지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 저널정보
- 대한전기학회 Journal of Electrical Engineering & Technology Journal of Electrical Engineering & Technology Vol.3 No.2
- 발행연도
- 2008.6
- 수록면
- 167 - 176 (10page)
이용수
초록· 키워드
오류제보하기
Vulnerability assessment of power systems is important so as to determine their ability to continue to provide service in case of any unforeseen catastrophic contingency such as power system component failures, communication system failures, human operator error, and natural calamity. An approach towards the development of on-line power system vulnerability assessment is by means of using an artificial neural network (ANN), which is being used successfully in many areas of power systems because of its ability to handle the fusion of multiple sources of data and information. An important consideration when applying ANN in power system vulnerability assessment is the proper selection and dimension reduction of training features. This paper aims to investigate the effect of using various feature extraction methods on the performance of ANN as well as to evaluate and compare the efficiency of the proposed feature extraction method named as neural network weight extraction. For assessing vulnerability of power systems, a vulnerability index based on power system loss is used and considered as the ANN output. To illustrate the effectiveness of ANN considering various feature extraction methods for vulnerability assessment on a large sized power system, it is verified on the IEEE 300-bus test system.
목차
Abstract
1. Introduction
2. Artificial Neural Network
3. Data Generation
4. Feature Extraction Methods
5. Results and Discussion
6. Conclusion
References
참고문헌 (14)
참고문헌 신청
1. [학술지(정기간행물)] - V. L. Paccar / 2002 / Artificial neural networks for solving the power flow problem in electric power systems / Electric Power Systems Research Elsevier 62 : 139 ~ 144
2. [학술지(정기간행물)] - C. A. Jensen / 2001 / Power system security assessment using neural networks: feature selection using Fisher discrimination / IEEE Transaction on Power System 16 (1) : 757 ~ 763
3. [학술지(정기간행물)] - H. Sawhney / 2004 / On-line transient stability assessment using artificial neural network / Proceedings of the IEEE on Large Engineering Systems Conference 2 : 76 ~ 80
4. [학술대회논문] - M. Delimar / 2003 / Artificial neural networks in power system topology recognition / Proceedings of the IEEE EUROCON Computer as a Tool : 287 ~ 291
5. [학술지(정기간행물)] - S. C. Tan / 2004 / Application of an adaptive neural network with symbolic rule extraction to fault detection and diagnosis in a power generation plant / IEEE Transaction on Energy Conversion 19 (2) : 369 ~ 377
2. [학술지(정기간행물)] - C. A. Jensen / 2001 / Power system security assessment using neural networks: feature selection using Fisher discrimination / IEEE Transaction on Power System 16 (1) : 757 ~ 763
3. [학술지(정기간행물)] - H. Sawhney / 2004 / On-line transient stability assessment using artificial neural network / Proceedings of the IEEE on Large Engineering Systems Conference 2 : 76 ~ 80
4. [학술대회논문] - M. Delimar / 2003 / Artificial neural networks in power system topology recognition / Proceedings of the IEEE EUROCON Computer as a Tool : 287 ~ 291
5. [학술지(정기간행물)] - S. C. Tan / 2004 / Application of an adaptive neural network with symbolic rule extraction to fault detection and diagnosis in a power generation plant / IEEE Transaction on Energy Conversion 19 (2) : 369 ~ 377
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