Hybrid Approach to Maximum Peak Power Tracking

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International Journal of Electrical and Electronics Engineering Research and Development (IJEEERD),ISSN 2248 9282(Print), ISSN 2248 9290(Online), Volume 1, Number 1, April-June (2011)

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HYBRID APPROACH TO MAXIMUM PEAK POWER TRACKING

ALGORITHM

M D Goudar1,2, B P Patil2, V Kumar11Electronics Department, Indian School of Mines Dhanbad, India, 826004,

2Maharashtra Academy of Engineering Alandi (D), Pune, India, 412105,[email protected], [email protected], [email protected]

ABSTRACT

In this paper, the various maximum power point tracking (MPPT) methods based on number ofsensors required, speed of convergence, ability to perform under noisy and environmentallyvarying conditions have been discussed. The stability of maximum peak point without oscillating

around the peak has been described. A new hybrid MPPT algorithm has been suggested, whichcombine the advantages of fractional open circuit voltage, variable step and optimized hillclimbing MPPT algorithm (dP-P&O). The analysis of MPPT based two stage AC photovoltaicsystem topology has been analysed. The simulation results of MATLAB and PSIM have beencompared with earlier methods. Improvements in the starting characteristic performance andtracking effect have been obtained.

Keywords: Insolation, Maximum Power Point (MPP), Maximum Power Point Tracking (MPPT),Perturb and Observe (P&O), Photovoltaic (PV).

1. INTRODUCTIONMaximum Power Point Tracking (MPPT) is very important in solar power systems because it

reduces the solar array cost by decreasing the number of solar panels needed to obtain the desiredoutput power. Photovoltaic power system performance depends on local irradiance andtemperature conditions. The sunlight intensity of the direct normal irradiance to that on anysurface can be determined using the cosine of the angle between the normal to the sun and themodule plane [1]. The PV applications range from a few watts to few megawatts in PV plants.The electrical output of a single cell is usually insufficient for most of the applications. In order toprovide useful power to any application, the individual solar cells are connected together. Thereare two basic connection methods, series and parallel connections to increase the voltage andcurrent respectively [2]. The over all cost of PV array and space are two main factors contributingto the cost of total PV system hence optimizing the size and space are important in reducing thecost of overall system. Figure. 1 shows a typical space required against the system capacity forvarying PV cell efficiency. The size of the PV module decreases with increasing efficiency of the

PV cells.

International Journal of Electrical and Electronics EngineeringResearch and Development (IJEEERD), ISSN 2248 9282(Print), ISSN 2248 9290(Online), Volume 1, Number 1,April-June (2011), pp. 01-11 PRJ Publication http://www.prjpublication.com/IJEEERD.asp

IJEEERD

PRJ PUBLICATION


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Fig. 1. Size of PV Module as a function of Power for various efficiencies of solar cells

When a PV array is directly connected to a load, the systems operating point will be at theintersection of the IV curve of the PV array and load line. In general, this operating point is notat the PV arrays MPP. Thus, in a direct-coupled system, the PV array must usually be oversized

to ensure that the loads power requirements can be supplied. This leads to an over expensivesystem. To overcome this problem, a switch-mode power converter, called a maximum powerpoint tracker, can be used to maintain the PV arrays operating point at the MPP [3,4]. The MPPTalgorithm can be used to locate and track the MPP of the PV array. However, the location of theMPP in the IV plane is not known. It must be tracked, either through model calculations or by asearch algorithm. The situation is further complicated as the MPP depends in a nonlinear way onirradiance and temperature, which is taken care off by developing algorithm in the present paper.Many such algorithms have been reviewed by Esram et. al. [5]. However, one particularalgorithm, the perturb and observe (P&O) method, is widely used in commercial PV MPPTs. It isfound that the P&O method can have MPPT efficiencies well in excess of 96% if it is properlyoptimized.

A typical AC photovoltaic system is shown in Fig. 2. The PV generation system is

basically classified into single-stage system and two stage system. Two stage system include adc-dc circuit and the MPP tracker is achieved by the dc-dc circuit. The inverter control isachieved by the dc-ac circuit. When MOSFETs M1 and M4 is shorted, the load of the current ispassed from M1 and M4. Similarly, when MOSFETs M2 and M3 are shorted, the load of thecurrent is passed from M2 to M3.The output DC voltage of boost converter is transferred ACoutput by the power switches M1 to M4 [6].


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The photon generated current, which is equal to Isc and is directly proportional to the irradiance

and the intensity of illumination on the PV cell. If the value ofIsc is known from the datasheet

under the standard test conditions and irradiance Go is 1000W/m2 at the air mass (AM) = 1.5, then

the photon generated current at any other irradiance G (W/m2) can be calculated by the equation

oGsc

o

GscI

G

GI ||

= (3)

It is possible to combine the diodes D1 and D2 and we get the following equation

+

=

+

+

p

snkT

RIVq

nkT

RIVq

scR

RIVeIeIII

ss

.11

.

02

.

01 (4)

where n is known as the ideality factor and takes the value between 1 and 2. The reverse

saturation current (Io) is temperature dependant, which can be calculated by the following

equation at a given temperature T.

=

ref

g

ref

TTnk

qEn

ref

ToTo eT

TII

113

|| (5)

The Eq. (4), at Rp can be written as

+=

nkT

RIVq

o

ss

eI

qnkT

dV

dIR

..

.

/(6)

The first value of output current (IN) has been calculated by Eq. (1) and the next value i.e., IN+1

can be calculated by Newtons method. The equation for output current can be written as

+

+

+

=

nkT

RIVqs

o

nkT

RIVq

oNsc

NNs

s

enkT

RqI

eIII

II.

.

1.

1

1

(7)

The above Eq. (7) has been simulated using MATLAB and described in the following section.

3.

PROPOSED MPPT ALGORITHMTracking the maximum power point (MPP) of a photovoltaic array is usually an essential part

of a PV system. As such, so many methods have been developed and implemented for maximumpower point tracking. The methods vary in complexity, sensors required, convergence speed,cost, range of effectiveness, implementation hardware, popularity and in other aspects. In fact, itis difficult to determine which method is most appropriate [7-12]. The first one based onfractional open circuit voltage (FCV) algorithm sets the PV array current to zero to allow themeasurement of array's open circuit voltage. The array's operating voltage is then set to 78%ofthis measured value. This operating point is maintained for a set amount of time and then the


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cycle is repeated. Problems with this algorithm are that the available energy is wasted when theload is disconnected from the PV array and the MPP is not always located at 78% of the arraysopen circuit voltage. FCV is a proximate power tracking method suitable when temperature isconstant. The advantage of FCV method is simplicity, which satisfy the starting characteristic.But the applicability of system is poor, because external environment keeps on changing. In caseof basic P&O MPPT algorithm, the change in the output power is observed for a perturbation

created. If there is an increase in the PV output power, the subsequent perturbation for referencevoltage will be kept the same direction to reach the MPP, while the perturbation shall be reversedin the case of a decrease in the PV output power. The advantage of this algorithm is its simplicitybut the drawback of the system is that it is slow, oscillatory at peak point. The power drawn fromthe PV array with a lager step size contributes to faster dynamics but excessive steady stateoscillations, resulting in a comparatively low efficiency. If iteration step size is small, then thepower drawn from the PV array will have slower dynamics. This problem can be removed byusing the second algorithm i.e. Incremental MPPT with variable step size. In this algorithm if theoperating point is far from MPP, it increases the step size which enables a fast tracking ability. Ifthe operating point is near to the MPP, the step size becomes very small that the oscillation iswell reduced contributing to a higher efficiency. Variable step size Incremental MPPT algorithmeffectively improves the MPPT speed and accuracy simultaneously. The main advantage of this

algorithm over the P&O method is its fast power tracking process. However, it has thedisadvantage of possible output instability due to the use of derivative algorithm. Also thedifferentiation process under low levels of insolation becomes difficult and results areunsatisfactory. The rapidly changing conditions are tracked by an optimized hill climbing MPPTmethod (dP -P&O). The algorithm separates the effects of the irradiation change from the effectof the trackers perturbation by performing an additional measurement in the middle of the MPPTsampling period as shown in Fig. 4. This information is optimized in tracking according to theirradiation change [13]. The change in power between Px and Pkonly reflects the change in powerdue to the environmental changes, as no action has been made by the MPPT. The differencebetween Px and Pk-1 (dPPI) contains the change in power caused by the perturbation of the MPPTplus the irradiation change, if the rate of change in the irradiation is constant over one samplingperiod of the MPPT. The difference between the two consecutive measurements of power is used

to determine the next perturbation direction. This is the extra computational load as compared toclassical P&O method, if the change in power due to irradiation |dPI| is smaller than the change ofpower due to the MPPT perturbation |dP|, it is considered to be a slowly changing condition andthe system will use the basic dP-P&O algorithm with small increment values to reduceoscillations around the MPP.

POWER(P)

Fig. 4. Measurement of the power between two MPPT sampling instances


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The knowledge of the direction of the irradiation change enables the MPPT to use differentoptimized tracking schemes for the different cases of increasing, decreasing, or steady irradiance.When the irradiance is changing rapidly this strategy leads to faster and better tracking, while insteady-state conditions it leads to lower oscillations around the MPP.The proposed hybrid algorithm combines the advantages of all the above three algorithms. Theoverall flow of the proposed algorithm is shown in Fig. 5. The first step is the initialization

program is as shown in Fig. 6, which will set the PV arrays

Fig. 5. Flow chart of the proposed hybrid algorithm

operating voltage point to 78% of this measured value of the open circuit voltage which wasdecided from the I-V curve tracer for various operating temperatures. The P&O algorithm doesnot use the exact power out of the solar panel, but a difference in the proportional power. Theamount the power changed has no effect on the algorithm. For this reason, the exact power out ofthe solar panel is not required, which allows us to use the linear relationship of the PWMs dutycycle and the solar panels current. The proportional power out of the solar panel is calculated bymultiplying the voltage of the solar panel by the duty cycle of the PWM. A variable step has beenused to track the peak point quickly and accurately. Type of environmental changes has beenobserved and two different algorithms have been initiated for slowly varying environmentconditions and fast varying environment conditions.

Fig. 6. The flow chart of initialization program


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The change in the power (P = P(k) P(k-1) is calculated vide measurement of V(k), I(k), V(k-1)and I(k-1). In order to avoid oscillations at the MPP, no actions are initiated if the change inpower is within the thresh hold limit ||. The size of the variable step is decided by the ratio ofP/V, i.e., if the change in power is more then the variable step size will be larger or vice versa.The type of environmental variations is calculated using dP-PO algorithm. If the change in powerdue to irradiation (|dPI|) is smaller than the change of power due to the MPPT perturbation (|dP|),

it is considered to be a slowly changing condition and the system will use the basic variable stepP&O algorithm with small increment values to reduce oscillations around the MPP as explainedin Fig. 7. If the change in power due to irradiation (|dPI|) is larger than the change of power due tothe MPPT perturbation (|dP|), it is considered to be a fast changing condition and the system willuse the variable step P&O algorithm (MPPT2) with smaller or larger increment values to reachthe MPP quickly as shown in Fig. 8. If a fast rise of irradiation was detected by dPI, the MPPTincrease the duty cycle (increase the PV array reference voltage). Decrease the duty cycle(reference voltage is decreased) only when the voltage was increased in the previous MPPTsampling instance and it caused a reduction of power dP. A negative threshold value N LIMIT andpositive thresh hold PLIMIT are applied in order to avoid unnecessary switching around the MPP.

Fig 7. Flow chart of slowly varying environment conditions (MPPT1)

START

NOYES YES

D(k) =

D(k-1) + VS

D(k) =

D(k-1) VS

Is dP >

PLimit

Is dP >

NLimit

Is V > 0 Is V = 0

D(k) =

D(k-1) + VS

D(k) =

D(k-1) VS

Is dP >

PLimit

Is dP >

NLimit

Is V > 0Is V = 0

Is Irradiance

Decreasing

RETURN

D(k) =

D(k-1) VSD(k) =

D(k-1) + VS

YES

YES

YES

YES

NO

NOYES

NO NO NO

NO

NOYESYES

Fig. 8. Flow chart of fast varying environment conditions (MPPT2)


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4. SimulationThe simulations have been carried out for plotting the V-I characteristics and the PowerVoltagecharacteristics for various input profiles of irradiance and temperature and using MATLAB. Theresults for various values of temperature are shown in Fig. 9. It is observed from simulated resultsthat the module current increases with increase in cell temperature and the module voltagedecreases with increase in temperature as expected from the mathematical modeling.

Fig. 9. Output of a MATLAB GUI showing (a) Variation of I-V w.r.t temperature. (b) I-V curveof PV module under standard test conditions. (c) Power curve w.r.t module voltage understandard test conditions.

The irradiance and the temperature data collected for one day were given as the input to theMATLAB program and the MPPT tracking was plotted and validated using MATLAB simulationand the results of tracking the MPPT are as shown in Fig. 10. The proposed hybrid algorithmsuccessfully tracks the MPP under varying environmental conditions. The MPP at 200 W/m2, 400W/m2, 600 W/m2, 800 W/m2, and 1000 W/m2 insolation levels are marked prominently to confirmthe accuracy of tracking.

Fig. 10. MPPT tracking simulated using MATLAB. ($ implies values at standard test conditions)

The implementation of MPPT algorithm is mainly implemented using basic two topologies viz.buck and boost topology. In this paper the implementation has been done using buck topology asits transient response is stable as compared to boost topology [14]. The output was used to charge


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a battery, which in turn is used as a input a inverter. PSIM was used to simulate power electronicscircuit. The schematic of the inverter circuit is shown Fig. 11.

Fig. 11. PSIM circuit used for simulating the inverter circuit

The performance of the system was evaluated and results compared with the MPPTs methodsfrom which this hybrid MPPT has been evolved. The cumulative power for a period of one day is

evaluated and the results are as shown in Table 1.

Table 1. Comparison of performance parameters of various MPPT algorithms

5. RESULTS

The simulation results for V-I characteristics and the powervoltage characteristics werevalidated using a setup as shown in Fig. 12. A Multicrystal Si PV module with VOC of 21.0 V andISC of 2.8 A with a peak output power of 37 Wp at standard test conditions has been used for thetest [14]. The temperature and the irradiance data were stored on a multimedia memory card(MMC) at an interval of one minute. The results obtained were compared with the MATLABsimulation results.

Sl.

NoParameter FCV

P & O

Algorithm

Proposed Hybrid

MPPT Algorithm

Cumulative MPPT output Energy

for one day for same irradiance test

pattern

Maximum Energy

(209.9 Wh_Day)

[ ] [ ] =day

mm

day

nVnInP ][*][max

174.21 Wh_Day187.13

Wh_Day

201.5

Wh_Day (96.41%)

2

Increase in Power output in % as

Compared to Fractional Open-

Circuit Voltage

Not Applicable 8 % 5 %

3 Response Time at start (in ms)

4

Response time to adopt the fast

changing environment at peak

point for same irradiance test

pattern (in ms)


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Fig. 12 Experimental setup for I-V curve tracer

The Irradiance data was collected at city of Alandi (D), Pune, India using pyranometer and the

measurements are shown in Fig. 13.

Fig. 13. A typical sunny day insolation values measured at city of Alandi (D), Pune, India.

The irradiance and temperature profile for a one day data were fed to the MATLAB and theoutput power for three algorithms was evaluated. The observations have been summarized inTable 1. It is observed from the table that the proposed algorithm shows a improvement of 13.05% for cumulative power output over its previous algorithms. The response time of the proposedalgorithm (last column) is equal to the response time of fractional open circuit voltage. Animprovement of 87.5 % over P & O algorithm at start of the process has been obtained. Thesystem has also been tested under the conditions of changing irradiance and found that theproposed hybrid algorithm response time has been improved by 25% and 90.6%, respectively, forfractional open circuit voltage and P & O algorithm. The overall efficiency of the system has

been observed to be 96.41%.

5. CONCLUSIONWe have simulated simple FCV MPPT algorithm and observed that the response time of thesystem is quite fast and reaches quickly to the vicinity of peak point. The changes in theenvironmental conditions are tracked by a more efficient variable step size. Two differentalgorithms have been initiated for slowly varying environment conditions and fast varyingenvironment conditions. Finally we have simulated the proposed hybrid MPPT algorithm


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combining FCV, variable step, and dP-P&O using MATLAB and PSIM. Thus the proposedhybrid algorithm has been observed to achieve the starting characteristic performance, trackingeffect to achieve an efficiency of 96.41%, which demonstrates the soundness of the proposedmethod.6. REFERENCES[1] Tom Markvart and Luis Castaner, Practical Handbook of Photovoltaic fundamentals andapplications, Elsevier, 2007.[2] Tomas Markvart, Solar Electricity, John Wiley, 2001.[3] Suhas P. Sukhatme, Solar energy Principles of Thermal Collection and Storage, TMH, 2nded., 2007[4] Luis Castaner and Santigo Silvestre, Modelling Photovoltaic Systems using PSpice, JohnWiley, 2002.[5] Trishan Esram, Patrick L. Chapman, Comparison of Photovoltaic Array Maximum Power

Point Tracking Techniques, IEEE Transactions on Energy Conversion, 22 (2008), 441-449.[6] Jui-Liang Yang, Ding-Tsair Su, Ying-Shing Shiao, Research on MPPT and Single-Stage

Grid-Connected for Photovoltaic System, WSEAS Transactions on Systems, 7 (2008), 1-4.[7] M D Goudar, B. P. Patil, V. Kumar, A Review of Improved Maximum Peak Power Tracking

Algorithms for Photovoltaic Systems, International Journal of Electrical Engineering andTechnology (IJEET), Vol. 01, No. 01, Sept. Oct. 2010, pp. 72-94.[8] Nicola Femia, Giovanni Petrone, Giovanni Spagnuolo, Massimo Vitelli, A Technique for

Improving P&O MPPT Performances of Double-Stage Grid-Connected Photovoltaic System,IEEE Transactions on Industrial Electronics, 56 (2009), 4473-4482.

[9] N. Femia, G. Petrone, G. Spagnuolo, and M. Vitelli, Optimization of perturb and observemaximum power point tracking method, IEEE Trans. Power Electron., 20 (2005), 963-973.

[10] W. Xiao, and W. G. Dunford, A modified adaptive hill climbing MPPT method forphotovoltaic power systems, Proc. PESC, 2004, 1957-1963.

[11] Xuejun Liu and Luiz A. C. Lopes, An Improved Perturbation and Observation MaximumPower Point Tracking Algorithm for PV Arrays, 35 th Annual IEEE Power ElectronicsSpecialists Conference, Aachen, Germany, 2004.

[12] Fangrui Liu, Shanxu Duan, Fei Liu, Bangyin Liu, and Yong Kang, A Variable Step Size INCMPPT Method for PV Systems, IEEE Transactions on Industrial Electronics, 55 (2008), 2625-2628.

[13] Dezso Sera, Remus Teodorescu, Jochen Hantschel, and Michael Knoll, OptimizedMaximum Power Point Tracker for Fast-Changing Environmental Conditions, IEEETransactions on Industrial Electronics, 55 (2008), 2629-2631.

[14] M D Goudar, B. P. Patil, V. Kumar, Review of Topology for Maximum Power PointTracking Based Photovoltaic Interface, International Journal of Research in EngineeringScience and Technology (INJOREST) ISSN 2229-5135, accepted for publication.

[15] Ryo Ito, Yasuyuki Matsuzaki, Tatsuo Tani and Toshiaki Yachi, Evaluation of Performanceof MPPT Equipment in Photovoltaic System, IEICE/IEEE INTELEC'03, 2003, 256-260.

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Hybrid Approach to Maximum Peak Power Tracking