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Wind turbine generation performance monitoring with Jaya algorithm
Author(s) -
Jin Rui,
Wang Long,
Huang Chao,
Jiang Shancheng
Publication year - 2019
Publication title -
international journal of energy research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.808
H-Index - 95
eISSN - 1099-114X
pISSN - 0363-907X
DOI - 10.1002/er.4382
Subject(s) - scada , turbine , wind power , parametric statistics , power (physics) , wind speed , algorithm , electric power system , engineering , parametric model , control theory (sociology) , computer science , control engineering , control (management) , mathematics , statistics , meteorology , artificial intelligence , mechanical engineering , electrical engineering , physics , quantum mechanics
Summary 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.