DTU Wind Energy · 2017-05-09 · DTU Wind Energy, Technical University of Denmark 03 November 2016...
Transcript of DTU Wind Energy · 2017-05-09 · DTU Wind Energy, Technical University of Denmark 03 November 2016...
Experiences, best practice, pitfalls and challengesDTU Wind Energy
03 November 2016DTU Wind Energy, Technical University of Denmark
WASA fundamentals and guiding principles
• Public domain [and free]–67 966 data downloads and 4 800 other downloads
• Traceable and transparent [methodology]
• Industry-standard [tools and procedures]
• Uncertainties assessed [to the extent possible]
• Platform for future development [research based]
Results of the Wind Atlas for South Africa (WASA)
WASA Project Team, 8 April 2014, Cape Town
This is exactly what is needed for educational purposes!
Experiences and best practice2
03 November 2016DTU Wind Energy, Technical University of Denmark
WASA data in education WASA data and materials used for:
• Course exercises
• Course project work
• Master thesis work
• Special course reports
• Teaching materials
• Lectures and talks
• Fundamental for grid planning
• Validation of models– boundary-layer theory– microscale models– mesoscale models– Global Wind Atlas– and many more
Experiences and best practice3
03 November 2016DTU Wind Energy, Technical University of Denmark
Experiences• Data easily accessible and free
• Few copyright considerations
• WASA data are ‘real data’
• Easy to mimic real-life projects
• State-of-the-art data quality
• WASA project well documented
• Tools and software available
• WASA part of wider tradition
• SA climatology challenging
⇒WASA data are excellent for teaching and project work
Experiences and best practice4
03 November 2016DTU Wind Energy, Technical University of Denmark
Best practiceEngineering best practice
• IEC standards
• Measnet guidelines
• WAsP best practices
• WRF best practices
• WASA R&D etc.
Educational best practice
• Well-described methodology
• Lots of material available
• Continuing education too
Experiences and best practice5
03 November 2016DTU Wind Energy, Technical University of Denmark
Pitfalls and challenges• Mean statistics and pdf’s
– Wind power is distributions– Meteorology / climatology– Prognostic / diagnostic
• Reality and model world– Measurements and modelling– Model operational envelopes– Model resolution
• Engineering best practice– Sensitivity analyses– Uncertainty assessment– Reporting practices
• Implementation for teaching– Few challenges
Experiences and best practice6
03 November 2016DTU Wind Energy, Technical University of Denmark
Final remarks about uncertainty• Knowledge of the surface data is very important
Experiences and best practice7