstract

This paper proposes a computationally efficient technique for estimating the composite load model parameters based on analytical similarity of parameter sensitivity. When the model parameters are updated in the optimization procedure to best fit the actual load dynamics, i.e., measurements, parameters of similar sensitivity representation in the given mathematical model structure are updated in the same manner at every iterative step. This research allows for practically reducing the number of load model parameters to be identified in the estimation process and the overall computational cost while preserving the desired complexity and accuracy of the original model. This approach consequently facilitates the parameter estimation in the optimization process and helps manage increased number of parameters often criticized for adopting the dynamic composite load model via measurement-based approach. Case studies for the real power system demonstrate the computational efficiency and intact accuracy of the proposed method with reference to the existing methods of estimating all the parameters of the given composite load model independently.

Original languageEnglish
Article number7063273
Pages (from-to)663-671
Number of pages9
JournalIEEE Transactions on Power Systems
Volume31
Issue number1
DOIs
Publication statusPublished - 2016 Jan

All Science Journal Classification (ASJC) codes

  • Energy Engineering and Power Technology
  • Electrical and Electronic Engineering

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