Power utility nontechnical loss analysis with extreme learning machine method

被引:253
作者
Nizar, A. H. [1 ]
Dong, Z. Y. [1 ]
Wang, Y. [2 ]
机构
[1] Univ Queensland, Sch Informat Technol & Elect Engn, Brisbane, Qld, Australia
[2] Nanyang Technol Univ, Sch Elect & Elect Engn, Singapore, Singapore
关键词
classification techniques; extreme machine learning (ELM); nontechnical losses (NTL); support vector machine (SVM);
D O I
10.1109/TPWRS.2008.926431
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
This paper presents a new approach to nontechnical loss (NTL) analysis for utilities using the modern computational technique extreme learning machine (ELM). Nontechnical losses represent a significant proportion of electricity losses in both developing and developed countries. The ELM-based approach presented here uses customer load-profile information to expose abnormal behavior that is known to be highly correlated with NTL activities. This approach provides a method of data mining for this purpose, and it involves extracting patterns of customer behavior from historical kWh consumption data. The results yield classification classes that are used to reveal whether any significant behavior that emerges is due to irregularities in consumption. In this paper, ELM and online sequential-ELM (OS-ELM) algorithms are both used to achieve an improved classification performance and to increase accuracy of results. A comparison of this approach with other classification techniques, such as the support vector machine (SVM) algorithm, is also undertaken and the ELM performance and accuracy in NTL analysis is shown to be superior.
引用
收藏
页码:946 / 955
页数:10
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