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Analysing the Performance of a Building-Mounted Battery Charging Wind Turbine with Particular Emphasis on the Effect of Yaw Misalignment. Tom Hodkinson, Simon Watson & Paul Rowley
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Analysing the Performance of a Building-Mounted Battery Charging Wind Turbine with Particular Emphasis on the Effect of Yaw Misalignment Tom Hodkinson, Simon Watson & Paul Rowley Centre for Renewable Energy Systems Technology, School of Electronic, Electrical and Systems Engineering, Loughborough University, UK
Outline • The problem – underperformance of urban wind turbines • The experiment • The results • The conclusions
Possible Reasons • High levels of ambient turbulence intensity • Rotor inertia leading to sub-optimal Cp-λ tracking • Non-horizontal flow – steady state or due to turbulence • Increased yaw misalignment
Loughborough University Urban WT • Marlec Rutland Wind Charger 913 • Rated at 90W for 10m/s • Battery charging (12V) • Wind speed & direction measurements • Novel yaw sensor • Investigation of performance in turbulent environment
Turbine and Sensors • Yaw sensor made from an adapted wind vane
Site Meteorological Characteristics Wind Speed Distribution Wind Roses 1st data period – 720 hours 2nd data period – 672 hours Turbulence Intensity
Turbine Performance Published power curve Raw data points Bin averaged Large discrepancy
Yaw Misalignment and Rate of Change of Wind Direction Accurate tracking <10 degrees/second Steep rise in error 10-20 degrees/second
Yaw Error and Power Output Fractional Power Loss (PL) should be approximately related to the cube of the yaw misalignment: From the measured yaw misalignment this gives a predicted energy loss of 19% compared to the actual loss of 41%.
Conclusions • Capacity factor was found to be 3.6% • Yaw error: • <2m/s, large errors • 2m/s-7m/s, the SD of yaw error decreases 32˚ 24˚ • >7m/s, the yaw error starts to increase again • Yaw response: • wind direction changes <10˚/sec, av. yaw error is zero • yaw error increases rapidly for changes >10˚/sec • Overall energy loss (c.f. power curve) found to be 41% with 19% estimated to be due to yaw error