A new data-driven condition monitoring method for wind turbines is proposed to prevent a turbine failure in a wind farm. The method works through the power curve model which is built with historical SCADA data. New constraints that established based on Betz’ law and RC model are developed for governing the power curve model. Since abnormal data has a strong impact on the power curve model, the Inner-DBSCAN algorithm is proposed to reject it. In addition, we use an edge recognition method for normal data to form the power curve model. Then, the turbine operation condition can be monitored through the model. Its effectiveness through industrial studies is confirmed, and the time cost of building the power curve model is only 1.08s.
목차
Abstract 1. Introduction 2. The Impact of Abnormal Data on Power Curve 3. Power Curve Model 3.1. Data Preprocessing 3.2. Data Clustering with Inner-DBSCAN Algorithm 3.3. Power Curve Model 4. Anomaly Detection 5. Industrial Case Studies 6. Conclusion Acknowledgments References
키워드
wind turbinecondition monitoringpower curve model
저자
Jianlou Lou [ School of Information Engineering, Northeast DianLi University, Jilin 132012, China ]
Kai Shan [ School of Information Engineering, Northeast DianLi University, Jilin 132012, China ]
Jia Xu [ Long Yuan(Beijing) Wind Power Engineering Technology CO.,LTD. Beijing 100034, China ]
보안공학연구지원센터(IJCA) [Science & Engineering Research Support Center, Republic of Korea(IJCA)]
설립연도
2006
분야
공학>컴퓨터학
소개
1. 보안공학에 대한 각종 조사 및 연구
2. 보안공학에 대한 응용기술 연구 및 발표
3. 보안공학에 관한 각종 학술 발표회 및 전시회 개최
4. 보안공학 기술의 상호 협조 및 정보교환
5. 보안공학에 관한 표준화 사업 및 규격의 제정
6. 보안공학에 관한 산학연 협동의 증진
7. 국제적 학술 교류 및 기술 협력
8. 보안공학에 관한 논문지 발간
9. 기타 본 회 목적 달성에 필요한 사업
간행물
간행물명
International Journal of Control and Automation
간기
월간
pISSN
2005-4297
수록기간
2008~2016
십진분류
KDC 505DDC 605
이 권호 내 다른 논문 / International Journal of Control and Automation Vol.9 No.3