14:30 - 16:00 Power curves in the real world
Investors acknowledge low risk through accurate predictions of their projects performance. To improve our predictions we need to understand real turbine behavior – real sites and real wind. We will look at new methods for better predicting and reviewing of power curve performance
New methods for more advanced power analysis are presented
• Determine the extent to which power curve degradation is an issue in your windfarm
• Evaluate first power curve measurement results from a spinner measurement as an alternative to the industry standard met mast based
Lead Session Chair:
Tomas Blodau, Senvion, Germany
Kimberley Dewhirst (1) F
(1) Parsons Brinkerhoff, Newcastle upon Tyne, United Kingdom
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Presenter's biographyBiographies are supplied directly by presenters at EWEA 2015 and are published here unedited
Kimberley Dewhirst is a senior wind engineer in WSP | Parsons Brinckerhoff's Renewables Team. She completed her Masters of Energy and Environmental Engineering from Cambridge University, UK and is a Chartered Engineer and member of the Institution of Mechanical Engineers. She has been with the company since 2007 and has undertaken technical advisory services on a large number of development, construction and operational wind projects throughout Europe, Africa and the Middle East. In particular, her expertise is in wind resource assessment.
Analysis of observed windfarm production change over time
Recent studies concerning wind farm degradation have reported alarming degradation rates using European wind farms as the study population. Degradation rates of 16% a decade have been reported in 282 UK wind farms. If degradation rates of 1.6%/year are systemic globally, the financial reputation of wind farm energy assessment will be damaged. This study will expand degradation results to include +25GW of installed capacity in North America with the goal of determining trends of degradation rates by wind farm age, region, name plate capacity and turbine manufacturer class (NPC will be grouped into broad categories and turbine manufacturer names will not be revealed).
Following similar methodology to the European study, degradation rates have been quantified for North American wind facilities based on data from the EIA, AESO and IESO databases representing 26.7 GW of capacity. The results have been broken down by region, wind farm size, manufacturer and turbine design era to identify trends in degradation rates.
Main body of abstract
The study calculated degradation rates for subsets of installed wind capacity in North America (US, Alberta and Ontario). The findings show a wide range of degradation rates depending on name plate capacity, region, turbine era and turbine manufacturer class (no specific turbine manufacturers will be mentioned).
Some key findings include:
1) Wind farms with a name plate capacity <50 MW showed significantly higher degradation rates than wind farms with a name plate capacity between 50-150 MW.
2) Degradation rates have been steadily improving with time. For the first 5 years of production, wind farms installed in the last 5-7 years show almost no degradation whereas wind farms installed between 11-15 years ago show a degradation of >1%/year for the first 5 years of production.
3) In North America, degradation rates are fairly consistent by region except for areas with significant changes in curtailment due to grid congestion and grid build-out. When changes to curtailment are included in degradation rates, positive degradation is possible with grid upgrades.
The results agree with the European based results in certain cases, but also show that the trends in degradation rates have improved substantially with more recent technology. It is important that the Wind Industry builds a strong argument to explain why the high degradation rates observed in first generation wind farms will not apply to later generation wind farms. Characterizing which wind farms suffer from high degradation rates will help analysts assign appropriate degradation losses, explain the poor performance of a subset of wind farms and for wind farm owners to more accurately estimate future energy yields based on their specific wind farm characteristics.
1. What are degradation trends with wind farm age, region, name plate capacity and turbine manufacturer type. (NPC will be grouped into broad categories and turbine manufacturer names will not be revealed)
2. What are reasonable degradation losses given specific characteristics of a wind farm.
3. Are there wind farms which can be classed as “lemons”? How can those badly performing wind farms be identified?