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Tuesday, 11 March 2014
14:15 - 15:45 Real world power curves: A new era for wind resource assessments?
Resource Assessment  


Room: Tramuntana
Session description

In recent years substantial datasets of historic power performance tests have been assembled and these deliver a clear message: wind turbine performance in real world conditions can depart considerably from performance in idealised test conditions. A renewed sense of realism has been awakened in the resource assessment community which has led to the adoption of various methods for transposing an ideal warranted power curve into a real world power curve, i.e. a curve which represents a true central estimate of the power delivered in the site-specific wind speed, air density, turbulence, wind shear, inflow etc. These methods remain embryonic and there is much work left to do. Can the industry converge on accepted and standardised methods? Is it feasible to apply these new methods in everyday resource assessment calculations? Can these methods be shown truly to improve upon the simpler approaches they are superseding?

Learning objectives

  • Understand why the use of real world power curves is important
  • Apply the Inner-Outer range concept
  • Apply the turbulence renormalisation method
  • Apply the rotor equivalent wind speed (RESW) method
  • Apply the power matrix (proxy) method
  • Get an update of the progress of the Power Curve Working Group (PCWG)
Lead Session Chair:
Peter Stuart, RES, United Kingdom

Co-chair(s):
Rozenn Wagner, DTU, Denmark
Tomas Blodau Senvion SE, Germany
Co-authors:
Tomas Blodau (1) F P
(1) REpower Systems, Hamburg, Germany

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Presenter's biography

Biographies are supplied directly by presenters at EWEA 2014 and are published here unedited

Tomas Blodau is Head of the Wind and Site Department in REpower Systems, where he has been working in various positions since 2002. In 1999 he graduated as a Mechanical Engineer from the National University of Ireland. He is also a member of TP Wind.

Abstract

Measurements replace generic assumptions

Introduction

Current industry practice is that generic losses and uncertainties are being applied to account for particular site wind conditions during yield calculations.
This is a coarse approach which can be refined by a more detailed analysis of the actual performance of turbines in the field. This leads to more accurate predictions and improved economics.
This paper covers:
1. Analysis of large dataset of measured power curves
2. Proposal for more detailed approaches
3. Feasibility of methods in everyday practice
4. Magnitude of improvement that can be expected


Approach

Over 350.000 ten minute data sets of wind conditions and turbine power from dozens of IEC 61400 12-1 compliant power curve measurements have been collected and analysed for turbine behaviour under a wide range of conditions. Procedures which will reduce uncertainties and losses for yield calculations are proposed. An approach for a wider power curve guarantee is presented.

Main body of abstract

Results from analysing many power curves are presented and show turbine behaviour over a wide range of wind conditions. For the turbine designs analysed good performance over a wide range of turbulence and shear conditions is evident.
The accuracy of yield calculations can be increased by using the inner outer range approach which divides wind conditions on site into two groups, one group for which the power curve can be applied to 100% and the other group (outer range) where the power curve is applied with a reduced level e.g. 99%. Due to its simplicity this approach can quickly and easily be integrated into existing calculation procedures.
A similar approach can be used for the power curve guarantee.
A more detailed approach is to use the information accumulated from past measurements in predicting yields directly.
In both cases using the more detailed understanding gained from the analysis of many power curve measurements allows for more precise yield predictions, and avoids the necessity for generic assumptions. In many cases this can lead to increases in the yield estimation of some percentage points and thus to a significant increase in project value.


Conclusion

Analysis of many measurements shows that turbines operate well under a very wide range of conditions. The detailed information available from many measurements can be combined to replace generic assumptions and thus reduce losses and uncertainties in yield calculations. Reduced losses and uncertainties increase the project value and ease financing.


Learning objectives
+ Realise value of detailed assessment of raw measurement data
+ Application of power curve measurement analysis results during yield calculations
+ Increase economic value of project by reducing losses and uncertainties.