Journal
INTERNATIONAL JOURNAL OF ENERGY RESEARCH
Volume 43, Issue 4, Pages 1604-1611Publisher
WILEY
DOI: 10.1002/er.4382
Keywords
Jaya algorithm; multivariate approach; performance monitoring; power curve
Categories
Funding
- Guangdong Provincial Key Laboratory of New and Renewable Research and Development [Y807s61001]
- University of Science and Technology Beijing-National Taipei University of Technology Joint Research Program [TW2018008]
- Fundamental Research Funds for the Central Universities [06500078]
- National Natural Science Foundation of China [71473155]
Ask authors/readers for more resources
Wind turbine (WT) power curves effectively reflect the generation performance of WTs and depict the relationship between the wind speed and the WT power output. This paper aims at developing an effective method for learning the intrinsic representations of WT power curves, which are robust to external environmental changes. Based on the obtained representations, WT generation performance is monitored. In the proposed approach, data of the supervisory control and data acquisition (SCADA) system is employed to derive the representations. Parametric models of WT power curves are developed using the two-parameter and four-parameter logic models. The parameters of these model are identified via Jaya algorithm. To detect the changes of WT power curve model parameters over different time, multivariate control charts are employed. The effectiveness of the proposed WT generation performance monitoring approach is validated based on SCADA data collected from real commercial WTs.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available