Wind turbine generation performance monitoring with Jaya algorithm

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

8 Scopus Citations
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Detail(s)

Original languageEnglish
Pages (from-to)1604-1611
Journal / PublicationInternational Journal of Energy Research
Volume43
Issue number4
Online published12 Feb 2019
Publication statusPublished - 25 Mar 2019

Abstract

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.

Research Area(s)

  • Jaya algorithm, multivariate approach, performance monitoring, power curve

Citation Format(s)

Wind turbine generation performance monitoring with Jaya algorithm. / Jin, Rui; Wang, Long; Huang, Chao et al.

In: International Journal of Energy Research, Vol. 43, No. 4, 25.03.2019, p. 1604-1611.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review