Modeling of a Variable Speed
Copyright © 2015 Praise Worthy Prize S.r.l. - All rights reserved International Review of Automatic Control, Vol. 8, N. 4
316 Changes in wind speed affect the PMSG output power
and system performance. A control system is required to improve the efficiency and performance by ensuring that
the generator operates at the maximum power point. By controlling the PMSG rotor speed, this maximum
power point can be achieved. Several speed control strategies have been developed. Proportional integral PI
controller has been widely used in industrial processes, because it is simple and easy to implement. PI controllers
have shown good performance for PMSG speed control [12]. However, the determination of the PI control
parameter is very difficult, so the necessary tuning parameters PI. This can be done by several methods,
including neural networks, fuzzy logic, B-spline networks, genetic algorithms, heuristic optimization
methods, and particle swarm optimization PSO [13]- [20], [28]-[30]. Aissaoui et al [13] tuned PI controllers
using a fuzzy logic method to control the speed of a PMSG. Based on simulation results, a PI controller tuned
by fuzzy logic can determine the maximum power with better performance than the PI controller alone. However,
the success of the fuzzy logic method is highly dependent on the determination rule and membership function used.
Tuning PI controller parameters with a heuristic method for PMSG speed control has shown to have
better performance with fewer errors [17]. However, the use of a heuristic method is a long process and is difficult
to implement in practice. To improve performance PI controller , in this paper, the parameters of PI controller
tuned by using PSO. PSO is a multiobjective optimization that can tune the parameters PI for the
PMSG speed control based on error steady state, maximum overshoot, rise time and settling time.
Compared with genetic algorithms and the linear quadratic regulator LQR method, PSO produces better
dynamic performance for linear brushless DC motors [21]. PSO is also a very simple method that is easy to
implement and code using a computer, and for these reasons has been widely studied [14]-[20]. This paper
presents the use of PSO for tuning a PI controller for the speed control of a PMSG-driven wind turbine.
Speed control using the PSO-PI system is compared to using the PI controller with pole assignment. This paper
is organized as follows: in Section 2 the variable speed wind turbine and the PMSG models are described.
In Section 3, an overview of particle swarm optimization is presented. In Section 4, the control
strategy is described how PSO is used to optimally tune the PI controller for speed control of the PMSG. In
Section 5, we compare the performance of the PSO-PI controller and PI controller via simulation results. In
Section 6 we show our final conclusions of the paper.