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

作者：Kim Jin sung; Jeon Jong hyun; Heo Hoon^*

来源：Journal of Mechanical Science and Technology, 2013, 27(2): 549-556.

DOI：10.1007/s12206-012-1239-0

• Optimization method
• Genetic algorithm
• Golden section method
• Optimal control
• Wind turbine generator
• Direct search method

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.

• 出版日期2013-2