MAXIMUM POWER POINT TRACKING ALGORITHMS APPLIED TO WIND-SOLAR HYBRID SYSTEM

Presented By:
T. ARUN KUMAR
(133120)
Under the guidance of :
Prof. G.V. MARUTHESWAR
MAXIMUM POWER POINT TRACKING ALGORITHMS
APPLIED TO WIND-SOLAR HYBRID SYSTEM
DEPARTMENT OF ELECTRICAL AND ELECTRONICS
ENGINEERING
SV UNIVERSITY COLLEGE OF ENGINEERING, TIRUPATI

CONTENTS
 ABSTRACT
 INTRODUCTION
 WIND ENERGY SYSTEM
 SOLAR PV SYSTEM
 MPPT ALGORITHMS
 FUZZY LOGIC CONTROLLER
 SIMULATION MODELS
 RESULTS
 CONCLUSION
 REFERENCES

ABSTRACT
 The production of electricity from renewable energy sources like solar, wind energy increases in recent years
due to environmental problems and the shortage of traditional energy sources in the near future.
 This Thesis presents modeling, simulation, and performance study of Hybrid generation system with PI and
MPPT controller.
 The Wind power generation system uses Wind Turbine(WT), a Permanent Magnet Synchronous
Generator(PMSG), a three phase controlled rectifier bridge with PI controller, a dc bus with a capacitor and a
current regulated PWM voltage source inverter.
 The PV cell model is developed including the effects of changing solar irradiation and temperature and
Maximum power from PV cell is obtained by using MPPT controller.
 This Thesis reports the development of wind/photovoltaic power generation system and also describes the
Wind-Solar Hybrid system for supplying electricity to the Load and describe various MPPT techniques namely
Perturb and Observe(P&O), Incremental Conductance(INC) and Fuzzy Logic based MPPT controller and their
performance.
 Simulation results of Hybrid system using various MPPT techniques are provided.

INTRODUCTION
 Uncontrolled Renewable energy sources essentially have random behaviors. eg: Solar, Wind, etc.
 Power production from Uncontrolled sources is independent of human intervention.
 Hybrid power systems may contain controlled and uncontrolled energy sources and energy storage
elements with appropriate control systems.
 Hybrid power systems take advantage of the complementary nature in profile of the renewable
energy sources.
 Hybrid power systems ensure continuous and reliable power production.
 PV modules still have relatively low conversion efficiency; therefore, controlling maximum power
point tracking (MPPT) for the solar array is essential in a PV system. The amount of power generated
by a PV depends on the operating voltage of the array. A PV’s maximum power point (MPP) varies
with solar insulation and temperature.

INTRODUCTION TO
WIND ENERGY SYSTEM

WIND POWER - WHAT IS IT?
 The origin of wind energy is sun.
 When sun rays fall on the earth, its surface get heated up as a
consequence unevenly winds are formed.
 Wind energy is basically harnessing of wind power to produce
electricity.
 When solar radiation enters the earth’s atmp. Different regions of
the atmp. are heated to different degrees because of earth curvature.
 Since air tends to flow from warmer to cooler regions.

Modelling OF ‘WIND TURBINE’
 For a variable speed wind turbine, the output mechanical power available from a end turbine
could be expressed as
3
^
)
,
(
5
.
0 Vw
ACp
P 



 The relation between rotor speed and wind speed can be given by
Vw
mR/

 
 The wind turbine torque on the shaft can be calculated from the power
   



 ,
3
^
/
5
^
5
.
0
/ Cp
Wm
R
Wm
Pm
Tm 

  




 0068
.
0
)
/
21
(
^
5
4
.
0
116
5176
.
0
, 

















 i
e
i
Cp
1
)]^
1
3
^
/(
035
.
0
)
08
.
0
(
1
[ 




 


i
Where,

Cont...
 The Cp -  characteristics for the value of the pitch angle β, are illustrated in Fig.1 for 12m/s.
 The maximum value of Cp(Cpmax=0.8) is achieved for β=0
and for =1. This particular value of  is defined as the nominal
value. The simulated system parameters are listed in the table-1.
Fig:1- Power coefficient versus Tip Speed Ratio
Wind Generation specification
Turbine model 3 blade Horizontal axis
Stator phase resistance 0.8e^-3 kg-m^2
Air density  = 1.225kg/m^3
Rated speed of wind 12m/s
Generator parameter 2 MW, PMSG
Generator Frequency 50HZ
Table:-1 Simulation parameters

MODEL OF ‘WECS’
Fig.2 Model of wind energy conversion system

SOLAR PV SYSTEM
INTRODUCTION TO

INSIDE A SOLAR PV CELL
An ideal solar cell can be considered as a current source.
The current produced is proportional to solar irradiation falling on it.
A typical cell produces about 0.5 V to 1V.
Equations Involved
Fig.3 Equivalent Circuit of Photovoltaic
Cell

P-V& I-V CURVES OF A SOLAR MODULE
Fig.4 P-V & V-I curves of solar module

TEMPERATURE AND IRRADIANCE EFFECT
1. With Increase of Temperature Voc decreases & corresponding Pout decreases.
2. With Increase of Irradiance Voc increases hence increase in Pout is observed.
Fig.5 Temperature and Irradiance effect

SOLAR PARAMETERS
• Short Circuit Current (Isc): Short circuit current is max current produced by a solar
cell when its terminals are short circuited.
• Open Circuit Voltage (Voc): Open circuit voltage is the max voltage that can be
obtained from a solar cell when its terminals are left open.
• Fill Factor (FF): Fill Factor is defined as ratio of maximum power to the product of Voc
and Isc. FF is 1 for ideal case. Practically its value ranges from 0.8 to 0.89.
• FF = Vm x Im
Voc x Isc
• Efficiency : Efficiency is defined as ratio of maximum power of module to power
delivered by solar irradiance or it can be defined as ratio of product of Voc ,Isc and FF to
solar irradiance .
• Efficiency = Voc x Isc x FF
solar irradiance

PV SYSTEM Modelling
 The PV power system is Designed by using Power System Blockset under Matlab.
 The simulated system parameters are listed in the table 2.
 The array consists of 56 strings of 6 series connected
modules connected in parallel (56 * 6* 150 W = 50.4 kW)
 PV-Model :- SPR-150E-WHT-D
Solar Module specification
Rating 50KW
SC Current 2.98A
Voltage at peak 27.35V
Open circuit
voltage
32.2V
No. cells per
module(Np)
56
Table:-2 Simulation parameters
Fig.6 PV system modelling

MAXIMUM POWER POINT TRACKING
To automatically find the voltage (VMPP) or current (IMPP) at which a PV
array should operate to obtain the maximum power output (PMPP)under
a given temperature and irradiance.
MPPT TOPOLOGY
Fig.7 MPPT Topology

CHOICE OF MPPT TECHNIQUE
IMPLEMENTATION
COMPLEXITY
SENSORS
REQUIRED
ABILITY TO
DETECT
MULTIPLE LOCAL
MAXIMA
RESPONSE TIME
COST APPLICATION

I-V & PV CURVES FOR DIFFERENT TEMPERATURE
AND IRRADIANCE LEVEL
Fig.8
Fig.9
Fig.10
Fig.11

P-V CURVE FOR
MPPT
Fig.13 P-V curve for MPPT

NEED FOR MPPT
IRRADIANCE & TEMPERATURE VARIATION
LOW CONVERSION EFFICIENCY(9-17%)
TO MAXIMIZE OUTPUT POWER
TO MAXIMIZE THE EFFICIENCY

 The most obvious source of RE is the solar energy, therefore, a
huge number of projects and researches have been adopted worldwide
to utilize the indispensable sunlight as a sustainable source of energy.
 PV solar cells have relatively low efficiency ratings; thus
operating at the MPP is desired because it is at this point array will
operate at the highest efficiency.

MPPT TECHNIQUES
MPP
Techniques
Indirect
Fixed Voltage
Method
Fractional
Open Circuit
Voltage Method
Direct
Perturb &
Observe
Method
Incremental
Conduction
Method

PERTURB & OBSERVE (P&O)/HILL CLIMBING
METHOD
Direct measurement
of current, voltage
and power.
Faster and accurate
response
Incrementing the voltage
increases
the power when operating on
the
left of the MPP and decreases
the
power when on the right of the
MPP.
 The P&O algorithm is used due to its simplicity and
easy implementation.
 The operation of P&O consists in periodically
perturbing the panel operating voltage incrementally, so
that the power output can be observed and compared at
consecutive perturbing cycles.
 If the power difference is positive, further perturbation
is added to the operating voltage with the same increment,
and again the output power is observed. This perturbing
process is continued until the power difference becomes
negative.
 Thus, the direction of perturbation in operating voltage
must be reversed.

A
MPP
Fig.14 Flowchart of P&O
Fig.15 MPP graph for P&O

DRAWBACKS OF P&O METHOD
• Hill climbing and P&O methods can fail under rapidly changing
atmospheric conditions.
• The process is repeated periodically until the MPP is reached.
• The system oscillates about the MPP.
• Smaller perturbation size slows down the MPPT
• If the irradiance fluctuates
and shifts the power curve
within one sampling period,
the operating point will
fluctuate
Fig.16 MPP graph for P&O

INCREMENTAL CONDUCTANCE METHOD
Based on the fact that slope of
PV:
1. Zero at MPP
2. Negative at right of MPP
3. Positive on left of MPP
.
.
Uses two sensors:
VOLTAGE & CURRENT
SENSORS to sense the
output voltage and current
ALGORITHM works by
comparing the ratio of
derivative of conductance with
the instantaneous conductance
When this instantaneous
conductance equals the
conductance of the solar then
MPP is reached
Fig.17 MPP graph for INC

Fig.18 Flowchart of INC
Fig.19 MPP graph for INC

Perturb and observe Condition Incremental conductance
Oscillates around the maximum
power point
Oscillations in the graph Doesn’t oscillate as much as
P&O towards the maximum
power point
Simpler algorithm Structure of algorithm More complex algorithm
Does not adjust as fast as INC
method
Rapid changes in irradiation Able to find the exact maximum
point rapidly
More efficient with temperature Varying temperature Doesn’t adjust as rapidly as P&O
method
Lower irradiation & efficiency Irradiation & efficiency Higher irradiation and efficiency
Not as efficient as the INC
method
Buck-Boost converter Rapidly adjusts to converter
Table.3 Comparison between P&O and INC

FUZZY CONTROLLER IN PV
Artificial intelligent tool
which computes output
based on expert
knowledge.
faster response
using the expert
knowledge and
measured data
base.
FL Algorithm is
chosen for PV
because of its fast
and accurate
response
Fuzzy logic
controller
algorithm is based
on three steps
1-expert knowledge,
2-fuzzification &
inference diagram
3-defuzzification.

FUZZY LOGIC MPPT
CONTROLLER
FUZZIFICATIO
N
INFERENC
E
DEFUZZIFICATI
ON
RULES
Fig.21 Fuzzy Logic Controller

MODELLING OF FUZZY IN MPPT
FUZZIFICATIO
N
INFEREN
CE
ENGINE
DEFUZZIFICATI
ON
FUZZY
RULE
BOX
FUZZY LOGIC CONTROLLER
PV
MODEL
BUCK
BOOST
CONVERTE
R
LOAD
Duty Ratio Command
Fig.22 Modelling of FUZZY in MPPT

FUZZY LOGIC RULES TABLE
NB NS PS PB
NB PB PB NB NB
NS PS PS NS NS
PS NS NS PS PS
PB NB NB PB PB
Table shows the rule table of the fuzzy controller
where all the entries of the matrix are fuzzy sets of
change of power and change of current and duty
ratio D to the boost converter. The variable inputs
and outputs are divided into four fuzzy subsets:
PB(Positive Big), PS(Positive Small), NB(Negative
Big), NS(Negative Small). Therefore, the fuzzy
algorithm requires 16 fuzzy control rules.
Ex:-  If input 1 is NB and input 2 is NB then output is PB
 If input 1 is NB and input 2 is NS then output is PB
 If input 1 is NB and input 2 is PS then output is NB
Table.4 Fuzzy rules

INPUT P INPUT I
OUTPUT D

A hybrid energy system usually consisting of two or
more renewable energy sources are used together to provide
increased system efficiency as well as greater balance in energy
supply. In this thesis, the hybrid energy system is a photovoltaic
array coupled with a wind turbine. The developed system
consists of photovoltaic array and PMSG based wind turbine
connected to the load for achieving maximum power point with
a current reference control produced by MPPT algorithms.
Fig.23 PV-WIND hybrid system

Fig shows the schematic diagram of hybrid system
Fig.24 Model of PV-WIND hybrid system

SIMULATION MODEL OF WECS
Fig.25 Simulation Model of WECS

SIMULATION MODEL OF PV CELL
Fig.26 Simulation Model of PV CELL

SIMULATION MODEL OF HYBRID SYSTEM
Fig.27 Simulation Model of HYBRID System

SIMULATION MODEL OF P&O
Fig.28 Simulation Model of P&O

SIMULATION MODEL OF INC
Fig.29 Simulation Model of INC

SIMULATION RESULTS FOR WIND TURBINE
Fig.31 PMSG Phase current and Phase voltage
and Inverter Phase current and Phase voltage.
Fig.30 Wind Turbine output characteristics

Time(sec)
The figure shows wind turbine output
voltage, output current and output power.
Wind turbine output characteristics
which have been simulated for different
wind velocities. The wind turbine
modelled using the equations provide an
output voltage nearly 600v and output
power nearly 50kW.
Fig.32 Wind Turbine performance
50
-50

Time(s)
The figure shows PV module control
performance.
a) PV module varying temperature.
b) Solar irradiance.
c) PV output voltage.
d) PV output power.
e) PV module output current.
Fig.33 PV module performance

Fig.34 I-V and P-V characteristics for varying irradiance constant temperature

PARAMETERS VALUES
Load Voltage 600V
Load Current 0.16KA
Output Power 100KW
Table.5 Simulated Results of Hybrid System
Elapsed time of MPPT(s)
Irradiance 400W/m^2 600W/m^2 1000W/m^2
Temperature 21C 23C 25C
P&O 1.350743 1.66736 1.7038632
INC 1.48879 1.795515 1.8577312
FLC 1.545303 1.875628 1.9110148
Table.6 Comparison of the elapsed time for different MPPT
systems
Algorithm MPP Tracking Efficiency (%)
P&O 88.1
INC 92.5
FUZZY 94.6
Table.7 MPP Tracking Efficiencies of the different MPPT
methods

 This Thesis presents the modeling of hybrid system consisting of wind and solar.
 A 100KW hybrid model was developed using MATLAB/ simulink/ simpower systems.
 The wind generator used in the proposed system runs at constant speed.
 The PV array uses an MPPT algorithm to capture the maximum power.
 It is observed that the extraction of the maximum power from PV array is obtained using MPPT system.
 The perturb and observe(P&O), incremental conductance(INC) and Fuzzy logic controller algorithms has
been implemented.
 The proposed system has been simulated in MATLAB-SIMULINK environment.
 The dynamic behavior of the proposed model is examined under different operating conditions.
 Solar irradiance, temperature and wind speed data is gathered from wind power generation and solar PV
system.

REFERENCES
[1] Theodoros L. Kottas, “New Maximum Power Point Tracker For PV Arrays using Fuzzy Controller”, IEEE Transactions On Energy
Conversion, VOL.21, NO.3, SEPT 2006.
[2] Jayalakshmi N.S., D.N.Gaonkar, ”Maximum Power Point Tracking for Grid Integrated Variable Speed Wind based Distributed Generation
system with Dynamic Load”, International Journal Of Renewable Energy Research, Vol.4, No.2, 2014.
[3] Bader N.Alajmi, Khaled H. Ahmed, Stephen J. Finney, and Barry W. Williams, “Fuzzy Logic Control approach of a modified Hill-
Climbing method for Maximum power point in Microgrid standalone Photovoltaic system”, IEEE Transactions On Power Electronics, Vol.26,
No.4, April 2011.
[4] M.A. Mahmud, H.R .Pota and M.J. Hossain, “Nonlinear Current Control Scheme for a Single-Phase Grid- Connected PV system”, IEEE
Transactions On Sustainable Energy, Vol.5, No.1, Jan 2014.
[5] Ting-Chung Yu, Yu-Cheng Lin, “ A study on maximum power point tracking algorithms for photovoltaic systems”, IEEE Transactions On
Industrial Electronics, Vol.53, No.4, June 2006.
[6] Bader N. Alajmi, Khaled H. Ahmed,”Single-Phase Single-Stage Transformer less Grid-Connected PV system”, IEEE Transactions On
Power Electronics, Vol.28,No.6 June 2013.
[7] Monica Chinchilla, Santiago Arnaltes and Juan Carlos Burgos, “Control of Permanent-Magnet Generators Applied to Variable-Speed
Wind-Energy Systems Connected to the Grid” IEEE Transactions On Energy Conversion, Vol 21, NO, 1, March 2006 Pp. 130-135.
[8] C.Y Won, D.H. kim, W.S. Kim, ” A new maximum power point tracker of PV arrays using Fuzzy Logic Controller”, IEEE 25th Annu.
Power Electron. Spec. Conf., 1994, vol.1, pp. 396-403.
[9] Ashish Pandey, Nivedita Dasgupta,”High-Performance Alogorithms for Drift Avoidance and Fast Tracking in Solar MPPT System”, IEEE
Transaactions On Energy Conversion, Vol.23, No.2, June 2008.
[10] Fangrui Liu, Shanxu Duan,” A Variable Step Size Inc Mppt Method For Pv Systems”, IEEE Transaactions On Industrial Electronics,
Vol.55, No.7 July 2008.

MAXIMUM POWER POINT TRACKING ALGORITHMS APPLIED TO WIND-SOLAR HYBRID SYSTEM

MAXIMUM POWER POINT TRACKING ALGORITHMS APPLIED TO WIND-SOLAR HYBRID SYSTEM

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