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922 IEEE TRANSACTIONS ON ENERGY CONVERSION, VOL. 27, NO. 4, DECEMBER 2012

Evaluation of the Hill Climbing and theIncremental Conductance Maximum Power Point

Trackers for Photovoltaic Power SystemsSren Bkhj Kjr, Member, IEEE

AbstractMaximum power point tracking (MPPT) is an impor-tant function in all photovoltaic (PV) power systems. The classicalhill climbing and incremental conductance MPPT algorithmsare widely applied in many papers and applications. Both algo-rithms perturb the operating conditions of the PV array and detectthe changes in generated power. Since the detected change in gen-erated power also could be a result of changes in irradiance, bothalgorithms are prone to failure in case of large changes in irradi-ance. This paper starts to discuss the size of the perturbation ofthe operating conditions for both algorithms, based on the single-

diode model. The result is used to select the updating frequencyfor the two algorithms, in order not to run away under certain dy-namic conditions. Both algorithms are implemented in an inverterand tested over 16 days of simultaneous operation. Basic statisticalprocedures, the paired t-test, have been applied to the data withthe conclusion that the two algorithms perform equally good.

Index TermsBenchmark testing, efficiency, maximum powerpoint tracker (MPPT), photovoltaic (PV) power systems, solarpower generation.

I. INTRODUCTION

M

AXIMUM power point tracker (MPPT) is a device or

function which continuously seeks the optimal operat-

ing point of the photovoltaic (PV) array, or other generator, bychanging the operating point of the PV array.

The majority of the published papers on the subject PV power

systems (PVPS) apply either the hill climbing1 or the incre-

mental conductance MPPT algorithms for their experimental

work [1], [2], since both algorithms are easily implemented.

Exception is when the paper in particular is on the development

of new MPPT algorithms [3][24]. A comprehensive summary

of different MPPT algorithms is given by Esram and Chapman

in [25].

Most of the papers claim that the inherent perturbation of the

PV operating point around the maximum power point (MPP)

for the hill climbing algorithm reduces the energy outcometoo much [3], [4]. According to [4][7], this perturbation must,

Manuscript received February 21, 2012; revised May 3, 2012 and July 23,2012; accepted July 30, 2012. Date of publication October 11, 2012; date ofcurrent version November 16, 2012. Paper no. TEC-00038-2012.

The author is with Danfoss Solar Inverters, DK-6400 Snderborg, Denmark(e-mail: [email protected]).

Color versions of one or more of the figures in this paper are available onlineat http://ieeexplore.ieee.org.

Digital Object Identifier 10.1109/TEC.2012.22188161The expression perturb and observe should be avoided for the hill

climbing algorithm, since both the hill climbing and the incremental-conductance algorithms perturb the PV voltage to locate the MPP.

therefore, be reduced. What they do not note is that the in-

cremental conductance algorithm also perturbs the PV voltage

in order to locate the MPP, hence also lowering the outcome.

The classicalincremental conductance algorithm includes two

cases in the flowchart diagram in which the perturbation is

paused. Such pauses can also be implemented in the flowchart

of the hill climbing algorithm. On the other hand, the cases

where the algorithms are paused are most unlikely to happen,

since they require that the exact MPP has been tracked, that theirradiance and temperature are constant over time and that no

noise is present in the system.

The work of Femia et al. [8] is one of the first attempts

to optimize the classical hill climbing algorithm, in respect

to timing requirements and perturbation step size. The goal

is to avoid the runaway situations, which can occur with large

changes in theirradiance. Theresulting MPPT hasa perturbation

magnitude of approximately 3% of the MPP voltage, running at

a rate of 20 Hz, and decreases the output power by 0.7% due to

the perturbation around the MPP.

Schmidt et al. [26] claim that the speed of the MPPT should be

in the range of 0.11% of the nominal MPP voltage per second

in order to achieve an annual MPPT efficiency of 99.9%, basedon measurements of the best fixed voltage. However, this source

does not take the tendency to run away into consideration.

The work of Kjr [27], [28] estimates the amount of energy

lost due to a sinusoidal perturbation on the PV array voltage and

comes to the conclusion that the step size should be around 2%

of the MPP voltage in order to keep the loss below 0.1%.

The aim of this paper is twofold: first, the correlation between

the perturbation step size of the PV voltage and the correspond-

ing energy loss is established. Second, the two classical MPPT

algorithms are designed to meet real atmospheric conditions.

The approach in [8] for the hill climbing is based on a second-

order Taylor approximation of the PV cell, whereas the methodgiven in this paper uses the correlation between perturbation

step size and energy loss. The authors in [33] claim that the in-

cremental conductance method will always track the correct

MPP. This is shown, in this paper, not always to be the case

when the irradiance changes sufficiently fast.

Thesingle-diode model for the PV cells is revised in Section II

together with the derivation for the utilization ratio as function

of perturbation step size. The theories behind the two MPPT

algorithms are presented in Section III and further used to design

the parameters for the two algorithms. The algorithms are tested

in Section IV by means of measurements. Finally, a conclusion

is given in Section V.

0885-8969/$31.00 2012 IEEE

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KJR: EVALUATION OF THE HILL CLIMBING AND THE INCREMENTAL CONDUCTANCE MAXIMUM POWER POINT TRACKERS 923

Fig. 1. Electrical model and characteristics of a PV cell. (a) Electrical modelwith current and voltages defined. (b) Electrical characteristic of the PV cell,exposed to a given amount of lightat a given temperature. As indicated, perturba-tion at thePV modulesterminals results in a somewhatlower powergeneration,compared with the case where no perturbation is present at the terminals.

II. METHODS

A. Modeling the PV Cell

A PV module is made up around several PV cells connected

in series. The cell inherently includes a light-controlled current

source in parallel with the diode (see Fig. 1).Using the notation of Fig. 1, and applying the Shockley ideal

diode equation and neglecting parallel and series resistances in

the cell, the following model of the PV cell can be established

[27]:

iPV =iSC iRS

exp q uPV

k A T 1

(1)

iPV iSC iRS exp q uPV

k A T

(2)

iPV iSC iRS exp

q (UM PP + u)k A T

=iSC id, 0 exp

q uk A T

(3)

where iSC is the short-circuit current (light-induced current), iRSis the reverse saturation current of the diode, qis the electroncharge(q= 160 1021 C),uPV is the voltage applied acrossthe diode,k is Boltzmanns constant (k= 13.8 1024 J/K),Ais the diode quality factor, andTis the absolute temperature.

The resistive parts in the cell can be left out in this analysis,

since their influence on the power-characteristic (power loss) is

almost unaffected by the small voltage (current) perturbation.

For example, for a 2% perturbation in the current, the losses in

the series resistance increase from 1.0 to 1.0002 p.u.A method for determination ofiRS andAis given as [27]

ln (iRS ) qk A T

= 1 UM PP1 UO C

1

ln (iSC IM PP )ln (iSC )

(4)

whereUo c is the open-circuit voltage and IM PP is the current atthe MPP.

The model in (1) can be reduced to (2) by noticing that

exp(qu/(kAT)) 1, which again can be rewritten as (3), whereid, 0is the current through the p-n junction at the particular large-signal voltage, in this case the MPP voltageUM PP , andu is

the small-signal perturbation around the MPP. The short-circuit

Fig. 2. PV characteristic for PV module Type A (see Appendix A for details)at STC.

currentiSC is given as [27]

iSC = (ISC ,ST C + kSC (T TST C )) GGST C

(5)

whereISC ,ST C is the short-circuit current at standard test con-ditions (STC),kSC is the temperature coefficient of the current,TST C is the reference temperature (TST C = 298.15 K), G isthe irradiance, and GST C is the reference irradiance (GST C =1000 W/m2 ). Finally, the reverse saturation current is givenas [27]

iRS =IRS ,ST C

TTST C

3 exp

Ega pk A

1

TST C 1

T

(6)

where IRS ,ST C is thereversesaturation current at STC, and Ega pis the bandgap energy (Ega p = 1.11 eV for silicon at 298.15 K).The corresponding power generation is computed as [note the

sign, since the 1 in (1) is omitted]pPV =uPV iPV (UM PP + u)

iSC id, 0 exp

q uk A T

. (7)

Equations (1)(7) are used to plot the PV characteristics in

Fig. 2 for a modulewith thedata given in Appendix A (consistingof 36 cells in series).

In order to validate the single-diode model presented in (1)

(7), the measured characteristic of a PV module, of the type in

Appendix A, is also plotted in Fig. 2. The measurements are

obtained using a PVPM1000C peak power measuring device

andIVcurve tracer for PV Modules [29]. The MPP voltageis displaced 0.15 V compared with data sheet value and the MPP

power is 2 W higher than data sheet value. Thus, the model is

fairly accurate at STC. Validity is not tested at other conditions

for temperature and irradiance. This is not regarded problematic,

since it is assumed that the curve formof thepower characteristic

is left unchanged, and this is what is important here.

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Fig. 3. PV cell characteristic with the four operating points for one MPPT

cycle.

B. Relations Between Utilization Ratio and Perturbation

Assuming that the PV cell is perturbed around its MPP with

u as shown in Fig. 3, the utilization ratio can be expressed as

= 2 PM PP + P(UM PP + u) +P(UM PP u)

4 PM PP . (8)

Applying the results in (7), the power generated by the PV

cell atUM PP + uandUM PP ucan be expanded to

P(UM PP +u) = UM PP iSC UM PP id, 0 exp q

u

k A T+ u iSC u id, 0 exp

q uk A T

(9)

P(UM PP u) = UM PP iSC UM PP id, 0 expq u

k A T

u iSC + u id, 0 expq u

k A T

.

(10)

These twoexpressionsfor power are summed andby applying

the law of hyperbolic functions [30]

P(UM PP + u) +P(UM PP u) = 2 PM PP

+ 2 UM PP id, 0

1 cosh

q uk A T

2 u id, 0 sinh

q uk A T

. (11)

Substituting (11) into the expression for the utilization ratio

in (8), it becomes

=

2PM PP +UM PP id, 0 1cosh q uk A Tuid, 0 sinh quk A T2PM PP .

(12)

The utilization ratio for the Type A PV module in Appendix A

is depicted in Fig. 4 and Table I. It shows that the European

utilization ratio is around 97% for a 10 % step size and 99.9%

for a voltage perturbation of 2.0% of the MPP voltage, also at

STC. The European efficiency is a weighted value, defined in

(13) [31]

EU = 0.03 5% + 0.06 10 % + 0.13 20 % + 0.10 30 %

+ 0.48 50 % + 0.20 100% . (13)

Fig. 4. Utilization ratio for Type A PV module atT = 298 K. Plotted forthe six entries defining the European efficiency. Measured values from the testsetup also used in Fig. 2.

TABLE IRELATIONBETWEENPERTURBATION ANDEUROPEANUTILIZATION RATIO

The step size can now be selected on basis of the required

STC or European efficiency. Based on the single-diode model,the step size can be up to 24% of the MPP voltage without

compromising the utilization ratio significantly, cf., Table I.

C. Requirements for a MPPT

According to [32], which is dealing with test procedures for

MPPT, very fast changes (measured over 1 s) of irradiances are

in the order of 30 W/(m2 s), computed with 99% confidenceinterval. This source has also investigated the probability of

unrealistic large changes, e.g., from 15% to 120% irradiance

over 0.5 s, and has come to the conclusion that these changes

have a probability in the order of 107 . Thus, a MPPT should bedesigned for maximum changes in irradiance of approximately30 W/(m2 s) [32].

The MPP is also a function of temperature. However, changes

in the MPP as function of changes in temperature are much

slower than the changes from the irradiance. Temperature-

induced changes in the MPP are, therefore, considered non-

critical in this paper as the MPPT can easily track them.

The converter used to load the PV array should be of the

voltage-controlled type (may include an inner current loop).

The background is that the MPP voltage range is narrow over

the entire irradiance range and only changes slowly (see also

Figs. 6, 7, 9, and 10), whereas the MPP current is almost linearly

dependent on the instantaneous irradiance and would require a

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Fig. 5. Flowchart for the hill climbing MPPT algorithm.

faster controller to obtain good tracking. For example, the MPP

voltage range for the PV system applied later on in this paper is

between 500 and 700 V, measured over the entire year (mostly

depended on temperature), whereas the MPP current is in therange from zero to approximately 10 A. This is also validated

in [37] where they use the PV voltage as the reference to a

boost converter and in [38] where they come to the conclusion

that a peak-current-mode-controlled buck-converter can become

unstable in PV applications.

III. ANALYSIS OFMPPT ALGORITHMS

A. Hill Climbing

The hill climbing MPPT algorithm perturbs the voltage in

one direction and evaluates the corresponding difference (sign)

of the power [8]. If the power is increasing, the algorithm willmove the voltage in the same direction; otherwise, it will reverse

the direction. The flowchart is depicted in Fig. 5.

The hill climbing algorithm can be confused, and track the

MPP in the wrong direction [33], when the change in PV power

caused by change in irradiance is larger than the change in PV

power as a function of the perturbation. An example is shown

in Fig. 6, where a fast ramp of 30 W/(m2 s) is applied.The hill climbing will not be confused, if and only if [8]

|P u| > |P g| (14)where Pu is the change in power as function of change in

voltage, andPgis the change in power as function of changein irradiance. The hill climbing MPPT is optimized in [8], inrespect with the sampling interval Ta and minimum step size,Vd , in order to follow a certain irradiance ramp, dg/dt. Theapproach is based on a second-order Taylor approximation of

the PV model in (1)(7), including resistive parts.

Assuming that initial operating point is equal to the MPP, and

that the PV voltage is decreased by the MPPT, the difference

in power as function of the perturbation is simply computed as

(per PV cell)

P u = PM PP (UM PP u)

isc id, 0 expq u

k A T

(15)

Fig. 6. Upper plot shows the MPP and obtained power by the hill climbingalgorithm. The lower plot shows the corresponding voltages. Perturbation =2.0% of operating point. MPPT update frequency=1 Hz. Minimum irradiance

is equal to 200 W/m

2

, maximum is equal to 800 W/m

2

, slope of ramp is equalto 30 W/(m2 s). The simulated PV arrays are made up of around 40 modules ofType A (see Appendix A).

which from Fig. 3 and (8) also can be written as

P u= 2 (1 ) PM PP (16)where the utilization ratio is defined in (12). The difference in

power as function of change in irradiance is computed as

P g = UM PP K G (17)whereKis the ratio between the short-circuit current and theSTC irradiance (ISC ,ST C/GST C ), and G is given as dg/dtTa (sampling interval). Solving (14), (15), and (17) for u isimpossible, but solving forTa is possible from (14), (16), and(17):

2 (1 ) IM PPK

dg

dt

1> Ta . (18)

Assuming a 2% perturbation, IM PP = 7.22 A, cf., moduleType A in Appendix A, K = 0.008 Am2/W and a ramp of30 W/(m2 s), the sampling interval shall be lower then 60 ms,which corresponds to an update frequency of 16.6 Hz. The result

is shown in Fig. 7.

B. Incremental Conductance

The same analysis is now conducted for the incremental

conductance MPPT algorithm. The incremental conductance

MPPT algorithm perturbs the voltage in one direction and evalu-

ates the sign of the derivative of the power dp/du [33]. If the sign

is negative, the algorithm will decrease the voltage; otherwise,

it will increase the voltage. A flowchart is depicted in Fig. 8.

Thus, the algorithm attempts to maximize the power by driving

the derivative to zero

s= di

du+

i

u = 0. (19)

The incremental conductance algorithm can also be con-

fused to track the MPP in the wrong direction. Assume that the

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Fig. 7. Upper plot shows the MPP and obtained power by the hill climbingalgorithm. The lower plot shows the corresponding voltages. Perturbation =2.0% of operating point. MPPT update frequency =16.6 Hz. Tracker efficiency

= 99.8%.

Fig. 8. Flowchart for the ideal incremental conductance MPPT algorithm.Since the algorithm is assumed always to perturb the voltage (U[n]= 0),case 2 can be removed in the original flowchart.

MPPT is already tracking the MPP that the operating voltage

has just been decreased by the MPPT and at the same instant the

irradiance starts to increase. The PV current hereby increases

both due to lowered operating voltage and due to increased irra-diance. The term di/duin (19) is, therefore, large and negative.

Moreover, assume that the starting point for the irradiance is

low; thus, the operating current is low, and that the operating

voltage is large. The term i/u in (19) is, therefore, small andpositive.

Since the algorithm changes the voltage reference in the same

direction as the sign ofs (see Fig. 8), the algorithm can nowbe confused to believe that di/du + i/u is negative, thus de-creasing the voltage reference (see Fig. 9). The incremental

conductance will, therefore, not be confused, if and only if

didu < iu . (20)

Fig. 9. Upper plot shows the MPP and obtained power by the incrementalconductance algorithm. The lower plot shows the corresponding voltages.Perturbation= 2.0% of operating point. MPPT update frequency = 1 Hz.

The term di/du in (19) and (20) can be expanded to di/dtdt/du, and by substituting (5) into (3) and taking the derivative

with respect to time, the small signal di/du can be found (per

PV cell)

di

dt dt

du =K dg

du q id, 0

k A T exp

q uk A T

. (21)

Substituting this into (19), a new expression for s appears

s= K dgdu q id, 0

k

A

T exp

q uk

A

T +

i

u (22)

which shall be positive for dg/dt positive and du/dt negative

when starting at the MPP, in order not to get confused. Thus,

solving fordg/duyields

dg

du =

1

K

s+ q id, 0k A T exp

q uk A T

i

u

. (23)

Setting s equal to zero since the algorithm is assumed alreadyto track the MPP, u to zero (for obtaining the mean value),u equal to the MPP voltage per cell and i to the MPP currentyields

dg

du =

1

K q

id, 0

k A T IM PP

UM PP

. (24)

Assumingid, 0 = 8.00 7.22 = 0.78 A, IM PP = 7.22 A,UM PP = 17.3 V/36 cells = 0.481 V, cf. module Type A inAppendix A, K = 0.008 Am2/W, dg/du becomes equal to6.47 W/(Vm2 ). Finally, the updating frequency can then begiven as

u dgdu

dg

dt

1> Ta . (25)

Applying u = 2% ofUM PP and a ramp of 30 W/(m2 s), the

sampling interval shall be lower than 75 ms, which corresponds

to an update frequencyof 13.4 Hz. The result is shown in Fig. 10.

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Fig. 10. Upper plot shows the MPP and obtained power by the incrementalconductance algorithm. The lower plot shows the corresponding voltages.Perturbation= 2.0% of operating point. MPPT update frequency = 13.4 Hz.

Tracker efficiency= 99.7%.

C. Upper Boundary

The works of Femia et al.[8] and Sokolov et al. [37] shows

that there exists an upper boundary for the updating period of

the MPPT, which should not be violated in order to ensure good

MPPT performance. The boundary is determined by the natural

frequency of the PV-array/converters system, and is found to be

10 and 25 ms, respectively, in [8] and [37].

IV. RESULTS

In this section, the algorithms are evaluated using two dif-

ferent approaches: by testing them on a PV array simulator

(PVAS) [34] and by using two identical PV systems (PV arrays

+ inverter) under real sky. The algorithms are implementedinto a grid-connected inverter. The two inputs of the inverter

are equipped with their own dedicated booster converters for

performing individual MPPT.

A. Statistical Framework for Evaluation of the Results

When testing the algorithms with an inverter and either with a

PVAS or with real PV arrays, the data should be processed with

care in order not to draw a false conclusion. To avoid effectsfrom unknown and uncontrollable factors, the order of the test

sequence is randomized [35]. Besides, in order to eliminate ef-

fects from the weather, the experiments will be paired, meaning

that both algorithms will be tested simultaneously. With two PV

arrays or PVAS, two inverter inputs and two algorithms, four

different test combinations exist

1) ALG1 + PV-STRING1 + INV-INPUT1 and ALG2 +PV-STRING2+ INV-INPUT2,


3) ALG1 + PV-STRING2 + INV-INPUT1 and ALG2 +

PV-STRING1+ INV-INPUT2,

Fig. 11. Hill climbing testedwith ISORRIP sequence number 25,efficiencyrecorded to 99.7% for this particular run.


where ALG1 is the hill climbing algorithm, ALG2 is the

incremental conductance algorithm, PV-STRING1 and PV-STRING2 are the two different outputs of the PVAS/the two

different PV arrays, and INV-INPUT1 and INV-INPUT2 are

the corresponding inputs on the inverter.

The duration of one test is set to one day when testing on the

real PV arrays. In order to run all four different tests an equal

number of times, the number of tests is set to a multiple of 4, in

the following order (selected at random):

BCAD CACB ADDB CDAB.

The algorithms are evaluated by the paired t-test [35]. The

applied random variable is

E=EA LG 1EA LG 2 (26)where EAL G is the measured energy for the algorithm undertest. Using the paired t-test, a 95% confidence interval on the

difference in efficiencies is

EA LG 1EA LG 2 = E t0.02 5,(n1) Sd /

n (27)

where E is the sample mean of the differences in energy,t0.02 5,(n1) is the percentage points for the t-distribution with(n1) degrees of freedom [35] with 5% confidence interval,Sd is the sample standard deviation of the differences, and n is

the number of tests.

B. Test by PVAS

The input of the inverter is connected with a PVAS [34]. The

PV-array is modeled by (1)(6), and the model is implemented

in the PVAS. The arrays are modeled as 36 modules in series,

with a cell temperature of 25 C, in order to stay within thevoltage range of the PVAS, which is around 800 V. The effi-

ciency of the algorithms is directly obtained from the PVAS.

The algorithms are evaluated in terms of the identification of

a set of real and representative irradiance profiles (ISORRIP)

efficiency. The ISORRIP test is composed of 27 different test

profiles, based on measurements of the irradiance in Vienna,

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TABLE IIMEASUREDISORRIP EFFICIENCIES FOR THETWOALGORITHMS

Fig. 12. Two identical PV arrays, each of 5 kW, are divided into a left (PV2)and a right (PV1) part. A house to the east causes partial shadows on PV2 inthe mornings of JanuaryMarch. The chimney to the left causes partial shadowson PV1 in the late afternoon of AprilSeptember. Therefore, data in the periods06:2506:45 and 17:0019:10 are not used in the evaluation.

Fig. 13. Daily development of the 95% confidence interval shows that theintervalconvergesto small values around zero,indicatingthat the twoalgorithmsperform equally well.

Austria, over a one-year period [36]. The results from one run

are depicted in Fig. 11.

Applying (26) and (27) to the recorded data, together with

n = 4, t0.02 5,3 = 3.182, Sd = 0.014%, and =0.010%,the 95% confidence interval is from0.033% to 0.012% (seeTable II for details). Since zero is included in the interval, the

data support the hypothesis that the two algorithms are identical

(with a 5% level of significance).

C. Test by Two Identical PV Arrays

The algorithms are evaluated in terms of produced energy.

The input of the inverter is connected with two identical PV

arrays (see Fig. 12), each comprising 27 PV modules of Type B,

as listed in Appendix A. Data that include partial shadows of

some of the modules are omitted in order to improve the quality

of the result.

The energy produced by the PV arrays is measured with

the measuring circuits included in the inverter. Applying (26)

and (27) to the recorded data, together with n = 16, t0.02 5,15= 2.131, Sd = 0.146 kWh, and E = 0.011 kWh, the 95%confidence intervalis from0.067 to 0.089 kWh (seeFig.13 fordetails). Since zero is included in the interval, the data support

the hypothesis that the two algorithms are identical (with a 5%

level of significance).

V. CONCLUSION

The MPPT is an important part of every PVPS, employed in

order to increase the yield.

A model for the losswas established on the basis of the single-

diode model of the PV cell. The model predicts 0.1% loss for a

perturbation step size of 2% of the MPP voltage, at steady-state

irradiance and temperature.

The two classical MPPT algorithms were both investigated

in terms of required update rate, in order not to run away. Both

algorithms were designed for the same real atmospheric condi-tions, with a high level of fluctuation in the irradiance, approx-

imately 30 W/(m2 s). The outcome of the investigation is thatthe update rate for the hill climbing algorithm must be slightly

greater than that of the incremental conductance algorithm.

The two algorithms were implemented into a grid-connected

inverter and tested without significant difference in yield. The

two algorithms are equal with a 5% level of significance. Thus,

neither of the algorithms can be preferred over the other.

The similarity of the two algorithms will be investigated

deeply in a future work, since the recent results suggest that

the two algorithms are similar.

APPENDIXA

ACKNOWLEDGMENT

The authors thank M. Rick for modifying the firmware of the

inverter to accommodate the two algorithms, A. H. Jensen, K.

Hartmann, and Dr. U. Borup for proof reading the manuscript.

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Sren Bkhj Kjr(S98AM00M04) receivedthe M.Sc. and Ph.D. degrees in electrical engineer-ing from the Institute of Energy Technology, Aalborg

University (AAU), Aalborg, Denmark, in 2000 and2005, respectively.He is currently an R&D Consultant at Danfoss

Solar Inverters, Snderborg, Denmark. He was a Re-search and Laboratory Assistant at the Section ofPower Electronics and Drives, AAU, from 2000 to2004. He also taught courses on photovoltaic systemsfor terrestrial and space applications (power system

for the AAU student satellite: AAU CubeSat). His main research interests in-clude switching inverters for photovoltaic applications, including power quality,grid voltage control, fault ride through, maximum power point tracker, Smart-Grid, and design optimization. He has also been involved in standardizationwork within safety of inverters, ancillary services for inverters, and methods forevaluating inverters total efficiency.

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