Effect of Control Algorithms on Fixed Pitch Wind Turbine Generators

1990 ◽  
Vol 112 (3) ◽  
pp. 147-152 ◽  
Author(s):  
M. P. Rizzi ◽  
D. M. Auslander

The effect of control upon wind turbine performance is described using actual wind speed data from a wind farm in California. The algorithms studied are currently in use by some turbine manufacturers. The algorithms were studied using a computer model of a fixed pitch, utility connected wind turbine with an induction generator. The algorithm efficiency was found to be robust over a wide range of parameters. However, the number of start/stops varied greatly with different algorithms and parameters.

2013 ◽  
Vol 14 (3) ◽  
pp. 207-218 ◽  
Author(s):  
Kazuki Ogimi ◽  
Shota Kamiyama ◽  
Michael Palmer ◽  
Atsushi Yona ◽  
Tomonobu Senju ◽  
...  

Abstract In order to solve the problems of global warming and depletion of energy resource, renewable energy systems such as wind generation are getting attention. However, wind power fluctuates due to variation of wind speed, and it is difficult to perfectly forecast wind power. This paper describes a method to use power forecast data of wind turbine generators considering wind power forecast error for optimal operation. The purpose in this paper is to smooth the output power fluctuation of a wind farm and to obtain more beneficial electrical power for selling.


2007 ◽  
Vol 158 (4) ◽  
pp. 31-41 ◽  
Author(s):  
Tomonobu Senjyu ◽  
Ryosei Sakamoto ◽  
Naomitsu Urasaki ◽  
Toshihisa Funabashi ◽  
Hideki Fujita ◽  
...  

2021 ◽  
pp. 0309524X2110351
Author(s):  
Abeba Debru ◽  
Mulu Bayray ◽  
Marta Molinas

The objective of this paper was to assess the performance of the Adama-II Wind Farm in comparison to the feasibility study. Using 1-year mast data, the site potential was reassessed by WAsP software and the performance of wind turbine generators was assessed by 2 years of SCADA data. The obtained mean annual wind speed and power density were 7.75 m/s, and 462 W/m2 while in the feasibility study, 9.55 m/s, and 634.6 W/m2, which resulted in 18.8%, and 27.1% deviations respectively. The prevailing and secondary wind directions obtained were ENE and NE with 35.7% and 19.1% while, in the feasibility study, ENE with 36.5% and E with 17.3%. From the SCADA data, the Capacity factor, Annual Energy Production (AEP), and Availability of wind turbines were determined as 30.5%, 398 GWh, and 95.1%. The reasons for the deviation were difference in long-term correction data and weather conditions during study period.


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