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http://dx.doi.org/10.3795/KSME-A.2012.36.9.1065

Optimization of Wind Turbine Pitch Controller by Neural Network Model Based on Latin Hypercube  

Lee, Kwangk-Ki (Consulting Team, VP Korea)
Han, Seung-Ho (Dept. of Mechanical Engineering, Dong-A Univ.)
Publication Information
Transactions of the Korean Society of Mechanical Engineers A / v.36, no.9, 2012 , pp. 1065-1071 More about this Journal
Abstract
Wind energy is becoming one of the most preferable alternatives to conventional sources of electric power that rely on fossil fuels. For stable electric power generation, constant rotating speed control of a wind turbine is performed through pitch control and stall control of the turbine blades. Recently, variable pitch control has been implemented in modern wind turbines to harvest more energy at variable wind speeds that are even lower than the rated one. Although wind turbine pitch controllers are currently optimized using a step response via the Ziegler-Nichols auto-tuning process, this approach does not satisfy the requirements of variable pitch control. In this study, the variable pitch controller was optimized by a genetic algorithm using a neural network model that was constructed by the Latin Hypercube sampling method to improve the Ziegler-Nichols auto-tuning process. The optimized solution shows that the root mean square error, rise time, and settle time are respectively improved by more than 7.64%, 15.8%, and 15.3% compared with the corresponding initial solutions obtained by the Ziegler-Nichols auto-tuning process.
Keywords
Optimization; Wind Turbine; Pitch Controller; Auto-tuning; Ziegler-Nichols Step Response; Latin Hypercube; Neural Network; Genetic Algorithm;
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