TY - JOUR
T1 - Power distribution outage cause identification with imbalanced data using Artificial Immune Recognition System (AIRS) algorithm
AU - Xu, Le
AU - Chow, Mo Yuen
AU - Timmis, Jon
AU - Taylor, Lerov S.
N1 - Funding Information:
Manuscript received July 20, 2006; revised September 20, 2006. This work was supported in part by the National Science Foundation under Grant ECS-0245383 and Grant No. IIS-0426852. Paper no. TPWRS-00458-2006. L. Xu and M.-Y. Chow are with the Department of Electrical and Computer Engineering, North Carolina State University, Raleigh, NC 27695 USA (e-mail: [email protected]; [email protected]). J. Timmis is with the Department of Computer Science and Department of Electronics, University of York, Heslington, York, U.K. (e-mail: jtimmis@cs. york.ac.uk). L. S. Taylor is with Duke Energy, Distribution Standard, Charlotte, NC 28201 USA (e-mail: [email protected]). Digital Object Identifier 10.1109/TPWRS.2006.889040
PY - 2007/2
Y1 - 2007/2
N2 - 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.
AB - 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.
KW - Artificial immune system
KW - Classification
KW - Data imbalance
KW - Neural network
KW - Outage cause identification
KW - Power distribution systems
UR - http://www.scopus.com/inward/record.url?scp=33947327869&partnerID=8YFLogxK
U2 - 10.1109/TPWRS.2006.889040
DO - 10.1109/TPWRS.2006.889040
M3 - Article
AN - SCOPUS:33947327869
SN - 0885-8950
VL - 22
SP - 198
EP - 204
JO - IEEE Transactions on Power Systems
JF - IEEE Transactions on Power Systems
IS - 1
ER -