Advanced Methods for Wind Turbine Performance Analysis Based on SCADA Data and CFD Simulations

Francesco Castellani (), Ravi Pandit, Francesco Natili, Francesca Belcastro and Davide Astolfi
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Francesco Castellani: Department of Engineering, University of Perugia, Via G. Duranti 93, 06125 Perugia, Italy
Ravi Pandit: Centre for Life-Cycle Engineering and Management (CLEM), School of Aerospace Transport and Manufacturing, Cranfield University, Bedford MK43 0AL, UK
Francesco Natili: Department of Engineering, University of Perugia, Via G. Duranti 93, 06125 Perugia, Italy
Francesca Belcastro: FERA Srl, Piazza Cavour 7, 20121 Milan, Italy
Davide Astolfi: Department of Engineering, University of Perugia, Via G. Duranti 93, 06125 Perugia, Italy

Energies, 2023, vol. 16, issue 3, 1-15

Abstract: Deep comprehension of wind farm performance is a complicated task due to the multivariate dependence of wind turbine power on environmental variables and working parameters and to the intrinsic limitations in the quality of SCADA-collected measurements. Given this, the objective of this study is to propose an integrated approach based on SCADA data and Computational Fluid Dynamics simulations, which is aimed at wind farm performance analysis. The selected test case is a wind farm situated in southern Italy, where two wind turbines had an apparent underperformance. The concept of a space–time comparison at the wind farm level is leveraged by analyzing the operation curves of the wind turbines and by comparing the simulated average wind field against the measured one, where each wind turbine is treated like a virtual meteorological mast. The employed formulation for the CFD simulations is Reynolds-Average Navier–Stokes (RANS). In this work, it is shown that, based on the above approach, it has been possible to identify an anemometer bias at a wind turbine, which has subsequently been fixed. The results of this work affirm that a deep comprehension of wind farm performance requires a non-trivial space–time comparison, of which CFD simulations can be a fundamental part.

Keywords: wind energy; wind turbines; power curve; CFD; SCADA; data analysis; performance 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: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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