This research suggests maximum power point tracking (MPPT) for the solar photovoltaic (PV) power scheme using a new constant voltage (CV) fractional order incremental conductance (FOINC) algorithm. The PV panel has low transformation efficiency and power output of PV panel depends on the change in weather conditions. Possible extracting power can be raised to a battery load utilizing a MPPT algorithm. Among all the MPPT strategies, the incremental conductance (INC) algorithm is mostly employed due to easy implementation, less fluctuations and faster tracking, which is not only has the merits of INC, fractional order can deliver a dynamic mathematical modelling to define nonlinear physiognomies. CVFOINC variation as dynamic variable is exploited to regulate the PV power toward the peak operating point. For a lesser scale photovoltaic conversion scheme, the suggested technique is validated by simulation with dissimilar operating conditions. Contributions are made in numerous aspects of the entire system, including new control algorithm design, system simulation, converter design, programming into simulation environment and experimental setup. The results confirm that the small tracking period and practicality in tracking of photovoltaic array.
1. Introduction
Renewable energy sources are considered as an important source of energy in the twenty first century that is in use to fulfil our needs and growing demands of electricity. Among all natural energy sources, solar energy is more attractive, because it is easy to obtain anywhere and anytime throughout of the day and do not contribute global warming
[1]
. The extraordinary diffusion of solar PV system in electricity generation is evident from the fact that the PV scheme is anticipated to be the largest source of electricity generation among all the accessible renewable energy sources
[2]
. The PV modules are primarily a current source device and the current is produced when light falls on the surface of solar device. Characteristics curve of the PV module shows its nonlinear behavior. The nonlinear VI curve of PV module has only one point of maximum power extraction. Therefore, the energy harvesting at maximum efficiency is not simple enough. The survival of only one unique point of maximum power requires special techniques to function the scheme at the point of maximum power. These operating techniques are named as MPPT
[3]
. MPPT techniques control the power electronic interface such that the source impedance is matched with the load impedance and hence maximum power is transferred. In contrast of the nonlinear characteristics, MPPT techniques are vital for any solar PV system.
Different methods have been reported in literature for tracking the Maximum Power Point (MPP). In recent years, a large number of techniques have been proposed for tracking the MPP as follows: In Ahmed M.Kassem
[4]
this paper proposes the optimal technique to make the optimum chopping ratio of buckboost converter with optimizing it’s efficiency in photovoltaic water pumping system using MPPT. In addition, NARMA controller based on ANN approach is applied to optimize the ratio for PV maximum power at any irradiation level with fast responses good performance. In Kashif Ishaque et al.
[5]
was proposed particle swarm optimization(PSO) algorithm in MPPT eliminated the conventional PI control method. Since this method is based on optimized search method, it overcomes the common drawback of the conventional MPPT. This proposed algorithm used or employed in buckboost converter and yields an average MPPT efficiency of 95%. In A.D. Karlis et al.
[6]
details the offline trained fuzzy cognitive network(FCN) on MPPT gives a good maximum power operation of a PV array at any different conditions such as insolation, temperature, etc. In Hamed Mashinchi Mahery, Ebrahim Babaei.
[7]
proposed mathematical modelling of buckboost DC/DC converter in continuous conduction mode (CCM). In this method, using the Laplace transform the relations of inductor current and output voltage are obtained. Followed by calculated inductor current and voltage using the Ztransform. In ChiaHung Lin et al
[8]
, proposes the fractional order incremental conductance method (FOINC) for providing maximum power for PV array. This FOINC provides a dynamic mathematical model to adjust the PV array voltage towards the maximum power point. In YuChi Wu et.al
[9]
, suggests a three point weighting method that incorporates midpoint tracking to improve the limitation of the P&O and to enhance the efficiency of the threepoint weighting method. It was establish that the proposed method tracked better than the threepointweighting method, and it was capable of improving the deficiency of P&O method that has difficulty to track from the opencircuit voltage as well as enhancing the precision of the threepointweighting method in the case of zeroweight. In JungWoo Baek, et al
[10]
, proposes composed perturb and observe (PO) and constant voltage (CV) method. PO method is simple to realize and CV method is possible to tracking MPP with low radiation.
The Fuzzy Logic and/or Neural Network based MPPT technique have good performance under fast changing environmental circumstances and displays improved performance than the P&O method
[11]
. However, the main drawback of this technique is that its efficiency is extremely reliant on the technical information of the engineer in calculating the error and approaching up with the fuzzy rule based table. It is importantly reliant on how a designer assembles the system based on his experience and skill. Perturb and observe algorithm can be failure under fast varying environmental circumstances. The INC technique has partly solved divergence of perturb and observe model
[12]
. In this research proposed a new technique that will tune the online MPPT techniques based on varying weather conditions. The designed algorithm modifies the existing conventional INC controller based on constant voltagefractional order differentiator is implemented. The fractional order differentiator designed for numerical evaluation of fractional derivatives, Riemann Liouville definition and Grunwald Letnikov definition
[8]
. The suggested algorithm is implemented into MATLAB/Simulink environment and it is tested and validated. For a lesser scale photovoltaic power transformation scheme, the result shows computational efficiency and tracing time reduction. This research is organized as follows: section two discusses the problem formulation, equivalent model of PV, new CVFOINC control algorithm and DC to DC boostbuck converter. Section three describes the simulation setup and results. Section four and five give the experimental results and conclusion of the suggested system.
2. Problem Formulation of Proposed System
The block diagram for the suggested system is shown in
Fig. 1
. which contains PV panel, new CVFOINC MPPT algorithm, DC to DC boostbuck converter and load. The power switches of the framed DC to DC boostbuck converter are controlled by the gate drivers programmed via a controller module. The framed converter delivers required levels of the output power to the stand alone battery load. The impedance of the battery load should be assumed as suitable one for subsequent analysis. The DC to DC converters are responsible for MPPT and voltage regulations. When converter switch is the ‘off’ state, the solar energy is transferred to the output storage capacitor by the boostbuck inductance. Varying the duty of switching time can regulate the input voltage and current. The proposed sub systems as follows.
The block diagram of prosed PV conversion system
 2.1 Equivalent model of PV
The photovoltaic cell is a pn semiconductor junction that transfers solar energy into electrical energy. Photovoltaic cells, the so called photovoltaic module, are consistent in a series and/or parallel configuration to arrangement photovoltaic arrays, as shown equivalent circuit of PV model in
Fig. 2
. It has nonlinear characteristics, and the mathematical model can be expressed as
[13]
. The intensity of solar radiation in surface space is 1.367 kW/m
^{2}
. The nonlinear VI characteristics of a solar panel are extracted, neglecting the low value of series resistance and output current is as follows.
Equivalent model of solar photovoltaic module
where I
_{o}
is the PV module output current(A), V
_{o}
the PV module output voltage(V), k the Boltzmann’s constant in joule per kelvin, q charge of an electron, A the pn junction ideality factor, T the cell temperature in K, I
_{rs}
is the cell reverse saturation current. The A factor in Eq. (1) determines the cell deviation from the ideal pn junction characteristics. The series of ideal value is between 1 and 5. The optimal transfer efficiency of the wavelength of light wave is around 0.7 micro meter to 0.8 micro meter and light spectrum is yellow light. The PV array power can be computed using product of voltage and current. i.e. P = V × I.
The voltage versus power (VP) and the current versus power (IP) characteristic of single PV module curve are nonlinear as shown in
Fig. 3
. The panel contain four module equal powers rating connected side to side, which are installed on the floor of the Electrical and Electronics Engineering laboratory at Government College of Engineering, Salem, India. Sponsored by IIT, Bombay. The panel is fixed incline angle. It can be noticed that, at fixed irradiance and temperature, there is a unique point corresponding to the maximum power that the PV module can generate. A tool for tracking this exact point is thus required so that the energy produced by the PV system can be maximized. The specification of single PV module, which contain 10 watts peak maximum power, 16.4 volt peak maximum voltage, 0.610 amps peak maximum current, 21 volts open circuit voltage and 0.700 amps short circuit current.
Voltagepower (VP) and currentpower(IP) curves of single PV module.
 2.1.1 Estimation of maximum electrical energy output for PV system
The power production of a photovoltaic panel is contingent linearly on the working temperature, reducing with T
_{OT}
. Effects of photovoltaic working temperature on photovoltaic electrical energy production can be articulated by the subsequent equation
With essential correction functional to h
_{EFS}
, the result would be the UAO, in units of watt hour per peak watt per day. UAO is a chosen parameter for the sizing workout in contrast to parameters such as h
_{EFS}
or global irradiation. UAO is given by the subsequent equation
Thus Eq. (2) can be altered for temperature corrected photovoltaic electrical energy production as follows
To show the significance and importance of containing the effects of photovoltaic working temperature in the photovoltaic electrical efficiency an existing linear expression for T corrected photovoltaic electrical efficiency η
_{OT}
is specified by
Where η
_{STC}
is the photovoltaic electrical efficiency at standard test conditions, β is module/array efficiency coefficient, T
_{STC}
is reference temperature at standard test conditions for photovoltaic electrical efficiency, T
_{OT}
is the average photovoltaic working temperature. The values of electrical energy and UAO for PV system is shown in
Table 1
. Power transformation efficiency at standard test conditions can be designed for component of photovoltaic panel. An appearance for calculating photovoltaic array power transformation efficiency at standard test conditions has been established as Eq. (6), when array consists of four sub modules
Electrical energy, unit array output for photovoltaic module
Electrical energy, unit array output for photovoltaic module
where, V
_{ocm}
is open circuit voltage of module, I
_{scm}
is short circuit current of module, Ms represents no. of modules in series in a sub module and M
_{p}
represents no. of parallel strings of series connected modules in a sub module. FF is fill factor, I
_{p}
peak intensity with value of 1000 W/m
^{2}
. All these parameters are observed at standard test conditions and delivered by producer specifications. Same established formula can be changed for ‘n’ number of sub modules of any given photovoltaic array.
 2.2 New CVFOINC MPPT algorithm design
The perturb and observe method is widely applied in MPPT due to its simple structure, little parameters requirement, then not requiring solar panel characteristics
[1

5]
. It operates by periodically perturbing the instantaneous terminal voltage then comparing the output power with the pervious perturbation cycle. When it attains maximum power, it has an oscillation problem around the desired operating point and contains some unexpected losses. To overcome the weakness of perturb and observe, the array terminal voltage of incremental conductance is always regulated according to its value relative to the voltage of the maximum power point. Though novel incremental conductance practices the gradient method to increase tracking speed and it can quickly optimize the slope of the output power versus the voltage to match the maximum power point. However, the nonlinear functions power versus voltage and current versus voltage are not easy to get the first order derives. In PV system, the solar temperature, radiation, and electricity conduction are irregular diffusion phenomena in inhomogeneous media, and their dynamic variations can describe the fractional order electrical production by noninteger derivative based equations. The fractional differential function depends on its entire past values, and its model performs likes a scheme with a long memory. This paper, the incremental various in the voltage and current will describe the reasonable estimates in fractional order calculus (FOC). The basic concept of FOC will be showed in the following session.
 2.2.1 Fractional order calculus
A fraction order structure comprises by a fractional differential or an integral equation, and systems covering few equations, has been deliberate in engineering and physical appliances, for example active control, signal processing, linear and nonlinear response controller. The generally utilized approaches have been anticipated for numerical assessment of fraction derivatives by RiemannLioville and GrunwaldLetnikov definition
[8
,
9]
. It reflect a continuous function f(t), where its α
^{th}
order derivative can be conveyed in reference
[14]
.
For all α, positive, negative, and/or zero, and m =0,1,2,3,4…Note, the select of α can be seen as selecting the spectacles that will be modeled. By selecting 0<α<1, anomalous phenomena, such as heat conduction, diffusion, viscoelasticity and electrodeelectrolyte polarization can be described
[15
,
16]
.
 2.2.2 Control process of constant voltage fractional order INC method
The PV and PI characteristics of a single cell are determined and expand to determine the behaviour of a PV modules, as shown in
Fig. 3
. It appears dI/dV < 0, with increasing voltage V as current I is decreasing. According to Eqs. (1), I and V depend on atmospheric conditions and electricity conduction. The anomalous phenomena can be described as fractional order differentiation. Thus, the dI/dV can be modified as
The effectiveness of the weighing is changed as α>0, and α is an even number. For 0 < α < 1 the expression can be termed as the fractional rate of the change of function. Eq. (9) is used to express the fractional order incremental changes of the current and voltage of the PV panel. The incremental conductance load can be modified as
where Z=0,−1,−2,−3,−4,… residue Γ( 0 ) =Res(Γ0) =1. Thus the procedure of FOINC method searches the voltage as a variable at which the maximum power point has an increasing or decreasing the duty cycle.
Fig. 4
shows the flowchart of the proposed CVFOINC control algorithm. By using the radiation meter or pyrometer, this control method can alteration the operation mode in the program. Up to the output power of the solar cell array reaches the maximum power point, the proposed control method increases or diminishes the output voltage of the solar cell output voltage as the same direction and it can be tracked the MPP. It adjusts the duty cycle using the instantaneous values I and V at present iteration step and their corresponding values of I
_{o}
and V
_{o}
stored at the end of the preceding iteration step. The incremental changes in current and voltage are approximated as, respectively. To avoid misjudging the working state under various conditions, the initial voltage V can be set to zero V or default values according to the temperature variations. According to the four judgements, the control procedure of CVFOINC method algorithm can be expressed as follows
[16]
:
Flowchart of new CVFOINC MPPT algorithm
Condition one: if
or (ΔV
^{α}
= 0 and ΔI = 0) no control action is needed. Condition two: if
or (ΔV
^{α}
= 0 and ΔI > 0) a control action is needed to add the ΔV
^{α}
to present voltage V with an increasing duty cycle. Condition three: if
or (ΔV
^{α}
= 0 and ΔI < 0) a control action is needed to reduce the ΔV
^{α}
to present voltage V with a decreasing the duty cycle. Condition four: compute output power is equal to product of output voltage and current and P = VI. If P
_{o}
< P, update the voltage V
_{o}
= V and I
_{o}
= I otherwise, terminate the control procedure. In the constant voltage control technique the output voltage of the solar cell modules has the constant voltage characteristic having the little bit of vibration amplitude about the solar radiation change. Therefore, it can be said to the constant voltage control method in which it sets as output voltage is equal 0.76 and it controls by the constant voltage. This method is not required the calculated power value for an output. The duty of the DC to DC converter is determined by the control circuit and the DC voltage of the output terminal is consistently maintained by output voltage value. The advantages of this control method have reducing the sensor of a panel and DC part. However, there is the drawback that is unable to track the MPP in solar radiation rapidly changes and the power efficiency is reduced.
In the proposed MPPT algorithm is the method for maximizing the efficiency of the output power of the solar cell modules with the solar radiation variation. The algorithm performed MPPT with the solar radiation variation is dissimilar. One case the solar irradiation is low, the constant voltage control method is performed and the other case to improve dynamic performance the FOINC is based on the fractional order incremental changes of the PV array terminal voltage and current to rapidly track the maximum output power. Therefore, the operation mode is changed in the low solar radiation to the constant voltage control method.
 2.3 DC to DC boostbuck converter design
The boostbuck converter has low switching losses and the highest efficiency among nonisolated DC to DC converters. It can also provide an improved output current characteristic due to the inductor on the output stage. Thus, the boostbuck configuration is a proper converter to be employed in deceitful the MPPT. The converter provides a negative polarity regulated output voltage with respect to the common terminal of the input voltage as shown in
Fig. 5
. Here the capacitor C
_{1}
acts as the primary means of storing and transferring energy from the input to the output. In steady state, the average inductor voltage V
_{L1}
and V
_{L2}
are zero.
DC to DC boostbuck converter
where V
_{C1}
is larger than both V
_{c}
and V
_{o}
. Assuming C
_{1}
to be sufficiently large, in steady sate the variation in vc1 from its average value V
_{C1}
can be assumed to be negligibly small i.e vc
_{1}
≅V
_{C1}
, even though it stores and transfers energy from the input to the output.
When the switch is off, the inductor current I
_{L1}
and I
_{L2}
flow to the load through the diode. The circuit is shown in
Fig. 6(a)
capacitor C
_{1}
is charged through the diode by energy from both the input and L
_{1}
. Current I
_{L1}
decreases, because V
_{C1}
is larger than V
_{d}
. Energy stored in L
_{2}
feeds the output. Therefore i
_{L2}
also decreases. When the switch is on, V
_{C1}
reverse biases the diode. The inductor currents i
_{L1}
and i
_{L2}
flow through the switch in
Fig. 6(b)
. Since V
_{C1}
>V
_{o}
, C
_{1}
discharges through the switch, transferring energy to the output and L
_{2}
. Therefore, i
_{L2}
increases. The input feeds energy to L
_{1}
causing i
_{L1}
to increase. The inductor currents i
_{L1}
and i
_{L2}
are assumed to be continuous. The voltage and current expressions in steady state can be obtained in two different ways. If we assume the capacitor voltage V
_{C1}
to be constant, then equating the integral of the voltage across L
_{1}
and L
_{2}
over one time period to zero yields
[2]
.
Converter waveforms: (a) switch off (b) switch on.
From Eqs. (14) and (15)
Assuming P
_{d}
=P
_{o}
gives
where I
_{L1}
=I
_{d}
and I
_{L2}
=I
_{o}
.
There is another way to obtain these expressions. Assume that the inductor currents i
_{L1}
and i
_{L2}
are essentially ripple free (i.e i
_{L1}
=I
_{L1}
and i
_{L2}
=I
_{L2}
). When the switch is off, the charge delivered to C
_{1}
equals I
_{L1}
(1−D)T
_{s}
. When the switch is on, the capacitor discharges by an amount I
_{L2}
DTs. Since in steady state the net change of charge associated with C
_{1}
over one time period must be zero.
and V
_{o}
/ V
_{d}
=D/1−D since P
_{o}
= P
_{d}
. Both methods of analysis yield identical results. The average input and output relations are similar to that of a buckboost converter. In practical circuits, the assumption of a nearly constant V
_{C1}
is reasonably valid. An advantage of this circuit is that both the input current and the current feeding the output stage are reasonably rippled free (unlike the buckboost converter where both these currents are highly discontinuous). It is possible to simultaneously eliminate the ripples in i
_{L1}
and i
_{L2}
completely. Leading to lower external filtering requirements. A significant disadvantage is the requirement of a capacitor C1 with a large ripple current carrying capability
[17]
.
3. Simulation Results and Discussions
The proposed methods were designed and tested on a Laptop Intel(R) Core(TM) i53210M CPU @ 2.50GHZ with 4.00GB RAM and MATLAB / Simulink software. PV array which was used in this study had a maximum power Pmax = 40Watts and an open circuit voltage of individual panel is V
_{OC}
= 21 V at a solar radiation of 1.0 kW/m
^{2}
and temperature of 25℃. The related specific parameters of the PV array and a DC to DC boostbuck converter are utilized between the PV array and the 12V battery for the purpose of MPPT. The overall control procedure consists of two stages: one is a MPPT algorithm keeping the PV array operating at the maximum power point. Second one is a voltage controllable converter adapting the PV array with the storage battery for energy transfer. However, the energy transfer is strongly influenced by the solar radiation and cell temperature (the surface of PV array), such as the angle of incidence of the sunlight and weather. These phenomena affect the efficiency of solar energy generation. The illustration of the closed loop system designed in MATLAB and Simulink is shown in
Fig. 7
, which includes the PV array electrical circuit, DC to DC boostbuck converter, and the MPPT algorithm. The converter components are chosen according to the values presented in
Table 2
. PV module is modelled using electrical characteristics to provide the output current and voltage of the PV panel. The provided current and voltage are fed to the converter and the controller simultaneously
[18]
.
Illustration of the closed loop proposed system.
Design specifications of the proposed system.
Design specifications of the proposed system.
The control tasks include quantifying the analog voltage and current of the photovoltaic array using voltage and current sensors, transform them into digital by means of an Analog to Digital Control (ADC), process the acquired information in a microcontroller, then them match to the predefined values to determine the following step, revert the pulse width modulation (PWM) to the gate drive, and hence control the switching of MOSFET
[19]
. The control loop frequently happens with respect to the sampling time, and the main program continues to track the Maximum power points. The key proposed power and control circuit components information is summarized in
Table 3
.
Key components used for the proposed system.
Key components used for the proposed system.
 3.1 Simulation results and description
To verify the proposed method, the MPPT procedure was used to control the output power in a PV array panel under lower solar radiation. Simulation has been performed when solar radiation and cell temperature changes with a transient. The characteristic of PV array will be changed when solar radiation and cell temperature change, which cause the VI curves of PV panel to change, where the specific radiation range between 200 W/m
^{2}
and 1000 W/m
^{2}
and cell temperature range between 20℃ and 80℃. In the proposed simulated model PI control loop is removed, and the duty cycle is in tune directly in the algorithm. To experiment the proposed system operation, the condition of changing irradiation was modelled. The temperature and irradiance lever is varying two levels. The first illumination and temperature level is 1000 W/m
^{2}
and 45℃ at t = 0.3 s, the illumination and temperature level suddenly changes to 800 W/m
^{2}
and 40℃. An illustration of the relationship between the duty cycle and PV output power are shown in
Fig. 8 (a)
and
(b)
respectively. To demonstrate the effectiveness of the algorithm mentioned in the flowchart.
Fig. 8 (a)
shows the change in duty cycle adjusted by the MPPT to extract the maximum power from the modules. The results from the
Fig. 8 (b)
show that the output power at G = 1000 W/m
^{2}
are 38 W and 800 W/m
^{2}
are 15.18 W respectively, which are absolutely the desired output power from current versus voltage curve of a PV modules. It also shows that the system provides the best desirable tradeoff between the two temperature and irradiation levels
[20]
. From the figure conclude the proposed MPPT algorithm which can automatically adjust the step size with track the maximum power output. The sampling period used for the proposed MPPT algorithm is chosen as 0.01 s. Therefore, the duty cycle of the power converter is updated every 0.01 seconds. The output power performance of MPPT method with a fixed step size 0.01 under an irradiance step change from 1000 W/m
^{2}
(at temperature T = 45℃) to 800 W/m
^{2}
(at temperature T = 40℃) at 0.3s. For the proposed MPPT method with allowable maximum duty size is 0.05 is also shown in
Fig. 8
. It is obvious the oscillations occurring at steady state are almost eliminated by the proposed MPPT algorithm. Also, the dynamic performance of the proposed method is obviously faster than the traditional method.
Change in (a) duty cycle and (b) power of the system due to the change in illumination and temperature level.
 3.2 Comparison results
In order to validate the designed new controller process, an enhanced CVFOINC controller is equated with exciting FOINC and conventional Perturb and Observe (P&O) MPPT technique with fixed irradiance and temperature at 1000W/m
^{2}
and 25℃ respectively.
Fig. 9.
demonstrations the superiority of the designed technique as it exemplifies the controller output power generated by the three controllers: conventional P&O and IncCond; and an improved VSS IncCond. The outcome shows visibly that the designed an enhanced CVFOINC algorithm influences quicker to the highest power with low oscillation compared to the exciting techniques.
Comparison waveform of proposed system.
4. Experimental Results and Discussions
To verify the functionality and performance of the suggested system is shown in
Fig. 10
. Which composed (a) photovoltaic panel with direct load test (b) DC to DC converter with controller. Four modules attached in one PV panel, each module contain ten watts power output made up of multi and mono crystalline silicon materials. The type of connection totally depends on the application where large current or voltage is required. The panel is fixed tilt angle in 39° south direction. A prototype of the boostbuck converter and control circuit was implemented. The ATMega 8 microcontroller was used to provide the control signals for the DC to DC converter. The C code of the CVFOINC algorithm (based on flowchart shown in
Fig. 4
) and PWM scheme is built, debugged and run with the help of the Arr studio development tool and Proisp software. Voltage measurement is required at the point where the PV module output is connected to the input of the DC to DC converter. The voltage at this point is the operating voltage of the PV panel. On the other hand, current measurement is also necessary to indicate the generated current of the PV array on each operating point. It is particularly important to determinate the weather condition, which is vital in connection with the accuracy of maximum power point tracking. For the aforementioned reason, the PV array voltage and current are measured using resistance divider sensors
[21]
.
Photos of prototype setup (a) PV array (b) DC to DC boostbuck converter with CVFOINC method MPPT algorithm.
 4.1 Results and description
The experimental data for a typical day on December 29
^{th}
2012 have been investigated. The intensity of sun solar radiation in outer space is around 1.367 kW/m
^{2}
. Each control action will act the MPPT procedure is undertaken continuous measurements of climate condition. Maximum PV operating temperature of the panel was found 28.21℃ at 12:00 noon when ambient temperature was measured 27.3 ℃ and solar radiation was 513 W/m
^{2}
. As the solar radiation and temperature slowly increase, the FOINC algorithm can rapidly track the maximum power at each step disturbance. The experimental tests confirm the proposed method can provide maximum power in practical applications. There are two general types of connecting modules of PV panel such as series and parallel. The type of modules connection totally depends on the application where large current or voltage is required. The purpose in the series arrangement is to increase the output voltage, while the parallel connection is prepared to increase the current. The interconnection of PV cells in a module itself is mostly in series to provide higher voltage. When modules are coupled in series, the entire voltage is the sum of each module voltage, nevertheless the current stays constant, and it is the slightest current of a module available in the configuration. In the hardware configuration, there are four modules connected in series and parallel arrangement. The sampling time of the system is selected to be 0.2 sec, which is the required time for the designed boostbuck converter to reach the steady state condition. The step size of duty cycle is preferred to be 0.2 sec, so the converter can smoothly track the maximum power point.
Fig. 11
shows the initial waveforms of current and voltage after connecting the PV module to the power circuit. There is some overshoot in both current and voltage waveforms, which was forecast from the simulation results shows in
Fig. 8(b)
. After further conducting test an indepth investigation on system performance under rapidly varying illumination levels, the numbers of modules were changed from four to three. The variations of the voltage, power and pulse width modulation of duty cycle of the proposed system is revealed in
Fig. 12
. As the sudden fluctuations of simulation results are close to the experimental CVFOINC conduction technique.
Fig. 13
shows the events where the solar radiation is fluctuating with real climatic data. It shows the performance of the day
[3]
.
Initial current and voltage after connecting to the MPPT with one module (channel 1 is current, channel 2 is voltage waveform).
Change in voltage, current and pulse width modulation when the number of PV modules is decreased from four to three.
Hourly difference of solar radiation.
5. Conclusion
This research designed a new constant voltage fractional order incremental conductance (CVFOINC) MPPT algorithm for a small photovoltaic transformation scheme. Under constantly changing environment conditions, the CVFOINC algorithm reduces the tracing period and average error less than the conventional methods. MPPT controller is joined with a CV and FOINC controller to develop the competence of the entire scheme. The benefits of the suggested technique are reduces the tracking time, tracking number, switching number and ensures the maximum amount of energy is transferred to the load. It is simplest way to implement in a microcontroller. Hence, the CVFOINC system a favourable way for further application of a standalone scheme, such as a PV power generating scheme and monitoring scheme.
BIO
Arulmurugan, R was born in Salem, India, in 1985. He is working towards the Ph.D. degree in Electrical Engineering, Anna University, Chennai, Regional zone Coimbatore. He is the recipient of the best faculty, best researcher award in 201213 and 201314 at Knowledge Institute of Technology. He has published more than 15 papers in refereed journals and International conference proceedings such as ElsevierElectrical power system research, International Journal of photo energy and IEEE xplorer, etc. He is the recipient of the best paper award in 2013 IEEE international conference on research and development prospects on engineering and technology. His main are of research activity is photovoltaic, MPPT, FLC, DC to DC buckboost converter.
Venkatesan, T was born in Salem, India, in 1971. He received B.E degree in Electrical and Electronics Engineering from NIT, Tiruchy, in 1997, the M.E degree in Power System Engineering from Annamalai University, India, in 2002, and receives Ph.D. in Anna University, Chennai, in 2013. Currently he is working as a Professor in K S Rangasamy College of Technology, Tiruchengode, India. His research interests are Economic Dispatch, Unit Commitment problem solution using soft computing techniques, photovoltaic and Maximum power point tracking techniques.
Arulmurugan R.
,
Suthanthiravanitha N.
“Improved Fractional Order VSS IncCond MPPT algorithm for Photovoltaic Scheme”
International Journal of Photoenergy
Article ID 128327
2014
10 
DOI : 10.1155/2014/128327
safari Azadeh
,
mekhilef saad
2011
“simulation and hardware implementation of incremental conductance MPPT with direct control method using cuk converter”
IEEE transactions on industrial electronics
58
(4)
1154 
1161
DOI : 10.1109/TIE.2010.2048834
Arulmurugan R.
,
Suthanthiravanitha N.
2014
“Performance Calculation of Electrical Energy Output and Power Conversion Efficiency of Standalone PV System”
Australian Journal of Basic and Applied Sciences
8
(7)
149 
159
Kassem Ahmed M.
2012
“MPPT control design and performance improvements of a PV generator powered DC motorpump system based on artificial neural networks”
Electrical Power and Energy Systems
43
90 
98
DOI : 10.1016/j.ijepes.2012.04.047
Ishaque Kashif
,
Salam Zainal
,
Shamsudin Amir
,
Amjad Muhammad
2012
“A direct control based maximum power point tracking method for photovoltaic system under partial shading conditions using particleswarm optimization algorithm”
Applied Energy
99
414 
422
DOI : 10.1016/j.apenergy.2012.05.026
Karlis A.D.
,
Kottas T.L.
,
Boutalis Y.S.
2007
“A novel maximum power point tracking method for PV systems using fuzzy cognitive networks (FCN)”
Electric Power Systems Research
77
315 
327
DOI : 10.1016/j.epsr.2006.03.008
Hamed Mashinchi Mahery
,
Ebrahim Babaei
2013
“Mathematical modeling of buckboost dcdc converter and investigation of converter elements on transient and steady state responses”
Electrical Power and Energy Systems
44
949 
963
DOI : 10.1016/j.ijepes.2012.08.035
Lin ChiaHung
,
Huang b CongHui
,
Duc YiChun
,
Chen JianLiung
2011
“Maximum photovoltaic power tracking for the PV array using the fractionalorder incremental conductance method”
Applied Energy
88
4840 
4847
DOI : 10.1016/j.apenergy.2011.06.024
Wua YuChi
,
Chen MengJen
,
Huang SihHao
,
Tsai MingTsung
,
Li ChiaHuang
2013
“Maximum power point tracking on standalone solar power system: Threepointweighting method incorporating midpoint tracking”
Electrical Power and Energy Systems
52
14 
24
DOI : 10.1016/j.ijepes.2013.03.008
Qin Lijun
,
Lu Xiao
2012
“Matlab / SimulinkBased Research on Maximum Power Point Tracking of Photovoltaic Generation”
2012 International Conference on Applied Physics and Industrial Engineering,Physics procedia
24
10 
18
Mellit A.
,
Rezzouk H.
,
Medjahed Messai
2011
“FPGAbased real time implementation of MPPTcontroller for photovoltaic systems”
Renewable Energy
36
1652 
1661
DOI : 10.1016/j.renene.2010.11.019
Eakburanawat Jensak
,
Boonyaroonate Itsda
2006
“Development of a thermoelectric batterycharger with microcontrollerbased maximum power point trackingtechnique”
Applied Energy
83
687 
704
DOI : 10.1016/j.apenergy.2005.06.004
Abd Kadir Mahammad
,
Sharifah Saon
,
Wong Swee Chee
2013
“Development of Optimum Controller based on MPPT for Photovoltaic System during Shading Condition”
Malaysian Technical UniversitiesConference on Engineering & Technology 2012, MUCET 2012 Part 3Civil and Chemical Engineering, Procedia Engineering
53
337 
346
Podlubny Igor.
1999
“Fractional differential equations. Mathematics in science and engineering”
Academic Press
New York
Chengbin Ma
,
Yoichi Hori
2007
“Fractionalorder control: theory and applications in motion control”
IEEE Ind Electron Mag
(Winter)
6 
16
Arulmurugan R.
,
Suthanthiravanitha Dr. N.
2015
“Model and design of fuzzybased Hopfield NN tracking controller for standalone PV applications”
Electric Power Systems Research
120
184 
193
DOI : 10.1016/j.epsr.2014.05.007
Ahmad Al Nabulsi
,
Rached Dhaouadi
2012
“Efficiency Optimization of a DSPBased Standalone PV System Using Fuzzy Logic and DualMPPT Control”
IEEE Transactions on Industrial informatics
8
(3)
573 
584
DOI : 10.1109/TII.2012.2192282
Subiyanto Subiyanto
,
Mohamed Azah
,
Hannan M.A.
2012
“Intelligent maximum power point tracking for PV system using Hopfield neural network optimized fuzzy logic controller”
Energy and Buildings
51
29 
38
DOI : 10.1016/j.enbuild.2012.04.012
Nopporn Patcharaprakiti
,
Suttichai Premrudeepreechacharn
,
Yosanai Sriuthaisiriwong
2005
“Maximum power point tracking using adaptive fuzzy logic control for gridconnected photovoltaic system”
Renewable Energy
30
1771 
1788
DOI : 10.1016/j.renene.2004.11.018
Tafticht T.
,
Agbossou K.
,
Doumbia M.L.
,
Cheriti A.
2008
“An improved maximum power point tracking method for photovoltaic systems”
Renewable Energy
33
1508 
1516
DOI : 10.1016/j.renene.2007.08.015
Ghassami Ali Akbar
,
Sadeghzadeh Seyed Mohammad
,
Soleimani Asma
2013
“A high performance maximum power point tracker for PV systems”
Electrical Power and Energy Systems
53
237 
243
DOI : 10.1016/j.ijepes.2013.04.017