Power distribution outage cause identification with imbalanced data using Artificial Immune Recognition System (AIRS) algorithm

Le Xu*, Mo Yuen Chow, Jon Timmis, Lerov S. Taylor

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

54 Citations (SciVal)

Abstract

Power distribution systems have been significantly affected by many fault causing events. Effective outage cause identification can help expedite the restoration procedure and improve the system reliability. However, the data imbalance issue in many real-world data often degrades the outage cause identification performance. In this paper, artificial immune recognition system (AIRS), an immune-inspired algorithm for supervised classification task is applied to the Duke Energy outage data for outage cause identification using three major causes (tree, animal, and lightning) as prototypes. The performance of AIRS on these real-world imbalanced data is compared with an artificial neural network (ANN). The results show that AIRS can greatly improve the performance by as much as 163% when the data are imbalanced and achieve comparable performance with ANN for relatively balanced data.

Original languageEnglish
Pages (from-to)198-204
Number of pages7
JournalIEEE Transactions on Power Systems
Volume22
Issue number1
DOIs
Publication statusPublished - Feb 2007

Keywords

  • Artificial immune system
  • Classification
  • Data imbalance
  • Neural network
  • Outage cause identification
  • Power distribution systems

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