Data-Dirven Method for Wake Effect Analysis on Nacelle Anemometer. Issue 1 (August 2020)

• Record Type: Journal Article
• Authors: Jing, Bo; Qian, Zheng; Chen, Tianyang; Pei, Yan; Kang, Dahai
• Is Part Of: IOP conference series. Volume 555:Issue 1(2020)
• Publication Date: 2020-08
• Subjects: Earth sciences -- Periodicals; Environmental sciences -- Congresses; Environmental sciences -- Periodicals; 550.5
• DOI: 10.1088/1755-1315/555/1/012117
• Languages: English
• ISSNs: 1755-1307

Abstract

Abstract: For most wind turbines, blade wakes affect the measurement of nacelle anemometer, result in the inconsistency between nacelle wind speed (NWS) and free stream wind speed, which seriously affects the power forecasting and performance evaluation of wind turbine. This paper proposes a data-driven method to analyse the wake effect on nacelle anemometer. At first, we use Relevance Vector Machine to establish a site calibration model between Lidar wind speed (LWS) and NWS. After that, we can use the calibrated LWS to replace the free stream wind speed, and wake effect on nacelle anemometer can be evaluated by comparing the calibrated LWS and NWS. Then, Wind Turbine Power Curve (WTPC) is applied to make a detail analysis of wake effect on nacelle anemometer. The experimental results show that wake effect accelerates the air velocity behind the impeller. Therefore, WTPC fitted by NWS is "lower" than the real one. However, SCADA system overcorrects the wake effect, thus WTPC fitted by SCADA data is "higher" than the real power curve.