PERMANENT MAGNET SYNCHRONOUS GENERATOR BASED WIND ENERGY CONVERSION SYSTEM

International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 09 Issue: 10 | Oct 2022 www.irjet.net p-ISSN: 2395-0072
© 2022, IRJET | Impact Factor value: 7.529 | ISO 9001:2008 Certified Journal | Page 1176
PERMANENT MAGNET SYNCHRONOUS GENERATOR BASED WIND
ENERGY CONVERSION SYSTEM
PRIYESH PANDEY1, SHIVENDRA SAURABH2, AJAY SHEKHAR PANDEY
1Assistant Professor, Department of Electrical Engineering, IET Ayodhya, U.P., INDIA
2Research Scholar, Department of Electrical Engineering, KNIT Sultanpur, U.P., INDIA
3Professor, Department of Electrical Engineering, KNIT Sultanpur, U.P., INDIA
----------------------------------------------------------****-----------------------------------------------------------
Abstract - For India's renewable energy sector to grow
sustainably, wind energy will be essential. The tip speed
ratio needs to be kept at its ideal level in order for a wind
turbine to obtain the most power possible from its speed in
range-cut in to rated. Maximum power point tracking
refers to the entire procedure of reaching the maximum
power, and this is known as the tip speed ratio control
(MPPT). The two MPPT methods—(i) the perturb and
observe technique (P&O) and (ii) the particle swarm
optimisation algorithm method (PSO)—are presented in
this research. The algorithm block receives the voltage and
current from the dc side as input, and it outputs the duty
ratio, which is sent to the dc-dc boost converter. Both
constant and variable speed wind input the model's
simulation and analysis are carried out using MATLAB
(simulink). Using a permanent magnet synchronous
generator (PMSG), a model of a variable-speed wind
turbine is shown, along with suggested control strategies.
The model displays the mechanical, electrical, and
aerodynamic components of the wind turbine.
Matlab/Simulink simulations have been run to validate
the model and suggested control strategies.
Keywords: Wind Turbine, Permanent Magnet
Synchronous Generator, Maximum Power Point
Tracking (MPPT), Perturb & Observe (P&O), Particle
Swarm Optimization (PSO). Boost converter
1. Introduction
The urgent need to replace fossil fuels due to their
detrimental environmental effects, such as pollution and
the greenhouse effect, has increased in recent years,
making renewable energy sources (RESs) an essential
part of the answer. Because of its benefits to the
environment and the economy, wind energy is one of the
main RESs [1]. According to predictions, 20% of the
world's energy will come from wind power by 2030 [2].
Two distinct wind turbines (WT) are used in the wind
energy conversion system (WECS) configuration and are
available on the international market. Compared to a
variable speed wind turbine, the fixed speed wind
turbine (FSWT) is straightforward to install and manage
(VSWT). The VSWT, however, offers the benefits of
improved energy collection, lower load transient load
reduction, and total load reduction controllability [3]. In
variable-speed WECSs, a variety of generator types are
used, including permanent magnet synchronous
generators (PMSGs), doubly fed induction generators,
and squirrel cage induction generators (SCIGs) [4, 5, 6].
Researchers have expanded utilisation of PMSGs in
variable speed WECSs due to high power density, high
performance efficiency, and high reliability even though
DFIGs with partial power converters is a strong
commercial contender. Additionally, the wide operating
speed range and lack of DC excitation increase the
WECSs efficiency by 10% [8–10]. Researchers are now
looking at multi-phase machines to lessen torque
pulsations and current per phase fluctuations while also
enhancing fault tolerant capabilities (FTC) [11,12].
Although they are used in WECSs, the dual-three-phase
machines [13] and the siX-phase machines [14] need for
sophisticated control systems and pricey converters.
Five-phase PMSGs are being used in numerous
applications, including small-scale WECSs and maritime
turbines [15, 16]. To optimise the power generated by
the wind and to complete essential tasks of utility grid
integration, the five-phase PMSG is used in conjunction
with an effective control system for wind power
generating [17]. The 1.5 MW five-phase PMSG used in
the variable-speed WECS arrangement is integrated with
the utility grid (UG) via a frequency converter. The
machine side converter (MSC) and the grid side
converter are two categories for the frequency converter
(GSC). In order to extract the best produced power at
each different wind speed and inject the active power
into the UG with unity power factor (UPF), the frequency
converter applies the back-to-back converter (BTBC)
through the DC-link capacitor [18].
To increase the amount of wind-generated electricity, a
number of maximum power point tracking (MPPT)
algorithms have been suggested [3,6]. They are often
divided into two groups: indirect power controllers (IPC)
and direct power controllers (DPC). Several other MPPT
algorithms, including the tip speed ratio (TSR), the
optimal torque (OT) [19], and the power signal feed-back
(PSF) [20], are implemented by the IPC control. The TSR

algorithm is a simple and effective way to control the
generator speed under a variety of weather
circumstances.
From above discussion this research prefers standalone
mode for PMSG based wind energy conversion system.
This paper deals with P&O and PSO techniques for
maximum power point tracking. The remaining sections
are followings: Section II deals with wind energy
conversion system, Sections III equipped with MPPT
techniques, Section IV elaborates the simulation results
and Section V describes the conclusion of research work.
II. Wind Energy Conversion System (WECS)
2.1. Wind Turbine
The equation for mechanical power of WT is expressed
using Eq. (1) as,
3
1
( , )
2
m p w
P AC V
  
 (1)
where ρ is air density, A is turbine swept area and Vw is
wind speed. The power coefficient is represented by Cp
which is a nonlinear function of tip speed ratio (λ) and
blade pitch angle (β) and is expressed using Eq. (2). The
Cp has a theoretical value of 0.59 [21].
5
2
1 3 4 6
( , ) i
c
p
i
c
C c c c e c

   


 
   
 
 
(2)
Where,
2
1 1 0.035
0.08 1
i
   
 
 
(3)
The tip speed ratio λ of the WT is expressed using Eq. (4)
as,
t
w
R
V


 (4)
where, Ωt is the rotor speed, R is the radius. The
developed mechanical torque τm of the WT can be
expressed as,
m
m
t
P
 
 (5)
The relationship between Cp and λ developed using
the measured data from the simulation of WT is shown
in Fig. 2(a)-(b).
0
0.1
0.2
0.3
0.4
0.5
1 2 3 4 5 6 7 8 9
Power
Coefficient
Tip speed ratio
0
  
18
  
Polynomial Fit
Measured Value
optimal

optimal

(
)
p
C
( )

(a) Cp - λ curve
0 2 4 6 8 10 12 14 16 18 20
3
4
5
6
7
8
9
( )

Optimal
Tip
Speed
Ratio
(

optimal
)
Blade Pitch Angle ( )
Polynomial Fit
Measured Value
(b) λoptimal - β curve
Figure 2(a)-(b). Polynomial fit of measured data.
From Fig. 2(a) it can be observed that the optimal
value of the λ varies with β for maximizing the value of
Cp. Therefore, a relationship is developed between β and
λoptimal to track Cp to maximum value and is expressed in
Eq. (6) [22].
2
1 2
3 4 5 6
3 4 5 6
optimal
     
       
  
   
(6)
Eq. (6) is acquired by using polynomial fit method
and plotted in Fig. 2(b).
2.2 Permanent Magnet Synchronous Generator
(PMSG)
The mathematical model of a PMSG is developed in
the direct-quadrature (d-q) reference frame. The
modeling of the PMSG in state equation is expressed
using Eq. (7) & (8) as,

V V
L
V C
R
V
1
( ( ) )
d
s d e qs ls q d
ds ls
di
R i L L i u
dt L L

    

(7)
1
( ( ) )
q
s q e qs ls d q
qs ls
di
R i L L i u
dt L L

    

(8)
where, Rs is the stator resistance, id and iq are the
currents, Ld and Lq are the inductances of the generator
along the d and q axis, Lls is the leakage inductance of the
generator and ωe is the electrical rotating speed (rad/s)
of the generator [23].
The electromagnetic torque (Tem) of the PMSG is
expressed using the Eq. (9) as,
1.5 (( ) )
em ds ls d q q f
T p L L i i i 
   (9)
Where, ψf is the permanent magnetic flux.
III. Boost Converter & MPPT Techniques
3.1 Boost Converter
The boost converter's main purpose is to raise the
voltage. Fig. 3.1 depicts the boost converter's circuit
configuration.
Fig. 3.1 Circuit Diagram of Boost Converter
During the ON period of the switching element, the
inductor's current starts to increase and it begins to
store energy. It is said that the circuit is charging. The
inductor's reserve energy begins draining into the load
and the supply while it is in the OFF position [24]. The
inductor time constant affects the output voltage level,
which is greater than the input voltage level. The
switching device's duty ratio and source side voltage
are compared to determine the load side voltage.
3.1.1 OPERATING MODES OF BOOST CONVERTERS
The operation of Boost Converter can be classified in two
modes:
(a) Mode I Operation of Boost Converters:
Fig. 3.2 Mode I of Boost Converter
When the switch is closed, the source voltage charges
the inductor, which then stores the energy. Although
the inductor current grows exponentially in this mode,
we'll assume for the sake of simplicity that it charges
and discharges in a linear fashion [25]. Since the diode
prevents current from flowing, the load current, which
is provided by the discharge of the capacitor, remains
constant.
(b) Mode II Operation of Boost Converters:
Fig. 3.3 Mode II of Boost Converter
In mode II, the switch opens, short-circuiting the diode
as a result. Through opposing polarities, the inductor's
stored energy is released, which charges the capacitor
[26]. The output voltage is the result of adding VS and VL
while the load current stays constant.
3.2 MPPT Techniques
The two MPPT methods used in this project are-
(i)Perturb and Observe Method
(ii) Particle Swarm Optimization
Let us discuss them in detail.

(i) PERTURB AND OBSERVE (P&O) ALGORITHM
BASED MPPT:
It monitors the power fluctuation and, in response,
makes modifications to the relevant parameter, such as
the duty cycle of the DC-DC converter to control the dc
voltage or to regulate current to change the rotor speed
and track the MPP. This approach is based on randomly
perturbing control variables in tiny stages, then choosing
the next perturbation depending on how the power
curve has changed as a result of the previous
perturbation. Due to its simplicity and lack of a
mechanical speed sensor or anemometer, the P&O
technique is a commonly used MPPT algorithm [27].
Fig. 3.4 P&O Flowchart
Where, D=duty cycle;
I=Current;
V=Voltage;
P=Power;
Subscript k denotes the value at a particular instant;
Subscript k-1 denotes the value at previous instant;
Subscript k+1 denotes the value at succeeding instant
(ii) PARTICLE SWARM OPTIMIZATION (PSO)
ALGORITHM BASED MPPT:
Due to its shown resilience, simplicity of usage, and
potential for global exploration across a range of
applications, PSO is an evolutionary computing approach
that is often used. It searches the optimised particle's
position and velocity in an iterative procedure to
discover a minimal value of the objective function in
order to arrive at the best outcome. Along with a few
learning and weighting criteria, the method makes use of
parameters including swarm size (N), iteration number
(T), and search space dimension (D) [28].
Fig. 3.5 PSO Flowchart

3.2 Block Diagram of the system
Fig.3.2. Block Diagram of the MPPT based WECS
The above figure 3.2 shows the simple block diagram
preferred for this research. As two MPPT methods have
been used in the model, the block corresponding to the
MPPT algorithm is altered to obtain the different MPPT
control.
The MPPT block that I have used for the Perturb and
Observe (P&O) method is purely a mathematical one. On
the other hand the MPPT algorithm block used for PSO
method is a MATLAB compatible PSO code.
IV. Simulation Results
4.1 MPPT-1
4.1.1 PERTURB AND OBSERVE ALGORITHM BASED
MPPT WITH CONSTANT WIND SPEED INPUT :
The following is the simulation designed in Simulink:
The input wind speed is kept constant at 12 m/s.
Fig 4.1 Simulation Model
The following is the curve traced by dc power output of
the System. As we can see initially the curve is traced
upward which signifies that the MPPT is achieved.
Fig 4.2 Result of wind turbine
4.1.2 PERTURB AND OBSERVE ALGORITHM BASED
MPPT WITH STAIRCASE INPUT WIND SPEED:
SIMULATION:-
The constant input in the previous simulation is removed
by staircase input in this simulation.
Fig 4.3 P&O MPPT Based WECS

RESULT:
Fig 4.4 Result of DC o/p Voltage
Component of PMSG :
Name Value
C 6.6094e^-04
D 0.8813
Io 30
Io ripple 0.2
L 4.5573e^-05
Po 12000
RL 13.33
Vinmin 50
Vout 400
Dl 48
Dv 2
Fs 20000
N 0.95
S.No. R
load
V in V out Pac Pdc
1. 13.5 55.87 281.33 7.163 5.863
2. 13.33 -0.17 0.00 0.4375 2.068e^20
3. 54 79.91 398.18 5.918 2.936
4. 56 4.77 0.00 0.4822 0
4.2 MPPT-2
4.2.1 PSO ALGORITHM BASED MPPT WITH STAIRCASE
INPUT:
SIMULATION:-
Fig 4.5 PSO MPPT Based WECS
Fig 4.6 Result of DC o/p Power
V. Conclusion
Both MPPT methods—P&O and PSO—have been
successful in obtaining the maximum power. In all
situations, MPPT is accomplished by adjusting the dc-dc
boost converter's duty ratio. The Tip-speed ratio is
influenced by the DC/DC control, which also impacts
rotor speed. Thus, TSR is kept at a desirable level by
regulating the duty ratio. We may thus conclude that tip-
speed ratio management has been effective. The WECS
system has been designed using the necessary
methodology. The simulation was carried out in
MATLAB Simulink using both constant and variable
0 0.5 1 1.5 2 2.5 3
0
2000
4000
6000
8000
Time (Sec)
P
L
(W)

input conditions. PSO method has demonstrated
superior performance than P&O algorithm because it
takes less time to first obtain the MPPT under
continuous input.
Additionally, compared to P&O algorithm, PSO has
exhibited superior steady state stability. We may also
construct WECS in hardware to test its real viability as
the simulation of WECS to regulate the TSR was
performed in MATLAB Simulink. As was the project's
goal, just the Tip-speed ratio is controlled here, but we
can also create an appropriate pitch controller for WECS
hardware implementation.
IV. References
[1] Kaldellis J, Apostolou D. Life cycle energy and
carbon footprint of offshore wind energy.
Comparison with onshore counterpart. Renew
Energy 2017;108:72–84.
[2] Hossain MM, Ali MH. Future research directions
for the wind turbine generatorsystem. Renew Sustain
Energy Rev 2015;49:481–9.
[3] Kumar D, Chatterjee K. A review of conventional
and advanced MPPT algorithms for wind energy
systems. Renew Sustain Energy Rev 2016;55:957–70.
[4] Zribi M, Alrifai M, Rayan M. Sliding mode control
of a variable-speed wind energy conversion system
using a squirrel cage induction generator. Energies
2017;10:604.
[5] Saeed MS, Mohamed EE. Partitioned stator doubly-
fed brushless reluctance machine for wind generating
systems. Power Systems Conference (MEPCON), 2017
Nineteenth International Middle East. 2017. p. 864–9.
[6] Tripathi S, Tiwari A, Singh D. Grid-integrated
permanent magnet synchronous generator based
wind energy conversion systems: a technology review.
Renew Sustain Energy Rev 2015;51:1288–305.
[7] Xie D, Lu Y, Sun J, Gu C. Small signal stability
analysis for different types of PMSGs connected to the
grid. Renew Energy 2017;106:149–64.
[8] Linus RM, Damodharan P. Maximum power point
tracking method using a modifiedperturb and observe
algorithm for grid connected wind energy conversion
systems. IET Renew Power Gener 2015;9:682–9.
[9] Yang B, Yu T, Shu H, Zhang X, Qu K, Jiang L.
Democratic joint operations algorithm for optimal
power extraction of PMSG based wind energy
conversion system. Energy Convers Manage
2018;159:312–26.
[10] Bonfiglio A, Delfino F, Gonzalez-Longatt F,
Procopio R. Steady-state assessments of PMSGs in
wind generating units. Int J Electr Power Energy Syst
2017;90:87–93.
[11] Toliyat HA, Rahimian MM, Lipo T. dq modeling of
five phase synchronous re-luctance machines
including third harmonic of air-gap MMF. Industry
Applications Society Annual Meeting, 1991.,
Conference Record of the 1991 IEEE. 1991. p.231–7.
[12] Levi E, Bojoi R, Profumo F, Toliyat H, Williamson
S. Multiphase induction motor drives–a technology
status review. IET Electr Power Appl 2007;1:489–
516.
[13] Reusser CA, Kouro S, Cardenas R. Dual three-
phase PMSG based wind energy conversion system
using 9-switch dual converter. Energy Conversion
Congress and EXposition (ECCE), 2015 IEEE. 2015. p.
1021–2.
[14] Abdelsalam I, Adam G, Holliday D, Williams B.
Assessment of a wind energy conversion system
based on a siX-phase permanent magnet synchronous
generator with a twelve-pulse PWM current source
converter. ECCE Asia Downunder (ECCE Asia), 2013
IEEE. 2013. p. 849–54.
[15] Liang C, Le Claire J-C, Aït-Ahmed M, Benkhoris
M-F. Power control of 5-phase PMSG-diode rectifier-
interleaved Boost set under health and fault modes.
Electr Power Syst Res 2017;152:316–22.
[16] Youssef A-R, Sayed MA, Abdel-Wahab M. MPPT
control technique for direct-drive five-phase pmsg
wind turbines with wind speed estimation. Variations
2015;21:22.
[17] Rhaili S, Abbou A, Marhraoui S, El Hichami N.
Vector control of five-phase Permanent Magnet
Synchronous Generator based variable-speed wind
turbine. Wireless Technologies, Embedded and
Intelligent Systems (WITS), 2017 International
Conference on. 2017. p. 1–6.
[18] Athari H, Niroomand M, Ataei M. Review and
classification of control systems in grid-tied
inverters. Renew Sustain Energy Rev 2017;72:1167–
76.
[19] Ganjefar S, Ghassemi AA, Ahmadi MM. Improving
efficiency of two-type maximum power point tracking

methods of tip-speed ratio and optimum torque in
wind turbine system using a quantum neural
network. Energy 2014;67:444–53.
[20] Pagnini LC, Burlando M, Repetto MP.
EXperimental power curve of small-size windturbines
in turbulent urban environment. Appl Energy
2015;154:112–21.
[21] hul Pazhampilly, S. Saravanan and N. Ramesh Babu,
“Incremental Conductance Based MPPT For PV System
Using Boost and Sepic Converter”, ARPN Journal of
Engineering and Applied Sciences, Vol. 10, No. 7, April
2015.
[22] Mei Shan Ngan and Chee Wei Tan, “A Study of
Maximum Power Point TrackingAlgorithms for Stand-
alone Photovoltaic System”, 2011 IEEE Applied Power
ElectronicsColloqulum (IAPEC).
[23] Kok Soon Tey and Saad Mekhilef, “ Modified
Incremental Conductance Algorithm for Photovoltaic
System under Partial hading Conditions and Load
Variations”, IEEE Transactions On Industrial Electronics,
Vol. 61,
[24] Luxy Xavier and Veena Wilson, “ Tracking of
Maximum Power Point Using Direct Control Algorithm
with a DC-DC Converter and a BLDC Motor for Domestic
Applications”, International Journal of Engineering and
Innovative Technology (IJEIT) Volume 4, Issue 10, April
2015.
[25] hul Pazhampilly, S. Saravanan and N. Ramesh Babu,
“Incremental Conductance Based MPPT For renewable
energy System Using Boost and Sepic Converter”, ARPN
Journal of Engineering and Applied Sciences, Vol. 10, No.
7, April 2015.
[26] Zhou Xuesong, Song Daichun, Ma Youjie, Cheng
Deshu, “The Simulation and Design for MPPT of PV
system based on Incremental Conductance method”,
2010 WASE International Conference on Information
Engineering.
[27] Selmi T, Niby A, Devis L, Davis A. P&O MPPT
implementation using MATLAB/Simulink. In Proceeding
IEEE Conference on Ecological vehicles and Renewable
Energies. Monte-Corlo; 25-27 March 2014. p. 1-4.
[28] Ye, M., X. Wang, and Y. Xu, Parameter extraction of
solar cells using particle swarm optimization. Journal of
Applied Physics, 2009. 105(9): p. 094502.

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