Abstract:A strategy was proposed to determine the optimal operating point for the proportional-integral-derivative (PID) controller of a wind turbine, and identify the stability regions in the parameter space. The proposed approach combined particle swarm optimization (PSO) and radial basis function neural network (RBFNN) algorithms. These intelligent algorithms are artificial learning mechanisms that can determine the optimal operating points, and were used to generate the function representing the most favorable operating p i k k − parameters from each parameter of d k for the stability region of the PID controller. A graphical method was used to determine the 2D or 3D vision boundaries of the PID-type controller space in closed-loop wind turbine systems. The proposed techniques were demonstrated using simulations of a drive train model without time delay and a pitch control model with time delay. Finally, the 3D stability boundaries were determined the proposed graphical approach with and without time delay systems.
In this paper, the application of PI control design is presented by use of the sensitivity function and the genetic algorithm. A systematic procedure is proposed to improve the result of the sensitivity function for PI control in the literature. By doing this, a certain gain margin (GM) and phase margin (PM) of PI control systems can be guaranteed. In order to obtain the stability boundary of the PI control systems in parameter space, the Tan's method is adopted here. In addition, the genetic algorithm is applied to fulfill the specifications of integral of absolute error (IAE). Moreover, this method is also utilized to design the PI controller of Kharitonov plants. Computer simulations show the effectiveness of the proposed method.
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