Neural network approach for estimation of load composition

J. Duan, D. Czarkowski, Z. Zabar

Research output: Contribution to journalConference articlepeer-review

Abstract

A neural network methodology to solve the problem of estimation of modern electrical load distribution in typical commercial and residential areas is proposed in this paper. The inputs for the neural network are harmonic characteristics of each type of typical loads and possible combinations of these loads. The output is the estimation of load composition. The Multi-Layer Feed-Forward Back-Propagation neural network and Elman neural network are used in the paper to calculate the load distribution. A case study of a Manhattan area and two practical tests are presented to demonstrate the feasibility of this approach. The new method will be useful for electrical load monitoring and harmonic reliability assessment in the new utility environment.

Original languageEnglish (US)
Pages (from-to)V-988-V-991
JournalProceedings - IEEE International Symposium on Circuits and Systems
Volume5
StatePublished - 2004
Event2004 IEEE International Symposium on Cirquits and Systems - Proceedings - Vancouver, BC, Canada
Duration: May 23 2004May 26 2004

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

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