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SIMULATION FOR
SMART GRID
INTEGRATION OF
SOLAR/WIND ENERGY
CONVERSION SYSTEM
1Dr.V.Jayalakshmi,
2Sujeet Kumar
1Associate Professor,
2UG Student
Department of EEE
BIHER, BIST, Bharath University
Chennai- 600073.
Abstract
Due to the fact that solar and wind power are
intermittent and unpredictable in nature, could
cause and create high technical challenges.
Unfortunately the actual energy conversion
efficiency of PV and wind energy system using
traditional controllers is low. To overcome this
problem and to improve the efficiency of the
system, MPPT with an intelligent control
techniques are used with closed loop system. In
this paper, the system is designed for constant
wind speed and varying solar irradiation and
insolation. Maximum power point tracking
(MPPT) algorithm is used to extract the
maximum power from PV array. Fuzzy logic
controller and PI controller are used to control
the duty cycle of the converter switch thereby
extracting the maximum power from solar
array. The system consists of photovoltaic (PV)
array, wind energy conversion system (WECS),
boost converter, and LC filter. The entire
proposed system has been modeled and
simulated using MATLAB/simulink software.
Keywords:, Fuzzy logic controller,Maximum
power point tracking, PI controller,Photo voltaic
system.
I. INTRODUCTION
Globally we are facing two major issues now
regarding electric power generation. The existing
power systems are fossil fuels like coal, gas etc.
burning of these fossil fuels giving rise to the
emission of carbon dioxide into the environment.
This action is the major reason for global warming
effects that cause environmental impacts.[2-7].The
available global resources are decreasing day by
day. The limitation of global resources of fossil and
nuclear fuels, push us to go for alternative sources
of energy & new way has to be found to balance
the supply and demand without fossil and nuclear
fuels.[13]. The renewable energy offers alternative
sources of energy which are generally pollution
free. But solar and wind power is naturally
intermittent and can create technical challenges to
operate.[8-12]. The peak operating time for wind
and solar systems occur at different times of the
day for different places. Therefore the solar and
wind energy system can’t provide a continuous
supply, can generate electricity only during sunny
and windy days.[14-19]. Therefore integration of
both solar and wind energy system into an optimum
combination improves overall efficiency of the
system. [20-21].The integration of PV and wind
system become more economical to run since the
weakness of one system can be complimented by
the strength of the other one.[1].
II. SMART GRID
The continuous and expanded growth of the share
of renewables in centralised and decentralised grids
requires an effective new approach to grid
management, making full use of “smart grids” and
“smart grid technologies”. Existing grid systems
already incorporate elements of smart functionality,
but this is mostly used to balance supply and
demand. Smart grids incorporate information and
communications technology into every aspect of
electricity generation, delivery and consumption in
order to minimize environmental impact, enhance
markets, improve reliability and service, and reduce
International Journal of Pure and Applied MathematicsVolume 119 No. 12 2018, 7897-7910ISSN: 1314-3395 (on-line version)url: http://www.ijpam.euSpecial Issue ijpam.eu
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costs and improve efficiency. These technologies
can be implemented at every level, from generation
technologies to consumer appliances. As a result,
smart grids can play a crucial role in the transition
to a sustainable energy future in several ways:
facilitating smooth integration of high shares of
variable renewables; supporting the decentralised
production of power; creating new business models
through enhanced information flows, consumer
engagement and improved system control; and
providing flexibility on the demand side.The
introduction of smart Grid technology is an
essential requirement that reduces overall green
house gas emissions with demand management that
encourages energy efficiency, improves reliability
and manages power more efficiency and more
effectively. Smart Grid is the system comprising of
the communication technology, information
technology and power system. Smart Grids is the
combination of centralized power plants and
distribute power generators that allows multi
directional power flows. Its two ways power
combination system. Smart Grid is an electric
power system which accomplishes in the Grid
system that is generators, transmission, distribution
operators and electric consumers are communicate
and work with each other to raise the efficiency
and reliability of the Grid. The key characteristics
of smart grid include.
Grid optimization, system reliability and
operational efficiency. Distributed generation not
only traditional large power stations, but also
individual PV panels, micro-wind, etc. Advanced
metering infrastructure (AMI) including smart
meters. Grid-scale storage. Demand response.
Plug-in hybrid electric vehicles (PHEVs) and
vehicle to grid (V2G).
Intelligent and efficient: Smart Grid is capable of
sensing system over loads and rerouting power to
prevent or minimize outage. It is efficient to meet
the increasing demand without adding any
infrastructure.
Accommodating: It can accommodate energy from
fuel sources as well as R E sources.
Reduce Global Warming: Possible to integrate
large scale R E into Grid that reduces global
warming effects.
Reliability It improves power quality and
reliability as well as enhance the capacity of
existing network.[27-29]
Increasing renewable electricity generation is an
essential component in achieving a doubling of the
renewable energy share in the global energy mix.
Such a transition is technically feasible, but will
require upgrades of old grid systems [30-37]and
new innovative solutions to accommodate the
deferent nature of renewable energy generation. In
particular, smart grids are able to incorporate the
following characteristics:
Variability.Some forms of renewable electricity,
notably wind and solar, are dependent on an ever-
fluctuating resource (the wind and the sun,
respectively). [32-34]As electricity supply must
meet electricity demand at all times, efforts are
required to ensure that electricity sources or
electricitydemand is available that is able to absorb
this variability.
Distributed generation.Distributed renewable
generation smaller-scale systems, usually privately
owned and operated represent a new and different
business model for electricity. [35-37]Traditional
utilities are often uneasy about allowing such
systems to connect to the grid due to concerns over
safety, effects on grid stability and operation, and
the difficulties in valuing and pricing their
generation.
High initial cost.Renewable electricity generating
technologies typically have higher first costs and
International Journal of Pure and Applied Mathematics Special Issue
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lower operating costs than fossil-fuelled electricity
generating technologies. Although renewable may
be “cost effective” on a lifecycle basis, some
electricity systems particularly in developing
countries simply do not have access to sufficient
capital to invest in renewables.
Smart grid technologies can directly address these
three challenges of[25-29] renewable electricity
generation. In addition, smart grids over added
benefits that can further ease the transition to
renewables.
Applications of smart grid technologies can be
found across the world, from isolated islands to
very large integrated systems. For developed
countries, smart grid technologies can be used to
upgrade, modernize or extend old grid systems,
while at the same time providing opportunities for
new, innovative solutions to be implemented. For
developing and emerging countries, smart grid
technologies are essential to avoid lock-in of
outdated energy infrastructure, attract new
investment streams, and create efficient and
flexible grid systems that will be able to
accommodate rising electricity demand and a range
of different power sources. Smart grid technologies
are already making significant contributions
toelectricity grid operation in several countries.
Denmark, Jamaica, the Netherlands, Singapore, and
the United States (New Mexico and Puerto Rico)
are have successful combinations of smart grid
technologies with renewable energy integration.
The successful implementation of smart grid
technologies for renewables requires changes in
policy and regulatory frameworks to address non-
technical issues, particularly with regards to the
distribution of benefits and costs across suppliers,
consumers and grid operators.
Many of the benefits of smart grids and renewable
depend largely on how projects are implemented.
Effective project planning and execution are key to
realizing these benefits.[28-29] It is crucial to
perform tests to ensure that smart grid technologies
will integrate successfully with legacy hardware
and back-office systems before developing a new
project. Power system data with good spatial and
temporal granularity is important for analyzing the
potential benefits of smart grid projects. Grid
operators considering smart grid projects should
start gathering hourly load data as [11-19]soon as
practical, preferably at the feeder level. Once smart
grid projects are in progress, success often depends
on realising the substantial value of the large
amounts of data generated. Smart grid technologies
can enable renewables, and make better use of
existing infrastructure and hence increase the
efficiency of the system.
III.THE PHOTOVOLTAIC MODULE
The general mathematical model for the solar cell
has been studied over the past three decades. The
circuit of the solar cell model, which consists of a
photocurrent, diode, parallel resistor (leakage
current) and a series resistor; is shown in Fig. 1.
According to both the[25-29] PV cell circuit shown
in Fig. 1 and Kirchhoff’s circuit laws, the
photovoltaic current can be presented as follows
Where Igcis the light generated current, Io is the
dark saturation current dependant on the cell
temperature, e is the electric charge = 1.6 x 10-19
Coulombs, K is Boltzmann’s constant = 1.38 x 10-
23 J/K, F is the cell idealizing factor, Tcis the cell’s
absolute temperature, vdis the diode voltage, and
Rpis the parallel resistance.
International Journal of Pure and Applied Mathematics Special Issue
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Fig. 1: Single diode PV cell equivalent circuit
The photocurrent (Igc) mainly depends on the solar
irradiation and cell temperature, which is described
as
Where μscis the temperature coefficient of the
cell’s short circuit current, Trefis the cell’s
reference temperature, Iscis the cell’s short circuit
current at a 25°C and1kW/m2, and G is the solar
irradiation in kW/m2. Furthermore, the
cell’ssaturation current (Io) varies with the cell
temperature, which is described as
Where Ioα is the cell’s reverse saturation current at
a solar radiation and reference temperature, Vg is
the band-gap energy of the semiconductor used in
the cell, and Vocis the cells open circuit voltage. In
this study, a general PV model is built and
implemented using MATLAB/SIMULINK to
verify the nonlinear output characteristics for the
PV module. The proposed model is implemented,
as shown in Fig. 2. In this model, whereas the
inputs are the solar irradiation and cell temperature,
the outputs are the photovoltaic voltage and
current. The PV models parameters are usually
extracted from the manufactures data sheet.
Fig.2: Block diagram of the proposed system
IV. MODELING AND DESIGN OF A WIND
TURBINE AND INDUCTION
GENERATOR
Wind energy is not a constant source of energy. It
varies continuously and gives energy in sudden
bursts. About 50% of the entire energy is given out
in just 15% of the operating time. Wind strengths
vary and thus cannot guarantee continuous power.
It is best used in the context of a system that has
significant reserve capacity [36-37]such as hydro,
or reserve load, such as a desalination plant,
tomitigate the economic effects of resource
variability.The power extracted from the wind can
be calculated by the given formula:
Pw = 0.5ρπR3Vw3Cp(λ,β)
Pw = extracted power from the wind,
ρ = air density, (approximately 1.2 kg/m3 at
20¤ C at sea level)
R = blade radius (in m), (it varies between
40-60 m)
Vw = wind velocity (m/s) (velocity can be
controlled between 3 to 30 m/s)
Cp = the power coefficient which is a function
of both tip speed ratio (λ), and blade
pitch
angle, (β)(deg.) Power coefficient (Cp) is defined
as the ratio of the output power produced to the
power available in the wind. No wind turbine could
convert more than 59.3%of the kinetic energy of
the wind into mechanical energy turning a rotor.
This is known as the [37-39]Betz Limit, and is the
International Journal of Pure and Applied Mathematics Special Issue
7900
theoretical maximum coefficient of power for any
wind turbine. The maximum value of CPaccording
to Betz limit is 59.3%. For good turbines it is in the
range of 35-45%. The tip speed ratio(λ) for wind
turbines is the ratio between the rotational speed of
the tip of a blade and the actual velocity of the
wind. High efficiency 3-blade-turbines have tip
speed ratios of 6–7.
Wind turbine generators (WTGs) extract energy
from wind and convert it into electricity via an
aerodynamic rotor, which is connected by a
transmission system to an electric generator.
Today’s mainstream [12-18]WTGs have three
blades rotating on a horizontal axis, upwind of the
tower Two-blade WTGs and vertical-axis WTGs
are also available. In general, a WTG can begin to
produce power in winds of about 3 m/s and reach
its maximum output around 10 m/s to 13 m/s.
Power output from a WTG increases by the third
power of wind speed, i.e. a 10 % increase in wind
speed increases available energy by 33 %, and is
directly proportional to the rotor-swept area (the
area swept by the rotating blades). Power output
can be controlled both by rotating the nacelle
horizontally (yawing) to adapt to changes in wind
direction, and rotating the blades around their long
axes (pitching) to adapt to changes in wind
strength. Offshore wind power generation sites
generally have better wind resources than onshore
sites, [11-17]so WTGs installed in offshore sites
can achieve significantly more full-load hours.
Offshore wind farm development can also relax
many constraints faced by onshore wind farms,
such as transport and land occupation.
V. MPPT TECHNIQUES.
Solar and wind energy system is naturally
intermittent.
The P V system does not provide constant energy
because its output power changes into temperature
and insulations level. The actual energy conversion
efficiency of P V Module is low and it ranges from
7% to 15%.[18-24] To overcome these problems
MPPT with intelligent control techniques are used
to extract maximum available power from PV
System. The power output from the solar panel is
function of irradiation level and temperature. Given
operating conditions, we have a curve which gives
the voltage level maintained by the panel for a
particular value of current. This is known as
characteristics of cell. The intersection point of
load line with characterizing plot is the operating
point.
MPPT is an electronic system that operates the PV
panel in a manner that allows the module to
produce all the power they [11-24]are capable of. It
varies the electrical operating point of the module
so that the modules are able to deliver maximum
available power.
Many techniques are used to track MPP have been
proposed in early nineties. Conventional algorithm
such as P and O fail to reach the maximum power
point of PV system because of using fixed step
size. These problems are overcome by using
intelligent control techniques to track MPP of PV.
The conventional algorithm changes the value of
duty cycle of switching signal of convertor to track
MPP by the fixed step each time. Because of this
the optimal values are not reached. The output
power can be increased by tracking MPP of PV
module by using a controller connected to a DC –
DC convertor. [40-42]The MPP changes with the
insolation level and temperature due to the non
linear characteristics of PV module. Each type of
PV module can have its own specific
characteristics. In general there is a single point in
the V-I or V-P called the maximum power point at
which the entire PV system is operated with
maximum efficiency and produces its maximum
output power. This point can be located with the
help of MPPT.
International Journal of Pure and Applied Mathematics Special Issue
7901
VI. PI CONTROLLER:
PI controller is mainly used to eliminate the steady
state error resulting from P controller. However, in
terms of the speed of the response and overall
stability of the system, it has a negative impact.
This controller is mostly used in areas where speed
of the system is not an issue. Since P-I controller
has no ability to predict the future errors of the
system it cannot decrease the rise time and
eliminate the oscillations. If applied, any amount of
I guarantees set point overshoot. A PI controller
attempts to correct the error between a measured
process variable and a desired set point by
calculating and then outputting a corrective action
that can adjust the process accordingly. The
proportional term makes the change in output that
is proportional to the current error value. The
proportional response can be adjusted by
multiplying the error by a constant value, Kp called
as proportional gain. The integral term causes the
steady-state error to reduce to zero, which is not the
case for proportional-only control in general. [25-
29]The integral term is proportional to both the
magnitude of error and the duration of the error.
The magnitude of the contribution of the integral
term to the overall control action is determined by
the integral gain Ki.
PI controller is the co-operation of both propotional
and integral action. The analytical expression can
be given
as:
𝑃 = 𝐾𝑝 𝑒 + 𝐾𝑝 𝐾𝑖 𝑒 𝑑𝑡 + 𝑝 ( 0 )
Where,
P = controller output
Kp = proportional gain
Ki = integral constant
E = error input
P(0) = initial value of controller output
The PI controller gain and integral gain are
obtained by Zeigler Nichols method. The output
voltage is compared with reference voltage and the
error signal is given as input to the PI Controller.
The PI Controller minimizes the error and the
output of the PI controller is given to discrete
power generator.
VII. FUZZY LOGIC CONTROLLER
The O/P voltage of PV module is varies with
varying temperature and varying insulation. So, the
output power also varies. To extract the maximum
power from PV modules MPPT with FLC is used.
The error and change in error values are calculated
using
Error (K) = P(K) – P(K-1) / V(K) – V(K-1)
Change in error (K) = error (K) – error (K-1)
where P(K) is the instant power of PV system. The
input error (K) shows whether the operating point
is located to the left or right of the MPP at the
instant K.
The change in error (K) indicates the movement of
operating point.
The error and change in error are given as inputs to
FLC. The FLC examines the output power at each
instant (K) and determines the change in power
relative to voltage dP/dV.
If dP/dV is greater than Zero the controller change
the duty cycle of pulse width modulation until the
power is maximum or dP/dV is equal to Zero.
If value is less than Zero then the controller
changes the duty cycle to decrease the voltage and
the power is maximum.[7-14]
The FLC’s output is given to PWM generator and
fed as duty cycle to the switch corresponding to the
solar input of the convertor.
International Journal of Pure and Applied Mathematics Special Issue
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The basic function of FLC are fuzzification, rule
base, defuzzification.
Fuzzification: Fuzzification includes the design of
input and output membership function.
Rule base: It defines relationship between the input
and output membership function. The control rules
are evaluated by the inference mechanism.
Defuzzification: it uses center of gravity to
compute the output of FLC. The output is nothing
but duty cycle.
Fuzzification stage converts input variable into
linguistic variable based on a[14-18] membership
function. As fuzzy levels increases, the accuracy
will increase.
The fuzzy logic controller output is converted from
a linguistic variable to a numerical variable using
membership function in the defuzzification stage.
By defuzzification, the controller produces an
analog output signal which can be converted to a
digital signal and controls the power convertor of
MPPT system.
The effectiveness of FLC is depends upon the
accuracy of the calculation of error and its variation
and rule based table developed by users.[21-29] For
better efficiency the membership functions and the
rule base table can be continuously updated to
achieve optimum performance.
Fig 3: Block diagram of fuzzy logic control
based MPPT
VII. SIMULATION RESULTS PV AND
WIND ENERGY CONVERSION
SYSTEM USING PI CONTROLLER
Fig 4 Circuit diagram for PV and wind energy
system using PI controller
PV and wind energy system with PI controller has
shown in fig. 4 For any PV system, the output
power can be increased by tracking the MPP
(Maximum Power Point) of the PV module by
using a controller connected to a dc- dc converter.
However, the MPP changes with insolation level
and temperature due to the nonlinear characteristic
of PV modules. The boost converter is a type of
DC-DC converter that has an output voltage
magnitude that is either greater than or less than the
input voltage magnitude. It is a switch mode power
supply with a similar circuit topology to the boost
converter. The output voltage is adjustable based
on the duty cycle of the switching MOSFET. Also,
the polarity of the output voltage is opposite to the
input voltage. Neither drawback is of any
consequence if the power supply is isolated from
the load circuit as the supply and diode polarity can
simply be reversed. The switch can be on either the
ground side or the supply side. While in the On-
state, the input voltage source is directly connected
to the inductor (L). [34-38]]This results in
accumulating energy in L. In this stage, the
capacitor supplies energy to the output load. While
in the Offstate, the inductor is connected to the
output load and capacitor, so energy is transferred
from L to C and R.
International Journal of Pure and Applied Mathematics Special Issue
7903
Fig 5 output voltage of PV panel
The figure 5 shows the output voltage of pv panel.
The irradiation disturbance occurred at 0.25
second. The output voltage varies from 12 V to
14V.
Fig 6 Output voltage of RL load
The figure 6 shows the output voltage of RL load.
The irradiation disturbance occurred at 0.25
second. The output voltage varies from 60V to 63V
at 0.25 second due to the irradiation disturbance.
Due to PI controller action the voltage settled to
normal value after a rise time of 0.35 second at 0.6
second.
Fig 7 Output current of RL load
The figure 7 shows the output current of RL load.
The irradiation disturbance occurred at 0.25
second. The output current changes at 0.25 second
due to the irradiation [31-39]disturbance. Due to
PIcontroller action the current settled to normal
value after a rise time of 0.35 second at 0.6 second
Fig 8 Output power of RL load
The figure 8 shows the output power of RL load.
The irradiation disturbance occurred at 0.25
second. The output power changes at 0.25 second
due to the irradiation disturbance. Due to PI
controller action the power settled to normal value
after a rise time of 0.35 second at 0.6 second
The figure 4 shows the circuit arrangement of PV
and wind energy system with PI. To test the
performance of the proposed algorithm, the PV
system has been simulated in Matlab/Simulink. The
model as shown in figure 4 is composed of PV and
wind system, boost converter, MPPT controller,
and resistance and inductive load. Controlled
voltage source is utilized to connect PV system
with boost converter.[40-42]
The key specification of PV panel is taken in this
study is listed below.
Parameter Values are
Maximumpower (Pm) 59.12 (W)
Open circuit voltage (Voc) 12.19 (V)
Short circuit current (Isc) 5.45 (A)
Current at Pm (Iamp) 4.85 (A)
Temp coefficient for Pm -0.46 (% / oC)
Temp coefficient for Voc -0.129 (V / oC)
Temp coefficient for Isc + 0.052 (% / °C)
By using the PI controller the error has been
minimized in the system and the efficiency is
improved.
PV AND WIND ENERGY CONVERSION
SYSTEM USING FUZZY
LOGICCONTROLLER
Fig 9 PV and wind system with Fuzzy logic
controller
International Journal of Pure and Applied Mathematics Special Issue
7904
The figure 9 shows the circuit arrangement of PV
and wind energy system with Fuzzy logic
controller. To test the performance of the proposed
algorithm, the PV system has been simulated in
Matlab/Simulink. [40-45]The model as shown in
fig 9 is composed of PV and wind system, boost
converter,MPPT with fuzzy logic controller, and
resistance and inductive load. Controlled voltage
source is utilized to connect PV system with boost
converter.
The key specification of PV panel is taken in this
study is listed below.
Parameter Values are
Maximumpower (Pm) 59.12 (W)
Open circuit voltage (Voc) 12.19 (V)
Short circuit current (Isc) 5.45 (A)
Current at Pm (Iamp) 4.85 (A)
Temp coefficient for Pm -0.46 (% / oC)
Temp coefficient for Voc -0.129 (V / oC)
Temp coefficient for Isc + 0.052 (% / °C)
Fig 10 Output voltage of solar panel
The figure 10 shows the output voltage of pv panel.
The irradiation disturbance occurred at 0.25
second. The output voltage varies from 12 V to
14V.
Fig 11 Output voltage across RL load
The figure 11 shows the output voltage of RL load.
The irradiation disturbance occurred at 0.25
second. The output voltage varies from 60V to 66V
at 0.25 second due to the irradiation disturbance.
Due to fuzzy logic controller action the voltage
settled to normal value after a rise time of 0.09
second at 0.33 second.
Fig 12 Output current of RL load
The figure 12 shows the output current of RL load.
The irradiation disturbance occurred at 0.25
second. [12-19]The output current varies at 0.25
second due to the irradiation disturbance. Due to
fuzzy logic controller action the current settled to
normal value after a rise time of 0.09 second at
0.33 second.
Fig 13 Output power of RL load
The figure 13 shows the output power of RL load.
The irradiation disturbance occurred at 0.25
second. The output power varies at 0.25 seconddue
to the irradiation disturbance. Due to fuzzy logic
controller action the power settled to normal value
after a rise time of 0.09 second at 0.33 second. [9-
11]
By using the Fuzzy logic controller the error has
been minimized in the system and the efficiency is
improved.
The following Table 2 shows the comparison of PI
and Fuzzy logic controller with settling time and
steady state error. [14-19]From the table it is
concluded that the rise time for fuzzy controller is
very less comparing with PI controller and hence
International Journal of Pure and Applied Mathematics Special Issue
7905
the transient response of the fuzzy logic controller
is fast.
Converter Tr Ts Tp Ess
PI controller 0.35 0.6 0.39 3.3
FUZZY
controller 0.08 0.33 0 0.03
Table 2 Comparison of PI and Fuzzy logic
controller
VIII. CONCLUSION
The results of maximum power output of PV
incorporating FLC are compared with the output of
PV module with PI controller when it is connected
to load under varying irradiance levels. It is clear
from the results in Table 1that significant increase
in the power output is obtained by using FLC.
Itshows the transient response of MPPT algorithm
with PI and FLC when PV is subjected to different
irradiation. It can be seen that Fuzzy logic
controller has extremely fast response with rise
time equals to 0.08 second. The proposed system
showed its ability to reach MMP under sudden
changes of irradiation and partial shading.
Simulation results have shown that using Fuzzy
logic controller has great advantages over
conventional methods and PI controller.. The Fuzzy
Logic Controlled closed loop system is more
effective for the non – linear system and it can find
the point of Maximum Power Point in a shorter
time.
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of Pure and Applied Mathematics, V-
116, I-14 Special Issue, PP-189-194,
2017
12. Peter, M., Dayakar, P., Gupta, C., A
study on employee motivation at
Banalari World Cars Pvt Ltd Shillong,
International Journal of Pure and
Applied Mathematics, V-116, I-18
Special Issue, PP-291-294, 2017
13. Peter, M., Kausalya, R., A study on
capital budgeting with reference to
signware technologies, International
Journal of Pure and Applied
Mathematics, V-116, I-18 Special
Issue, PP-71-74, 2017
14. Peter, M., Kausalya, R., Akash, R., A
study on career development with
reference to premheerasurgicals,
International Journal of Pure and
Applied Mathematics, V-116, I-14
Special Issue, PP-415-420, 2017
15. Peter, M., Kausalya, R., Mohanta, S., A
study on awareness about the cost
reduction and elimination of waste
among employees in life line
multispeciality hospital, International
Journal of Pure and Applied
Mathematics, V-116, I-14 Special
Issue, PP-287-293, 2017
16. Peter, M., Srinivasan, V., Vignesh, A.,
A study on working capital
management at deccan Finance Pvt
Limited Chennai, International Journal
of Pure and Applied Mathematics, V-
116, I-14 Special Issue, PP-255-260,
2017
17. Peter, M., Thooyamani, K.P.,
Srinivasan, V., A study on performance
of the commodity market based on
technicalanalysis, International Journal
of Pure and Applied Mathematics, V-
116, I-18 Special Issue, PP-99-103,
2017
18. Philomina, S., Karthik, B., Wi-Fi
energy meter implementation using
embedded linux in ARM 9, Middle -
East Journal of Scientific Research, V-
20, I-12, PP-2434-2438, 2014
19. Philomina, S., Subbulakshmi, K.,
Efficient wireless message transfer
system, International Journal of Pure
and Applied Mathematics, V-116, I-20
Special Issue, PP-289-293, 2017
20. Philomina, S., Subbulakshmi, K.,
Ignition system for vechiles on the
basis of GSM, International Journal of
Pure and Applied Mathematics, V-116,
I-20 Special Issue, PP-283-286, 2017
21. Philomina, S., Subbulakshmi, K.,
Avoidance of fire accident by wireless
sensor network, International Journal of
Pure and Applied Mathematics, V-116,
I-20 Special Issue, PP-295-299, 2017
22. Pothumani, S., Anuradha, C.,
Monitoring android mobiles in an
industry, International Journal of Pure
and Applied Mathematics, V-116, I-20
Special Issue, PP-537-540, 2017
23. Pothumani, S., Anuradha, C., Decoy
method on various environments - A
survey, International Journal of Pure
and Applied Mathematics, V-116, I-10
Special Issue, PP-197-199, 2017
24. Pothumani, S., Anuradha, C., Priya, N.,
Study on apple iCloud, International
Journal of Pure and Applied
Mathematics, V-116, I-8 Special Issue,
PP-389-391, 2017
25. Pothumani, S., Hameed Hussain, J., A
novel economic framework for cloud
and grid computing, International
Journal of Pure and Applied
Mathematics, V-116, I-13 Special
Issue, PP-5-8, 2017
26. Pothumani, S., Hameed Hussain, J., A
novel method to manage network
International Journal of Pure and Applied Mathematics Special Issue
7907
requirements, International Journal of
Pure and Applied Mathematics, V-116,
I-13 Special Issue, PP-9-15, 2017
27. Pradeep, R., Vikram, C.J.,
Naveenchandra, P., Experimental
evaluation and finite element analysis
of composite leaf spring for automotive
vehicle, Middle - East Journal of
Scientific Research, V-12, I-12, PP-
1750-1753, 2012
28. Prakash, S., Jayalakshmi, V., Power
quality improvement using matrix
converter, International Journal of Pure
and Applied Mathematics, V-116, I-19
Special Issue, PP-95-98, 2017
29. Prakash, S., Jayalakshmi, V., Power
quality analysis & power system
study in high voltage systems,
International Journal of Pure and
Applied Mathematics, V-116, I-19
Special Issue, PP-47-52, 2017
30. Prakash, S., Sherine, S., Control of
BLDC motor powered electric vehicle
using indirect vector control and sliding
mode observer, International Journal of
Pure and Applied Mathematics, V-116,
I-19 Special Issue, PP-295-299, 2017
31. Prakesh, S., Sherine, S., Forecasting
methodologies of solar resource and PV
power for smart grid energy
management, International Journal of
Pure and Applied Mathematics, V-116,
I-18 Special Issue, PP-313-317, 2017
32. Prasanna, D., Arulselvi, S., Decoupling
smalltalk from rpcs in access points,
International Journal of Pure and
Applied Mathematics, V-116, I-16
Special Issue, PP-1-4, 2017
33. Prasanna, D., Arulselvi, S., Exploring
gigabit switches and journaling file
systems, International Journal of Pure
and Applied Mathematics, V-116, I-16
Special Issue, PP-13-17, 2017
34. Prasanna, D., Arulselvi, S.,
Collaborative configurations for
wireless sensor networks systems,
International Journal of Pure and
Applied Mathematics, V-116, I-15
Special Issue, PP-577-581, 2017
35. Priya, N., Anuradha, C., Kavitha, R.,
Li-Fi science transmission of
knowledge by way of light,
International Journal of Pure and
Applied Mathematics, V-116, I-9
Special Issue, PP-285-290, 2017
36. Priya, N., Pothumani, S., Kavitha, R.,
Merging of e-commerce and e-market-a
novel approach, International Journal of
Pure and Applied Mathematics, V-116,
I-9 Special Issue, PP-313-316, 2017
37. Raj, R.M., Karthik, B., Effective
demining based on statistical modeling
for detecting thermal infrared,
International Journal of Pure and
Applied Mathematics, V-116, I-20
Special Issue, PP-273-276, 2017
38. Raj, R.M., Karthik, B., Energy sag
mitigation for chopper, International
Journal of Pure and Applied
Mathematics, V-116, I-20 Special
Issue, PP-267-270, 2017
39. Raj, R.M., Karthik, B., Efficient survey
in CDMA system on the basis of error
revealing, International Journal of Pure
and Applied Mathematics, V-116, I-20
Special Issue, PP-279-281, 2017
40. Rajasulochana, P., Krishnamoorthy, P.,
Ramesh Babu, P., Datta, R., Innovative
business modeling towards sustainable
E-Health applications, International
Journal of Pharmacy and Technology,
V-4, I-4, PP-4898-4904, 2012
41. Rama, A., Nalini, C., Shanthi, E., An
iris based authentication system by eye
localization, International Journal of
Pharmacy and Technology, V-8, I-4,
PP-23973-23980, 2016
42. Rama, A., Nalini, C., Shanthi, E.,
Effective collaborative target tracking
in wireless sensor networks,
International Journal of Pharmacy and
International Journal of Pure and Applied Mathematics Special Issue
7908
Technology, V-8, I-4, PP-23981-23986,
2016
43. Ramamoorthy, R., Kanagasabai, V.,
Irshad Khan, S., Budget and budgetary
control, International Journal of Pure
and Applied Mathematics, V-116, I-20
Special Issue, PP-189-191, 2017
44. Ramamoorthy, R., Kanagasabai, V.,
Jivandan, S., A study on training and
development process at Vantec
Logistics India Pvt Ltd, International
Journal of Pure and Applied
Mathematics, V-116, I-14 Special
Issue, PP-201-207, 2017
45. Pradeep, R., Vikram, C.J.,
Naveenchandran, P., Experimental
evaluation and finite element analysis
of composite leaf spring for automotive
vehicle, Middle - East Journal of
Scientific Research, V-17, I-12, PP-
1760-1763, 2013
International Journal of Pure and Applied Mathematics Special Issue
7909
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