Novel Adaptive Controller for PMSG Driven Wind Turbine To Improve Power System Stability

International
OPEN ACCESS Journal
Of Modern Engineering Research (IJMER)
| IJMER | ISSN: 2249–6645 www.ijmer.com | Vol. 7 | Iss. 4 | Apr. 2017 | 19 |
Novel Adaptive Controller for PMSG Driven Wind Turbine
To Improve Power System Stability
Vibin.R1
, Ramasamy.A2
1
(Assistant Professor, Department of EEE, CMS College of Engineering and Technology/Anna
University of Technology, Chennai, India)
2
(Assistant Professor, Department of EEE, CMS College of Engineering and Technology/Anna University
ofTechnology, Chennai, India)
I. INTRODUCTION
Modern wind power applications require efficient and flexible technologies that adapt to changes in
load and generation. In most of the wind turbines for on-land emplacements use double fed induction generators
due to their economic advantages (i.e., high efficiency, improved controllability and reduced rating of the
converter). Nevertheless, other energy conversion systems and generator technologies have been proposed
recently[1].One of the most promising of them is the permanent magnet synchronous generator (PMSG) which
has clear advantages in terms of efficiency and power density. Integration into the grid of this type of generators
requires a full rated AC/AC converter which, in most cases, is based on the voltage source converter technology
(VSC)[1]. Another possible type of converter is the pulse-width modulated current source converter (PWM-
CSC) which has potentially more advantages for medium size wind turbines. It is capable of controlling the DC
current according to the wind velocity independently of the DC voltage. This characteristic is exploited to create
an adaptive control which does not require measure of the rotational speed. Adaptive control techniques that
perform identification and control of dynamic systems can be adapted to highly-complex dynamic systems in
order to auto-adjust the controller parameters .Similar to widely used SVM schemes for voltage source inverters,
the SVM for CSI is also a digitally implemented vector synthesizing method [5].
A new adaptive control strategy for a wind energy conversion system based on a permanent magnet
synchronous generator and a pulse-width modulated current source converter [3]. The proposed conversion
system is a good alternative due to its high efficiency and reliability. The control strategy uses an adaptive PI
which is self-tuned based on a linear approximation of the power system and a desired closed loop
response[1][6].
II. ENERGY CONVERSION SYSTEM
The proposed energy conversion system is based on PMSG.This type of machine has three main
features which are relevant for wind power applications: there are no significant losses generated in the rotor;
magnetization provided by the permanent magnets allows soft start; and there is no consumption of reactive
power. The first characteristic implies an improvement in efficiency while the second and third effect the power
electronic converter which does not require bi-directional power capability [1][3]. Hence, a full bridge diode
rectifier is enough for the AC/DC conversion. In addition, PMSGs allow smaller, flexible and lighter designs as
well as lower maintenance and operating costs. A gear box is not required if it is designed appropriately with a
high number of poles [1].PMSG based wind turbine is the most suitable scheme for electrical power generation
and it overcomes the problem that exists in the conventional wind turbine power generation. The energy
conversion in the conventional wind turbine driven induction generator is not satisfactory but the PMSG driven
ABSTRACT: This paper describes the behavior of a permanent magnet synchronous generator
(PMSG) driven by wind turbine. The proposed conversion system is a good alternative due to its high
efficiency and reliability. Electrolytic capacitors are not required in this type of converter and the
voltage in the DC-link as well as the generated reactive power can be dynamically modified according
to the wind velocity. The adaptive control strategy uses an adaptive PI which is self-tuned based on a
linear approximation of the power system calculated at each sample time. Space vector technique for
grid side inverter for transformer less integration of generator and a pulse-width modulated current
source converter. A model reference is also projected in order to reduce the post-fault voltages.
Simulation results prove the advantages of the proposed control. A MATLAB computer simulation study
was undertaken and results on PWM-CSC are presented.
Keywords: Permanent Magnet Synchronous Generator (PMSG), Adaptive Controller, Space Vector
Modulation (SVM)

Novel Adaptive Controller For PMSG Driven Wind Turbine To Improve Power System Stability
wind turbine is the most suitable scheme in terms of energy conversion efficiency. A new adaptive control
strategy for a wind energy conversion system based on a permanent magnet synchronous generator and a pulse-
width modulated current source converter [3].
Fig.1: General Block Diagram of proposed system
Fig.2: Schematic of proposed renewable based distributed generation system
The paper is so arranged in the order that: Section II describes the energy conversion system under
consideration and the controller for grid-interfacing inverter. Section III includes a digital simulation study is
presented. Section IV finally concludes the paper.
III. PROPOSED ADAPTIVE CONTROL FOR PWM-CSC
A hierarchical control is proposed for integration of the wind turbine into the grid as depicted in Fig. 4.
First, the maximum tracking point algorithm is modified in terms of the DC current in the PWM-CSC. [5]
Therefore, the reference for this current is modified dynamically according to the wind velocity. Next, an
adaptive PI control is designed in order to track this reference. Finally, a model reference control is included in
order to reduce the overvoltageresultingfromafaultinthegrid.Space vector modulation (SVM) is used to modulate
the current of the converter [1][6].
3.1. Adaptive Control
This paper means by adaptive control any control strategy which uses parameter estimation of the plant
in real time by using recursive identification.[1].The adaptive controller to be designed is based on the
certainty equivalence principle: design the controller as long as the plant parameters are known. How- ever,
since these are unknown at time, they are replaced by an estimate given by an online identifier [6]. This adaptive
controller is easy to implement, since for the controlled plant, only the output signal is needed for feedback. An
adaptive PI control is designed where the plant parameters are estimated by an online identifier, as shown in
Fig.3.

In continuous time, a PI controller can be defined as
u(t)=kpe(t)+ki 𝑒 𝑟 𝑑𝑟
𝑡
0
(1)
Fig.3: Adaptive control and identifier
Fig.4: Simulation diagram representing the proposed energy conversion system connected to the grid
Beingu(t) the control signal, and e(t) the error signal (represented by the difference between the
reference and the output signals). In this case, these variables are given as follows [1]
'
( ) ( ) ( )ref DCe t I t I t 
(2)
( ) ( ). ( ). ( )x DCu t m t U t I t 
(3)
In discrete time, the PI controller can be defined as
1( ) ( ) ( )k i k kei t e t e t 
(4)
( ) . ( ) . ( )k P k i ei kU t K e t K h t 
(5)
Being the sample time h=tk-tk-1, e(tk)and ei(tk) the error and the integral error at time tk respectively, and
ei(tk-1) the integral error at time tk-1. By defining a delay operator q-1
such as y(tk-1)=q-1
y(tk) . Equation can be
rewritten as follows [1]
1( ) ( ) ( )i k i ke t e t e k 
(6)
1
(1 ). ( ) ( )i k kq e t e t
 
(7)
1
1
( ) . ( )
1
i k ke t e t
q

 (8)

1
1
( ) . ( ) . ( )
1
k p k ih ku t k e t k e t
q
 
 (9)
1
1
( ) ( . ). ( )
1
k p ih ku t k k e t
q
 
 (10)
1
1
(1 )
( ) . ( )
1
p ih
k k
k q k
u t e t
q


 

 (11)
1
1
.
( ) . ( )
1
p ih p
k k
k k k q
u t e t
q


 

 (12)
Obtaining the following expression for the PI controller in discrete time
1
1
1 2.
( ) . ( )
1
k k
c c q
u t e t
q




 (13)
where the parameters and of the PI controller in continuous time can be related to the controller in discrete time,
as follows
2pk c 
(14)
1 2
i
c c
k
h


(15)
Since the process to be identified is nonlinear, the identified model is a linear approximation of the
nonlinear model at time instant tk. A simplified first order model is selected, described by a discrete transfer
function, as
1
0
1
1
.
( ) . ( )
1 .
k k
b q
y t u t
a q
 
 

 (16)
Equations by using the transformation as follows:
1
1
1 2. ( )
( ) . ( ) . ( )
1 ( )
c c z L z
u z E z E z
z P z



 
 (17)
and
1
0
1
1
. ( )
( ) . ( )
1 . ( )
b z B z
y z u z
a z A z
 
 
 

(18)
By using equations it is possible to formulate the block diagram
Fig.5: Diagram block using transformation
From this figure it is possible to obtain the closed loop transfer function, as follows:
( )
( ) . ( )
( )
B z
y z u z
A z

(19)

( )
( ) . ( )
( )
L z
u z E z
P z

(20)
( ) ( ) ( )E z R z y z  (21)
( ). ( )
( ) . ( )
( ). ( ) ( ). ( )
B z L z
y z R z
A z P z B z L z

 (22)
If defining desired closed loop poles, given by
Pd (z) = (1-𝛼1z-1
) (1-𝛼2
-2
) (23)
where 𝛼1 and 𝛼2 are the discrete time roots of (34), which can be related to the continuous time roots 𝛼1 and 𝛼2
by using
𝛼1=es
1
h
(24)
𝛼2=es
2
h
(25)
It is possible to obtain the controller parameters by comparing the closed loop poles with the desired closed loop
poles as follows:
( ) ( ). ( ) ( ). ( )dp z A z P z B z L z 
(26)
1 1 1 1
1 0 1 2( ) (1 . )(1 ) . ( . )dP z a z z b z c c z     
    
(27)
1 2
1 0 1 1 0 2( ) 1 (1 . ) ( . )dp z a b c z a b c z     
      
(28)
1 2
1 2 1 2( ) 1 ( ) . .dp z z z    
   
(29)
Therefore, the controller parameters can be obtained as,
1 2 1
1
0
( 1)a
c
b
  

   

(30)
1 2 1
2
0
. a
c
b
  



(31)
Where it is evident that c1 and c2 are related to the linear approximation model of the nonlinear process,
represented by the discrete transfer function. When the projection algorithm is applied in for the estimation the
following actualization rule is obtained:
1 1 0 1 1( ) .( 1). ( ) ( ). ( )k k k k ky t a t y t b t u t  
     
(32)
1
1 1 1 2 2
1 1
( )
( ) ( ) .( ( ) ( ))
( ) ( )
k
k k k k
k k
y t
a t a t y t y t
y t u t
  

 

  
 (33)
1
0 0 1 2 2
1 1
( )
( ) ( ) .( ( ) ( ))
( ) ( )
k
k k k k
k k
u t
b t b t y t y t
y t u t
  

 
  
 (34)
where a1(tk) and b0’(tk) are the estimated parameters at time tk, anda1’(tk-1) and b0’(tk-1) are the estimated
parameters at time tk-1. Since the controller parameters are dependent on and according a1’ to b0’, a time varying
parameters for each can be obtained as follows [1][6].
(35)
1 2 1
1
0
( ( ) 1)
( )
( )
k
k
k
t
c t
b t
  

   


1 2 1
2
0
( . ( ))
( )
( )
k
k
k
a t
c t
b t
  



(36)
where c1(tk) and c2(tk) are automatically tuned according to the desired closed loop poles.
Finally, the controller parameters KP and KI can be calculated by
2( ) ( )p k kk t c t 
(37)
1 2
1
( ) ( )
( ) k k
k
c t c t
k t
h


(38)
Therefore, the resultant controller is an adaptive PI controller calculated for each tk. The behavior of the
controller can be determined by the selection of the desired closed loop poles and the sample time h.
3.2 Model Reference Adaptive Control
Reference current is modiﬁed during a short circuit in order to improve the short circuit behavior of
the converter. A slightlydifferentcurrent inwhichthedesiredoutputisgen- erated by a linear reference model is
proposed[1]. The reference model can be selected with an order less than or equal to the order of the process. In
this work, a zero order model is used in prefault no control during the fault and a ﬁrst order model after the fault
as follows
'
( ).mref refI H z I
(39)
1
0
1
0
(1 ).
( )
1 .
m
p z
H z
p z




 (40)
where 𝜌0 must be selected as a stable root ( |𝜌0 | < 1 ), where it is clear that the reference model must be
selected as a stable model with unitary gain. However, the selection of the reference model and the pole
placement technique are separate problems, so it is evident that by using a reference model the flexibility of the
control system in the assignment of the closed loop poles is increased. [1]The fault condition is detected using
the voltage Ux.
IV. SIMULATION OUTPUT
To evaluate the maximum power extraction, DC link voltage control, grid voltage support control of a
PMSG wind turbine the optimal control strategy has introduced, an integrated simulation of a complete PMSG
system developed and detailed models in MATLAB Simulink in which both steady and variable wind
conditions are considered[1][3]. In this chapter, obtained results are shown and discussed.
Fig.6: Output of DC Voltage and Current
The wind velocity provided to the system having 300 varying wind speeds for 0-15 sec output speeds.
After the system is settled, the output power is very close to the maximum power that can be captured by the
turbine at the wind speed. The rotor speed, electromagnetic torque, d-axis current, q-axis current and a single
phase A is taken from produced three phase stator currents as shown in Fig.6. The rotor speed increases linearly
as the rotor starts to rotate [4]. Electromagnetic torque produced by the PMSG is gradually decreased as shown

in figure. d-axis and q-axis currents also decreased, finally the stator current (a) can be measured current in
amps[1][3].
The control strategy changes dynamically according to the wind conditions as shown in Fig.8. If a time
invariant PI control is used the performance could be similar at least at nominal wind velocity. In that case, the
proposed algorithm can be used as a tuning technique.Three-phase voltages and currents in the PWM-CSC and
the grid is same that are shown in Fig.9. Small harmonic distortions are present in three-phase voltages due to
the commutation process. They are attenuated by the transformer and hence, the voltage in the point of common
coupling is completely sinusoidal. A smoother waveform can be achieved by increasing the switching frequency
at the expense of higher switching losses.
The system responses of the different control strategies are similar to those in Fig.7 Again, strategy
control has the quickest responsesonthegrid-sideactive/reactivecurrents,whichisquite helpful to the voltage
recovery of the utility. The output of modulation index (M), and DC current (Idc), Referencecurrent (Iref),
TotalPower (p) are obtained which results are shown in fig.7
Fig.7: Output of Modulation Index(m), DC Current(Idc), Reference Current(Iref), Total Power(P)
Fig.8: Kp, Ki characteristics
Fig.9: Output of Grid gives stable Voltage and Current

The generated 3 phase voltages, currents are shown in fig.9 ,which has similar operation as in PWM-
CSC. In fig 10 the gererated output are selected and the THD present in them are shown. From this figure we
can clearly say that the THD from the grid output.
Fig.10: Output of Grid gives stable Voltage and Current
V. CONCLUSION
An adaptive control for a PWM-CSC-based energy conversion system mainly designed for wind power
applications was presented. Both the control and the type of converter increase the ﬂexibility of the wind
turbine. They are able to operate in critical conditions such as short circuit and fast changes in wind velocity.
Measurements of wind velocity or rotational speed are not required. A reference model is used to improve the
transient behavior of the control after critical faults. For systems with time invariant behavior, the adaptive
controller also behaves as a ﬁxed controller. Therefore, it can be seen that the adaptive controller method can be
used as a technique for self-tuning the controller based on the desired response.
REFERENCE
[1]. Eduardo Giraldo, and Alejandro Garces, “An Adaptive Control Strategy for a Wind EnergyConversion
System Based on PWM-CSC and PMSG,”IEEE Transactions On Power Systems, Vol. 29, No. 3, pp.
1446-1453, May 2014
[2]. J. Carrasco, L. Franquelo, J. Bialasiewicz, E. Galvan, R. Guisado, M. Prats, J. Leon, and N. Moreno-
Alfonso, “Power-electronic systems for the grid integration of renewable energy sources: A survey,”
IEEE Trans. Ind. Electron., vol. 53, no. 4, pp. 1002–1016, 2006.
[3]. L.Wang and M. N. Thi, “Stability enhancement of a PMSG-based offshore wind farm fed to a multi-
machine system through an LCC-HVDC link,” IEEE Trans. Power Syst., to be published.
[4]. H. Geng, G. Yang, D. Xu, and B. Wu, “Unified power control for PMSG-based WECS operating under
different grid conditions,” IEEE Trans. Energy Convers., vol. 26, no. 3, pp. 822–830, 2011
[5]. J. Dai, “Current source converters for megawatt wind energy conversion systems,” Ph.D. dissertation,
Ryerson University, Toronto, ON, Canada, 2010.
[6]. H. Li, F. Li, Y. Xu, D. Rizy, and J. Kueck, “Adaptive voltage control with distributed energy resources:
Algorithm, theoretical analysis, simulation, field test verification,” IEEE Trans. Power Syst., vol. 25,
no. 3, pp. 1638–1647, 2010.
R.Vibin receivedhis B.E degree in Electrical and Electronics Engineering from V.L.B Janakiammal
College of Engineering and Technology, in 2008 and honored M.E degree with university rank & gold
medal in Power systems from S.N.S College of Technology, Coimbatore, India in 2012. His areas of
interest include Electrical machines and Power systems. He is a life member of IAENG and now he is
currently working as an Assistant Professor in the Department of Electrical and Electronics Engineering,
C.M.S College of Engineering and technology, Coimbatore, India.
A.Ramasamy received his B.E degree in Electrical and Electronics Engineering from Sri Subramanya
College of Engineering and Technology, India in 2005 and M.E degree in Power systems from Annamalai
University, India in 2007. His areas of interest include Control system, Electrical Machines and Power
systems. He is a life member of ISTE and IAENG. Now he is currently working as an Assistant Professor
in the Department of Electrical and Electronics Engineering, C.M.S College of Engineering and
technology, Coimbatore, India.

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