Smart Distributed Generation Systems Using Artificial Neural Network-Based Event Classification

Rami J. Haddad, Bikiran Guha, Youakim Kalaani, Adel El-Shahat

Research output: Contribution to journalArticlepeer-review

Abstract

Distributed Generation (DG) sources have become an integral part of today’s decentralized power systems. However, current DG systems are mostly passive and do not provide intelligent information to help detect power quality issues. In this paper, a novel and intelligent event classification scheme is proposed to provide DG systems with real-time decision making capabilities. The proposed technique has the ability to provide information to help maintain the quality and reliability of DG systems under various disturbances or operating conditions. This event classification technique was developed using artificial neural networks (ANN) with a pre-defined set of local input parameters. The algorithm is implemented using four parallel ANNs that were designed to operate under a majority vote fusion algorithm representing the final classification output. A total of 310 event cases were generated to test the performance of the proposed technique. Simulation results showed that events were accurately classified within 10 cycles of their occurrences while achieving a 96.21% average classification accuracy.

Original languageAmerican English
JournalIEEE Power and Energy Technology Systems Journal
DOIs
StatePublished - Mar 13 2018

Disciplines

  • Electrical and Computer Engineering

Keywords

  • Artificial Neural Networks
  • Distributed Generation
  • Event Classification
  • Majority Vote
  • Smart grid

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