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
- 발행연도
- 2021.8
- 수록면
- 1,215 - 1,219 (5page)
- DOI
- 10.5370/KIEE.2021.70.8.1215
이용수
초록· 키워드
오류제보하기
Failure of power facilities causes huge losses and inconvenience to customers. Therefore, Power system companies investing to the facilities for stable power supply. It called ‘Power System Asset Management’ and this means predicting the status of power facilities using data from assets (operation, maintenance, failure, etc.) and establishing an optimal investment plan to improve management efficiency. In the case of power distribution facilities in Korea, old facilities and high risk facilities are replaced through diagnosis of power distribution facilities and evaluation of asset health to minimize customer damage to and reduce investment costs. For Asset status prediction, it need variable input data which affect power facilities and analysis of the impact on power facilities is also essential. However, it is not sufficient for research that comprehensively analyzes the assessment factors of distribution facilities" asset health index and the effects of the facilities to form an health index table. Therefore, in this paper, we study methods for efficient implementation of asset health assessment schemes. Based on big data analysis and machine learning algorithms, we developed a function to automatically extract and assign scores to evaluation items that are highly related to the lifespan of the facility.
목차
Abstract
1. 서론
2. 본론
5. 결론
Reference
참고문헌 (2)
참고문헌 신청
Honhseok Lee / 2021 / Trends of Development system for Power Facility Asset Management / The Proceedings of the Korean Institute of Illuminating and Electrical Installation Engineers 35(2):23~28
Peter A. Flach / 2015 / Precision-Recall-Gain Curves:PR Analysis Done Right / Advances in Neural Information Processing Systems 1(28):838~846
Peter A. Flach / 2015 / Precision-Recall-Gain Curves:PR Analysis Done Right / Advances in Neural Information Processing Systems 1(28):838~846
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UCI(KEPA) : I410-ECN-0101-2021-560-001935312