ISSN 1974-6059 July 2015
Copyright © 2015 Praise Worthy Prize S.r.l. - All rights reserved
315
Tuning PI Controller Based on Multiobjective Optimization Approaches for Speed Control of PMSG Wind Turbine
Ratna Ika Putri
1, 2
, Lie Jasa
3
, Margo Pujiantara
1
, Ardyono Priyadi
1
, Mauridhi Hery P.
1
Abstract –
This paper presents a particle swarm optimization PSO method for determining proportional integral PI controller parameters for a variable speed wind turbine driven by a
permanent magnet synchronous generator. Generator speed is controlled to obtain optimal power. The performance of the PSO-PI controller is compared to that of a PI controller based on pole
assignment and simulation results show that the PSO method can successfully tune the PI controller to achieve better performance than the PI controller. Copyright © 2015 Praise Worthy
Prize S.r.l. - All rights reserved.
Keywords:
Permanent Magnet Synchronous Generator, Particle Swarm Optimization, PI Controller, Wind Turbine
Nomenclature
P
w
Wind Turbine Power watt Cp
The Coefficient of turbine power conversion ρ
The Air density kgm
3
λ The tip speed ratio
R The blade radius m
V
w
The wind speed ms β
The pitch angle ω
r
The turbine angular speed rads J
The inertia moment kg m
2
F The friction coefficient N
T
m
The mechanical torque Nm T
w
The wind turbine torque Nm i
q
,i
d
d and q axis current A u
q
,u
d
d and q axis stator voltage V Rs
The winding resistance Ω L
d
, L
q
q and d-axis inductance H ψr
Permanent magnetic flux Wb P
Pole pairs ω
g
The angular velocity of the generator rads Te
Electromagnetic torque Nm X
id
t Position of ith particle
P
best
The best position of each particle G
best
The best position of the whole swarm v
id
t velocity of ith particle
w The constriction factor
C
1
, C
2
learning factors r
1
, r
2
random numbers generated uniformly Mp
Maximum overshoot Ess
Steady state error t
r
Rise time t
s
Settling time α
The weight value Kp
d,q
Proportional constant for the d and q-axis current controller
t
i d,q
the constant of integration time for the d-axis current controller
ζ The damping coefficient
Wn The natural frequency
I. Introduction
In Indonesia, the use of electrical energy has been increasing every year by 6.2 [1]. The use of fossil fuels
in national power plants causes environmental damage due to CO2 gas emissions.
In an effort to diversify the energy supply away from dwindling fossil fuels and to reduce the environment
impact of the fossil energy, the use of renewable energy is increasing. Wind energy has great potential in
Indonesia and has been developed quickly [2]. Based on the wind speed data from various regions, wind energy
resources in Indonesia range from 2.5 to 5.5 ms at a height of 24 m above ground level.
This classifies wind speed in Indonesia in the low-to- medium grade [3]. Although wind energy is clean,
pollution free, and inexhaustible, the amount of energy generation fluctuates and the supply is intermittent.
The conversion of wind energy into electrical energy requires an electric generator. Some electrical generators
that can be used in the wind energy conversion system are permanent magnet synchronous generators PMSG,
doubly fed induction generators DFIG, and induction generators. Currently, PMSG is used widely in wind
energy conversion systems because it has several advantages: high reliability, lightweight components, low
volume, high performance, and high efficiency.
PMSG are cost effective and suitable for small-scale applications. The use of PMSG for variable-speed wind
turbines is being investigated, particularly with respect to optimizing the efficiency of the system [4]-[11].
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.
II. Modeling of a Variable Speed
