Neural network based composite load models for power system stability analysis

Ali Keyhani, Wenzhe Lu, Gerald T. Heydt

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations

Abstract

Load modeling is an essential element in power system stability analysis. With the continuing increase of nonlinear and composite loads in power system, the modeling techniques used in the past may no longer be adequate. This paper proposes a methodology for the development of neural network based composite load model which can be applied to power system transient stability analysis. A two-layer neural network has been implemented to estimate the load power (P and Q) from terminal voltage and system frequency. The model has been validated using simulation test bed. The effect of measurement noise on the proposed methodology is also studied.

Original languageEnglish (US)
Title of host publicationProceedings of the 2005 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications, CIMSA 2005
Pages32-37
Number of pages6
DOIs
StatePublished - Dec 1 2005
Event2005 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications, CIMSA 2005 - Giardini, Naxos, Italy
Duration: Jul 20 2005Jul 22 2005

Publication series

NameProceedings of the 2005 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications, CIMSA 2005
Volume2005

Other

Other2005 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications, CIMSA 2005
CountryItaly
CityGiardini, Naxos
Period7/20/057/22/05

Keywords

  • Artificial neural networks
  • Load modeling
  • Power systems
  • Stability analysis

ASJC Scopus subject areas

  • Engineering(all)

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  • Cite this

    Keyhani, A., Lu, W., & Heydt, G. T. (2005). Neural network based composite load models for power system stability analysis. In Proceedings of the 2005 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications, CIMSA 2005 (pp. 32-37). [1522821] (Proceedings of the 2005 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications, CIMSA 2005; Vol. 2005). https://doi.org/10.1109/CIMSA.2005.1522821