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Hybrid DSO-GA-based sensorless optimal control strategy for wind turbine generators

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Abstract

The operation of a wind turbine generator involves natural uncertainty due to aerodynamic characteristics, resulting in a system that performs inefficiently. In general, the conventional controller now in wide use is not suitable for every operating point, because its tuning parameters and set-points do not meet the varying system characteristics. A study into an optimal control technique is conducted to reduce the negative effects of inherent uncertainty in system operation. In order to resolve the uncertainty problem, an optimal control method for an effective wind turbine generator is designed on the basis of a sensorless frame by utilizing a hybrid of the direct search optimization method (DSO) and the genetic algorithm (GA). This method is easy to implement and computation of functional derivatives is not necessary. The conventional GA is well known for its high performance in global optimization and its effectiveness in making ideal choices for control variables. The proposed DSO-GA hybrid differs from the conventional GA in terms of the sampling survey and the crossover operation. Moreover, the proposed multivariable optimal control strategy is a sensorless optimization technique that determines the pitch angle of the blades and the yaw angle of the nacelles to produce stable maximum power from a wind turbine system under steady-state operation. The proposed DSO-GA controller is implemented for a lab-scale wind turbine generator exposed to artificial wind, and the experimental results constitute a 3-D performance surface model of output voltage, which is used as an objective function for simulation. The optimization procedure with the objective function is carried out by means of the conventional and proposed methods, whose results reveal that the proposed DSO-GA optimizer yields far better performance in terms of generation number, convergence rate, and robustness. Both techniques are applied to a wind power generator through simulation and experiments. The performances are compared, and conclusions are drawn for each case.

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Authors and Affiliations

  1. Department of Control and Instrumentation Engineering, Korea University, Seoul, 137-701, Korea

    Jin-sung Kim, Jong-hyun Jeon & Hoon Heo

Authors
  1. Jin-sung Kim
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  2. Jong-hyun Jeon
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  3. Hoon Heo
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Corresponding author

Correspondence to Hoon Heo.

Additional information

Recommended by Associate Editor Yang Shi

Jin-sung Kim received a B.S. degree in control and instrumentation engineering and an M.S. degree in mechatronics engineering from Korea University, Korea, in 2007 and 2009, respectively. He is currently a Ph.D. student in the Department of Control and Instrumentation Engineering at Korea University. His research interests and areas of study include the optimal control and tuning methods of PID controllers for wind turbine generators and reverse osmosis plants.

Jong-hyun Jeon received a B.S. degree in mechatronics engineering from Korea University, Korea, in 2010. He is graduated with M.S. Degree from the Department of Control and Instrumentation Engineering at Korea University. His research interests are in theoretical and experimental studies of controls for wind turbine generators. He is currently with LG Electronics as research engineer.

Hoon Heo received his B.Sc. in Mechanical Engineering, M.Sc. in Aerospace Engineering, and Ph.D. in Mechanical Engineering from Korea University, University of Texas at Austin, and Texas Tech University, respectively. He worked as a research engineer in LG Electronics from 1975 and as a principal researcher in the Agency for Defense Development from 1985 through 1989. He is now a professor in the Department of Control and Instrumentation Engineering at Korea University. His current interests include stochastic dynamics and control, new and renewable energy, and optimized management of smart grids through intelligent controls.

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Kim, Js., Jeon, Jh. & Heo, H. Hybrid DSO-GA-based sensorless optimal control strategy for wind turbine generators. J Mech Sci Technol 27, 549–556 (2013). https://doi.org/10.1007/s12206-012-1239-0

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  • DOI: https://doi.org/10.1007/s12206-012-1239-0

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