Wind Turbine Systematic Yaw Error: Operation Data Analysis Techniques for Detecting It and Assessing Its Performance Impact

Davide Astolfi, Francesco Castellani, Matteo Becchetti, Andrea Lombardi and Ludovico Terzi
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Davide Astolfi: Department of Engineering, University of Perugia, Via G. Duranti, 93-06125 Perugia, Italy
Francesco Castellani: Department of Engineering, University of Perugia, Via G. Duranti, 93-06125 Perugia, Italy
Matteo Becchetti: Department of Engineering, University of Perugia, Via G. Duranti, 93-06125 Perugia, Italy
Andrea Lombardi: Renvico srl, Via San Gregorio 34, 20124 Milano, Italy
Ludovico Terzi: Renvico srl, Via San Gregorio 34, 20124 Milano, Italy

Energies, 2020, vol. 13, issue 9, 1-17

Abstract: The widespread availability of wind turbine operation data has considerably boosted the research and the applications for wind turbine monitoring. It is well established that a systematic misalignment of the wind turbine nacelle with respect to the wind direction has a remarkable impact in terms of down-performance, because the extracted power is in first approximation proportional to the cosine cube of the yaw angle. Nevertheless, due to the fact that in the wind farm practice the wind field facing the rotor is estimated through anemometers placed behind the rotor, it is challenging to robustly detect systematic yaw errors without the use of additional upwind sensory systems. Nevertheless, this objective is valuable because it involves the use of data that are available to wind farm practitioners at zero cost. On these grounds, the present work is a two-steps test case discussion. At first, a new method for systematic yaw error detection through operation data analysis is presented and is applied for individuating a misaligned multi-MW wind turbine. After the yaw error correction on the test case wind turbine, operation data of the whole wind farm are employed for an innovative assessment method of the performance improvement at the target wind turbine. The other wind turbines in the farm are employed as references and their operation data are used as input for a multivariate Kernel regression whose target is the power of the wind turbine of interest. Training the model with pre-correction data and validating on post-correction data, it is estimated that a systematic yaw error of 4 ? affects the performance up to the order of the 1.5% of the Annual Energy Production.

Keywords: wind energy; wind turbines; yaw system; performance monitoring; multivariate regression; operation data analysis (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (11)

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