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Indepency to enhance accuracy on power curve measurements
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Jose Javier Ripa UL, Spain INDEPENCY TO ENHANCE ACCURACY ON POWER CURVE MEASUREMENTS Abstract ID: 216 Poster code: PO.035 | Download poster: ![]() |
Presenter's biography
Biographies are supplied directly by presenters at WindEurope 2016 and are published here uneditedJose Javier Ripa is the Regional Manager for Spain and Latin America in UL WIND (DEWI) and Site Leader of the Spanish office with more than 10 years of experience in consultancy and testing services covering power curve and loads testing, turbine inspections, resource assessment and due diligence. José Javier was, during more than 12 years, professor at the Public University of Navarra teaching Wind Energy and has commanded several Technical Seminars across Latin America.
Abstract
Indepency to enhance accuracy on power curve measurements
Introduction
Most of the wind farm owners in the world struggle with the decision of executing power curve tests on their wind farms. The investment is not marginal and the return of investment is not always evident.
Uncertainties (often incorporated into the contractual guaranty algorithms) let many stakeholders be sceptic when uncertainties are higher than 5% as power-curve test results are hardly useable as truthful inputs for the financial models or guarantee claims.
Approach
Best practices have clear impact on accuracy, and tiny investment on high-class tests will pay off. However there are powerful additional and powerful resources for uncertainty reduction that are rarely considered: averaging, correlation and independency.
How can we make use of those concepts? The answer is redundancy (of measurement systems, increase of the population under test, etc). Redundancies will derive into less uncertainty if there is certain degree of independency
Main body of abstract
The idea is simple, but requires a precise characterization of correlation factors. With this aim, DEWI, in cooperation with ACCIONA ENERGY is working in a research project in the wind farm Gostyn II located in Poland. With the results of the different test combinations (with variety of degrees of dependency and different averaging options), uncertainty contributions for averaged results will be more accurately determined. The measurement is ongoing and the results are expected by mid of summer.
Conclusion
The correct application of those ideas is as a powerful tool to decrease uncertainties without inflating costs. But it is even better to know that this is not only limited to power curve tests but all the wind energy different stages (resource, operations, etc.). Facing these questions rigor is key as it has the potential to revolution the wind energy business.
Learning objectives
Delegates will realize and learn that there are tangible ways of reducing uncertainty by applying concise and mathematical concepts. The results that will be presented will allow to debrief the different assumptions and let the delegates to design their own tests and evaluations to enhance the accuracy of their measurements and assessments.
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