Abstract
• This article presents a method to evaluate the Manufacturer Power Curve (MPC) by estimating any potential generic performance losses in the "knee" of the power curve. This simple method allows to verify the reliability of the power curve as provided by the wind turbine Original Equipment Manufacturer (OEM) and to estimate the expected losses for a given wind climate either before or during the turbine's operations.
• The method makes use of the established Turbulence Normalisation procedure as described in Annex M of the IEC standard 61400-12-1.
• Performance losses are estimated for nearly 300 MPC's which reveal large variations across various OEMs.
Methodology
Performance losses
• The methodology has been utilised to estimate the performance losses for nearly 300 MPC's from 5 different OEMs, assuming Iref = 10% and a Weibull distribution with A = 8 and k = 2.
• The boxplots below display the performance losses' distribution by OEM.
Validation
• As a validation, the theoretical power curve for Iref is compared to the related MPC and to measure power curves.
• Below, two examples are shown: one with a performance gain (left) and one with a performance loss (right). Both show a good agreement between the measured and theoretical power curves.
Conclusions
• Not all Manufacturer Power Curves are equally reliable and can have a consistent over- or underperformance in the knee of the power curve.
• These losses can be estimated only based on the MPC using the method described above. The method applies the Turbulence Normalisation steps as outlined in Annex M of the IEC 61400-12-1 by applying these directly on the MPC instead of the measured power curve.
Accurate power curve verification for enhanced monitoring & energy yield studies
At 3E, we have developed this methodology to assess wind turbine performance losses with high precision.
This approach is fully implemented in our SynaptiQ Wind Analytics application. Through advanced analytics, our SynaptiQ app assesses sensor reliability and compares corrected power curve with contracted ones. This enables SynaptiQ users to detect and correct turbine underperformance early, potentially saving up to €20K per year per turbine.
In addition, this methodology is used by our wind consultants for estimating performance losses in pre-construction energy yield studies.
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Acknowledgments
This research was carried out by David Schillebeeckx, Patrick Hoebeke and Louise Hanne from 3E, Brussels.
This research was funded in the context of the Energy Transition Fund project POSEIDON.
References
• IEC, IEC 61400-12-1, Wind energy generation systems - Part 12-1: Power performance measurements of electricity producing wind turbines, 2017-03 Edition 2.0
• Saint-Drenan, Yves-Marie, et al. "A parametric model for wind turbine power curves incorporating environmental conditions." Renewable Energy 157 (2020): 754-768.
