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IEEE TRANSACTIONS ON ENERGY CONVERSION 1
A Novel Adaptive P&O MPPT AlgorithmConsidering Sudden Changes in the IrradianceSathish Kumar Kollimalla, Student Member, IEEE and Mahesh Kumar Mishra, Senior Member, IEEE
Abstract—In this paper, a short-circuit current-based adaptiveperturb and observe maximum power point tracking algorithm isproposed to extract the maximum power from photovoltaic (PV)panel under sudden changes in the irradiance. This scheme is di-vided into two algorithms: 1) current perturbation algorithm; and2) adaptive control algorithm. The current perturbation algorithmmakes the PV panel operate at maximum power point. The adap-tive control algorithm identifies the operating limit violation andsets a new operating point nearer to maximum power point. Theselimits are derived in terms of changes in the irradiance and cur-rent. The new operating point is set by estimating the short-circuitcurrent. This algorithm proposes variable current perturbation,which varies continuously with the irradiance. A boost converter isused to realize the proposed algorithm. The proposed algorithm iscompared with a conventional algorithm and validated for suddenchanges in the irradiance through the experimental results.
Index Terms—Adaptive control algorithm (ACA), adaptive per-turb and observe (P&O), current perturbation algorithm (CPA),fractional short-circuit current (FSCC), maximum power pointtracking (MPPT) algorithm, photovoltaic (PV) cell.
I. INTRODUCTION
NONCONVENTIONAL energy sources are expected toplay an important role in meeting the world’s power de-
mand, due to their abundant availability and less impact on theenvironment. Solar power generation is currently consideredas one of the most useful renewable energy sources, as it isrelatively less polluted and maintenance free.
The main hindrance of solar energy going widespread is theinitial high capital cost of solar modules. The disadvantage ofsolar energy production is that the power generation is not con-stant throughout the day, as it changes with weather conditions.Furthermore, the efficiency of solar energy conversion to elec-trical energy is very low, which is only in the range of 9–17% [1]in low irradiation regions. This means that a fairly vast amountof surface area is required to produce high power. Therefore,maximum power point tracking (MPPT) is an essential part ofthe photovoltaic (PV) system to ensure that the power convert-ers operate at the maximum power point (MPP) of the solararray. Various MPPT algorithms have been developed in [2]and [3]. These algorithms differ from each other in terms of
Manuscript received June 20, 2013; revised April 14, 2014; accepted April 20,2014. This work was supported by the Department of Science and Technology,India, under Project Grant DST/TM/SERI/2k10/47(G). Paper no. TEC-00351-2013.
The authors are with the Department of Electrical Engineering, Indian Insti-tute of Technology Madras, Chennai 600036, India (e-mail: [email protected]; [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.2014.2320930
number of the sensors used, complexity, and cost to implementthe algorithm. The main objectives of all these MPPT algo-rithms are to achieve faster and accurate tracking performanceand reduce the oscillations around MPP. Each algorithm can becategorized based on the type of the control variable it uses:1) voltage; 2) current; or 3) duty cycle. Among different algo-rithms, much focus has been on perturb and observe (P&O) [4]and hill climbing (HC) [5] methods. The P&O method involvesa perturbation in the operating voltage of the solar array, and theHC method involves a perturbation in the duty ratio of the powerconverter [2].
In the P&O method, the voltage is being increased or de-creased with a fixed step size in the direction of reaching theMPP. The process is repeated periodically until the MPP isreached. At steady state, the operating point oscillates aroundthe MPP. A variable perturbation size algorithm is suggested in[6], to reduce the oscillations and improve the response speed.However, these algorithms are not accurate and fast becausethey do not consider the irradiance and temperature effects,even though they are simple in implementation. Adaptive con-trol algorithms (ACAs) [7], [8], are developed to address theseproblems.
In the incremental conductance (IC) method [9]–[12], theslope of the PV power curve is observed to identify the MPP.If the slope is zero, then the PV panel is operating at MPP. TheIC method also has the same drawbacks as presented in theP&O method, in terms of compromise between the oscillationsand speed. Several IC techniques are proposed to improve theperformance. For instance, in [13], the authors have suggestedto reduce the oscillations at MPP. But during the rapid fluctua-tion of irradiance and temperature, the tracking speed reducessignificantly.
In another method, the open-circuit voltage (VOC ) or short-circuit currents (ISC ) are monitored frequently to determinethe MPP voltage (VMPP ) or MPP current (IMPP ) [14]–[16].These methods are known as fractional open-circuit voltagemethod and fractional short-circuit current (FSCC) methods.The accuracy of these methods are not guaranteed because theyapproximate constant ratio of VOC and VMPP or ISC and IMPP .
To overcome the disadvantages mentioned above, neural net-work [17] and fuzzy logic controller [18] based algorithms areproposed. But these algorithms require huge data storage andcomplex computation structure. For example, the fuzzy logiccontroller requires complex computation to handle differentstages, whereas the neural network algorithm requires largeamount of data for training. Further, an advanced hardware pro-cessor is necessary for these applications because in real time,the MPP is continuously varying with weather conditions.
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2 IEEE TRANSACTIONS ON ENERGY CONVERSION
Fig. 1. Equivalent circuit of the PV module.
The proposed algorithm is developed from the basic ideapresented in [19]. The proposed method uses the concepts ofconventional P&O and FSCC methods. In this method, currentperturbation is considered instead of voltage perturbation inconventional P&O to operate the PV panel at MPP. In order tohandle the rapidly varying atmospheric conditions [11], [20]–[22], an ACA is used. The ACA sets the operating point closerto MPP by estimating the short-circuit current (ISC ) and optimalproportionality constant (ksc opt). Estimating the short-circuitcurrent saves the losses in the system by avoiding frequentshorting of the system. The ACA gets activated only if thereis a sudden change in the irradiance (ΔS) or a sudden changein the PV current (ΔITH ). Further, variable current perturbation(ΔI) is derived, which varies continuously with the irradiance.The variable current perturbation reduces the oscillations aroundMPP. This scheme has the following improvements over theearlier work [19]: 1) variable current perturbation size ΔI isproposed; 2) ACA execution criteria is used in terms of ΔSand ΔITH ; 3) design aspects of the boost converter and currentcontroller are presented; and 4) experimental study is carriedout to validate the performance of proposed algorithm.
II. MPPT ALGORITHM
A. PV Module Model
The PV cell is basically a p-n junction fabricated in a thinwafer of semiconductor. It exhibits the nonlinear P–V and I–Vcharacteristics. Different equivalent circuit models of PV cellhave been discussed in the literature [20], [21], [23], [24]. Anequivalent circuit of the PV module is shown in Fig. 1. It con-sists of a photocurrent source, a parallel resistor representinga leakage current, a diode, and a series resistor describing aninternal resistance to the current flow.
The nonlinear voltage–current characteristic equation of thePV module is given as [25]
Ipv = NP Iph − NP Irs
(e
q (V pv / N S + I pv R s / N P )A k T − 1
)
− NP Vpv/NS + IpvRs
Rsh(1)
where Ipv is the terminal current (A), Iph is the light-generatedcurrent or photocurrent (A), Irs is the diode reverse saturation
Fig. 2. Flowchart of the proposed MPPT algorithm.
current, Vpv is the terminal voltage (V), q is the electron charge(1.609 × 10−19 C ), A is the diode ideality constant, k is Boltz-mann’s constant (1.38 × 10−23 J/K ), T is the cell absolutetemperature (K), NP is the number of cells in parallel, NS is thenumber of cells in series, and other parameters can be obtainedfrom specifications given in [26].
B. Adaptive P&O MPPT Algorithm
The objective of the MPPT algorithm is to extract the max-imum power from the PV panel. The proposed algorithm usesthe concepts of FSCC and conventional P&O algorithms. Theproposed algorithm is divided into two parts.
1) Current perturbation algorithm (CPA): The basic idea ofthis algorithm is developed from the conventional P&Oalgorithm. In this algorithm, current perturbation is con-sidered to increase the tracking speed instead of voltageperturbation. The CPA is explained in detail as shown inFig. 2.
2) Adaptive control algorithm (ACA): The basic idea of thisalgorithm is developed from the FSCC algorithm. In thisalgorithm, the new operating point is determined, such thatit is nearer to MPP. This new operating point is obtainedby multiplying optimal proportionality constant (ksc opt)and short-circuit current (ISC ). The ACA will be activatedonly if there is an abrupt change in the PV current orirradiance (S).
The generalized equation of PV reference current (Iref ), de-rived from the CPA, is given as
Iref = Ipv(k) + sign(Ipv(k) − Ipv(k − 1))
∗ sign(Ppv(k) − Ppv(k − 1)) ∗ ΔI (2)
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KOLLIMALLA AND MISHRA: NOVEL ADAPTIVE P&O MPPT ALGORITHM CONSIDERING SUDDEN CHANGES IN THE IRRADIANCE 3
Fig. 3. Operating characteristics of the PV panel.
where Ipv(k) is the PV panel current at kth iteration, Ppv(k) isthe PV panel power at kth iteration, ΔI is the current perturba-tion size, and sign(.) gives the polarity (+1 or –1) of the valueinside.
The basic idea behind considering the current perturbationinstead of voltage perturbation is explained as follows: At agiven weather conditions, the PV panel current in the left-handside (LHS) of MPP, i.e., 0 to VMPP region, as shown in Fig. 3,is almost constant. But in the right-hand side (RHS) of MPP,the current is drastically changing. Therefore, the PV panelreaches MPP relatively faster with reduction in oscillations, ifthe operating point is in the LHS region, even for small ΔIcompared to ΔV as shown in Fig. 3. On the other hand, ifthe operating current Ipv(k) is lying in the RHS and less than[IMPP − (ISC − IMPP)], then the CPA alone exhibits slowerperformance. Therefore, an ACA is proposed in addition toCPA. Based on the above analysis, the operating current rangefor which CPA alone gives faster performance is given as
2 IMPP − ISC ≤ Ipv(k) ≤ ISC . (3)
The ACA always operates the PV system within the operatingcurrent range as given in (3). Once the MPP is reached, then thevoltage at MPP (VMPP ) and the current at MPP (IMPP ) oscillatearound MPP. These oscillations depend on current perturbationsize. If the operating current violates (3) due to abrupt changein S, then Ipv is controlled such that the operating current lieswithin (3). The first step to control Ipv requires the short-circuitcurrent (ISC ). Various methods are available to get ISC , as men-tioned in the FSCC method. But these methods will result inlosses and interruption of power supply from the PV panel toload. To eliminate these losses, a generalized expression is de-rived to estimate ISC for changes in S and T .
The variation of ISC with temperature and irradiance givenin [27] is modified as given below
ISC = KTS
SnomISC(Snom , Tnom)KA (4)
where
KT = 1 + αIS C (T − Tnom) . (5)
Here, αIS C is the current temperature coefficient. KA is the cor-rection factor accounting for ambient temperature and PV panelaging. The parameters ISC(Snom ,Tnom), Tnom , and Snom are
short-circuit current, temperature, and irradiance at standardtest conditions (STC), respectively. Short-circuit current varia-tion with temperature is very small compared to the irradiance.Thus, in (4) and (5), the term KT is approximated to unity. Thisapproximation will save the temperature sensor cost, memory,and computation time. Therefore, ISC is approximated as
ISC ≈ S
SnomISC(Snom , Tnom)KA ≈ mS (6)
where m = IS C (Sn o m ,Tn o m ) KASn o m
.Once short-circuit current is estimated, then the new operating
current is calculated using the FSCC method. According to thismethod, IMPP is linearly related to ISC as given in [2]
IMPP ≈ kscISC (7)
where ksc is a proportionality constant, and its value lies between0.78 and 0.92. The constant ksc is determined such that 1) theoperating current lies within the limits of (3) and 2) all theMPP currents, 0.78 ISC to 0.92 ISC , of (7) are considered whiledetermining the new operating current. Based on the above twoconditions, ksc opt is determined by equating the lower currentlimit of (3) and lower MPP current of (7) as follows:
2 IMPP − ISC = 0.78 ISC . (8)
Substituting (7) and ksc = ksc opt into (8) gives ksc opt = 0.89.Therefore, the reference PV current is given as
Iref = 0.89 ISC
= 0.89mS . (9)
Finally, the ACA block in Fig. 2 is active only when there isa significant change in the irradiance ΔS or violation of currentlimits specified in (3). And the output of the ACA block is givenby (9).
C. Determination of Operating Current Limits
From (3), the upper current limit for a given S is given as
Iup = ISC . (10)
Substituting (6) into (10) gives
Iup = mS. (11)
From (3), the lower current limit for a given S is given as
Ilow = 2 IMPP − ISC . (12)
Substituting (6), (7), and ksc = ksc opt into (12) gives
Ilow = 0.78mS. (13)
D. Determination of ΔI
The threshold current range ΔITH for a given S is defined as
Let us assume that N is the maximum number of iterationsrequired to determine MPP in the ΔITH range from any ex-treme limit of (3). Therefore, the current perturbation size ΔIis defined as
ΔI =ΔITH
N
=0.22m
NS. (17)
From the above equation, it is clear that ΔI linearly depends onthe irradiance.
E. Determination of ΔS
Let us consider that S1 , S0 , and S2 are three irradiance levelswith S1 > S0 > S2 . The corresponding operating ranges forwhich the current perturbation alone gives satisfactory responseare defined as
2 IMPP1 − ISC1 ≤ Ipv(k) ≤ ISC1 (18)
2 IMPP0 − ISC0 ≤ Ipv(k) ≤ ISC0 (19)
2 IMPP2 − ISC2 ≤ Ipv(k) ≤ ISC2 . (20)
Let us assume that the PV panel is operating at S0 . Suddenly,the irradiance is increased to S1 such that the lower limit of (18)is equal to the upper limit of (19) as
2 IMPP1 − ISC1 = ISC0 . (21)
Substituting (6), (7), and ksc = ksc opt into (21) gives S1 =1.2821S0 . Therefore, the incremental limit of ΔS is defined as
ΔSINC = S1 − S0 = 0.2821 S0 . (22)
Similarly, assume that the irradiance is suddenly decreased fromS0 to S2 such that the lower limit of (19) is equal to the upperlimit of (20) as
2 IMPP0 − ISC0 = ISC2 . (23)
Substituting (6), (7), and ksc = ksc opt into (23) gives S2 =0.78S0 . Therefore, the decremental limit of ΔS is defined as
ΔSDEC = S0 − S2 = 0.22S0 . (24)
III. REALIZATION OF THE PROPOSED MPPT ALGORITHM
USING THE BOOST CONVERTER
A boost converter is used to realize the proposed algorithm.As shown in Fig. 4, the PV panel is connected to the boostconverter, where SW is the main switch, R is the load resistor,L is the filter inductor, and C is the filter capacitor.
Let us assume that the boost converter is operating in con-tinuous current mode. According to the state-space averagingmethod [28], the system dynamics are described by the follow-
Fig. 4. Circuit diagram of the PV system.
TABLE ISPECIFICATIONS OF THE PV PANEL AT STC (Snom = 1000
W/m2 , Tnom = 25 ◦C)
Parameters Symbol Value
Maximum power PM P P 240 WVoltage at maximum power VM P P 31.10 VCurrent at maximum power IM P P 7.72 AOpen-circuit voltage VO C 37.50 VShort-circuit current IS C 8.30 AModule efficiency (% ) η 14.13Current temperature coefficient αI S C (%/◦C) + 0.05Voltage temperature coefficient βV O C (%/◦C) −0.34
ing equations:
dipv
dt= − (1 − d)
Lvo +
vpv
L(25)
dvo
dt=
(1 − d)C
ipv − 1RC
vo (26)
where ipv , vpv , and vo are input current, input voltage, andoutput voltage of the boost converter, respectively and d is dutyratio.
A. Design of the Boost Converter
The boost converter is designed for MPPT application, at STCof the PV panel. The specifications of the PV panel are given inTable I.
Therefore, let us consider the input parameters of the boostconverter are Vpv = VMPP , Ipv = IMPP , and Ppv = PMPP .The output voltage (Vo ) of the converter is determined for loadresistance (R) of 25 Ω, assuming a loss less converter, i.e., Po
= Ppv . Therefore, Vo and duty ratio (D) are determined as
Vo =√
PoR = 77.459V (27)
D = 1 − Vpv
Vo= 0.598. (28)
As the PV panel voltage also varies with current, to minimize theoscillations at MPP, the inductor value is designed for 1% inputcurrent ripple (ΔIpv ) at switching frequency (fsw ) = 20 kHz as
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KOLLIMALLA AND MISHRA: NOVEL ADAPTIVE P&O MPPT ALGORITHM CONSIDERING SUDDEN CHANGES IN THE IRRADIANCE 5
Fig. 5. Block diagram of the linearized model.
given [28]
L =VpvD
2ΔIpvfsw= 6mH. (29)
The output capacitor is designed for 1% output voltage ripple(ΔVo ) as given [28]
C =VoD
2ΔVoRfsw= 59μF. (30)
The practical value of capacitance available to support the outputvoltage of 77.459 V is 220 μF at 160 V. To minimize the ESReffect, two capacitors are connected in parallel. Therefore, theeffective capacitance is C = 440 μF at 160 V.
B. Design of the Current Control Loop
Fig. 4 shows that the PV panel voltage and current (induc-tor current) are given to the MPPT controller which generatesthe reference current (i∗pv ). This current is given to the cur-rent control loop to control the switch by generating duty ra-tio. The proper design of the control loop ensures the systemstability and faster response. The design of the current con-trol loop involves the design of PI controller parameters. Intro-ducing small perturbation around the steady-state value for thestate variables and other quantities such that ipv = Ipv + ipv ,vpv = Vpv + vpv , vo = Vo + vo , and d = D + d, the linearizedsmall-signal model of the boost converter is described by thefollowing equations:
dipv
dt= − (1 − D)vo
L+
dVo
L+
vpv
L(31)
dvo
dt=
(1 − D)ipv
C− dIpv
C− vo
RC(32)
where vpv , ipv , vo , and d are small perturbations in input voltage,input current, output voltage, and duty ratio, respectively. Thelinearized block diagram of the boost converter is constructedusing (31) and (32) as shown in Fig. 5.
The transfer function of control to inductor current is deter-mined by simplifying the block diagram
Gid(s) =ipv
d=
VoCs + Vo
R + (1 − D)Ipv
LCs2 + LR s + (1 − D)2
. (33)
TABLE IINOMINAL PARAMETERS OF THE BOOST CONVERTER
Parameters Symbol Value
Input voltage Vpv 31.1 VInput inductor current Ipv 7.72 AOutput voltage Vo 77.459 VDuty ratio D 0.598Load resistance R 25 ΩInput inductor L 6 mHOutput capacitor C 440 μFSwitching frequency fsw 20 kHz
Fig. 6. (a) Bode plot of current control loop. (b) Root locus of current controlloop.
The transfer function of PI controller is given by
Gc(s) = Kp +Ki
s. (34)
The open-loop transfer function of current loop is given by
GOL(s) = Gid(s)Gc(s) . (35)
The nominal parameters of the boost converter considered aregiven in Table II. The Bode plot of GOL(s) with compensatorand without compensator is shown in Fig. 6(a). From the plot,
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6 IEEE TRANSACTIONS ON ENERGY CONVERSION
it is observed that GOL(s) without compensator has a phasemargin (PM) of 89.6◦ at 12.9 krad/s. As suggested in [22], thesystem should reach the steady state in each MPPT cycle. Thus,the proposed algorithm is executed at every 50th cycle of thefsw . Therefore, the gains of the compensator are designed suchthat PM is 60◦ at 2.51 krad/s. The proportional and integralgains calculated are 0.169 and 226.6, respectively. The rootlocus diagram is shown in Fig. 6(b). It shows that the closed-loop poles for designed PM are p1 = –177 (ζ = 1, ωn = 177krad/s) and p2,3 = −1.05e3 ± j1.38e3 (ζ = 0.607, ωn = 1.74krad/s), ensuring stability of the system.
IV. SIMULATION STUDIES
The proposed algorithm is validated for abrupt changes inthe irradiance through digital simulations. The PV module con-sidered for the simulation is Solarex MSX60 [29]. A PV arrayis formed by connecting six modules in series and six mod-ules in parallel. To demonstrate the concept of the ACA forsudden changes in the irradiance, fixed perturbations are con-sidered for both current and voltage. For the proposed algorithm,ΔI = 0.06 A and for the conventional P&O MPPT algorithm[20], ΔV = 1.25 V. The perturbations are chosen such that theproposed and conventional P&O MPPT algorithms give samedynamic performance in terms of oscillations and speed at STC.
In this study, the PV array is simulated for a sudden change inthe irradiance assuming constant temperature of 100◦C. Fig. 7(a)shows the tracking of MPP for the conventional and proposedMPPT algorithms for a sudden increase in the irradiance from400 to 800 W/m2 . Fig. 7(b) shows the tracking of MPP for theconventional and proposed MPPT algorithms for a sudden de-crease in the irradiance from 800 to 400 W/m2 . Fig. 7(c)–(e)shows the simulation results of current, voltage, and power ofthe PV array against number of iterations to reach the MPP.
Initially, the PV array is simulated at S = 400 W/m2 . For theconventional P&O MPPT algorithm, the operating point startsfrom point A and reaches the MPP B as shown in Fig. 7(a). Forthe proposed MPPT algorithm, the operating point starts frompoint F (initial operating point is set to ISC ) and reaches theMPP B as shown in Fig. 7(a).
At 50th iteration, the irradiance is increased to 800 W/m2 ;then, the operating point follows the path B-C-D-C-E to reachthe new MPP denoted by E for the conventional P&O MPPTalgorithm and B-E for the proposed MPPT algorithm.
At 100th iteration, the irradiance is decreased to 400 W/m2 ;then, the operating point follows the path E-Y-B to reach the newMPP denoted by B for the conventional P&O MPPT algorithmand E-B for the proposed MPPT algorithm as shown in Fig. 7(b).
Fig. 7(c)–(e) shows that the number of iterations required toreach MPP is less for the proposed MPPT algorithm. The graphsare zoomed in the intervals of 70–90 and 120–140 iterations toobserve the oscillations and perturbations. It is also observedthat for the proposed MPPT algorithm, the oscillations aroundMPP are minimized when compared to the conventional P&OMPPT algorithm.
From the above study, it is observed that the proposed al-gorithm shows the faster tracking performance along with the
Fig. 7. Simulation results of a sudden change in the irradiance: (a) PV curvefor a sudden increase in the irradiance; (b) PV curve for a sudden decrease inthe irradiance; (c) current; (d) voltage; and (e) power.
reduction in sustained oscillations for abrupt changes in theirradiance.
V. EXPERIMENTAL STUDIES
The proposed algorithm is realized using the boost converterand dSPACE 1104 real-time controller. Fig. 8 shows the experi-mental setup used to validated the performance of the proposedalgorithm. Implementation of the control system and data acqui-sition are done using dSPACE 1104 software with the DSP mod-ule in the PCI slot of the host computer. The nominal parametersof the boost converter are given in Table II. The control switchused in the boost converter is Semikron SKM 75GB128D. ThePV panel used for the experimental study is HHV Solar 240-W multicrystalline panel. And the specifications are given inTable I. A pyranometer, which is used to measure the irradi-ance, generates 0–3 V output corresponding to 0–1800 W/m2 .The generated output voltage from pyranometer is given to thedSPACE. LEM sensors are used to measure currents and volt-ages. Lecroy MSO 44MXs-B is used to observe the waveforms.
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KOLLIMALLA AND MISHRA: NOVEL ADAPTIVE P&O MPPT ALGORITHM CONSIDERING SUDDEN CHANGES IN THE IRRADIANCE 7
Fig. 8. Experimental setup.
Fig. 9. Variation of current and operating limits with the irradiance: (a) short-circuit current; (b) operating current limits; (c) incremental irradiance limit; and(d) decremental irradiance limit.
A. Determination of m, KA , and Operating Limits
Short-circuit currents are calculated for various values of KA
and irradiance using (6), and plotted against the irradiance asshown in Fig. 9. Further, short-circuit currents are measured fordifferent values of S by conducting the short-circuit test. These
calculated and measured short-circuit currents are superimposedon each other and plotted against S as shown in Fig. 9(a).From Fig. 9(a), it is observed that the calculated and measuredshort-circuit currents are approximately equal for KA = 1.4.Therefore, m = 0.0116 A per W/m2 is found by substitutingKA = 1.4 into (6).
The variation of upper and lower operating current limits iscalculated for various values of S using (11) and (13), respec-tively. These currents are shown in Fig. 9(b). If the operatingPV current is not lying between these curves, then the ACA isactivated.
The variation of incremental limit (ΔSINC ) is calculated us-ing (22) as shown in Fig. 9(c). If the increment in the irradiancelies above the curve, then the ACA gets activated. Similarly, thevariation of decremental limit (ΔSDEC ) is calculated using (24)as shown in Fig. 9(d). If the decrement in the irradiance liesabove the curve, then the ACA gets activated.
B. Experimental Results
The proposed algorithm is evaluated for 1) operating currentlimit violation, 2) a sudden decrease in the irradiance, and 3) asudden increase in the irradiance. In this study, sudden changesin the irradiance are artificially created by shading the PV panel.The current perturbation size ΔI for the proposed algorithmis determined by (17). The maximum number of iterations Nconsidered to reach MPP is equal to 14.
There is a tradeoff between response speed and oscillationsaround MPP in the conventional P&O MPPT algorithm [20].To compare both the algorithms, ΔV for the conventional P&OMPPT algorithm is determined by fixing the oscillations aroundMPP approximately equal to the oscillations obtained by pro-posed algorithm. Therefore, the performance is evaluated basedon response speed to reach MPP for the same amount of oscil-lations.
The experimental study is divided into two parts. For thesame conditions, the proposed algorithm is verified in the firstpart and the conventional P&O algorithm is compared in thesecond part. Finally, both the results are compared to observethe effectiveness of the proposed algorithm.
To demonstrate the performance of proposed algorithm, theboost converter is operated in three modes: 1) an inactive mode;2) a proposed algorithm mode; and 3) a conventional algorithmmode. In the inactive mode, the control switch is OFF so that thePV panel is directly connected to the load. The operating pointof the PV panel is decided by the load resistance. In the proposedand conventional algorithm modes, switching pulses are givento the control switch according to the individual control logic.
Fig. 10(a) shows the variation of irradiance pattern consideredin this study. It shows that the irradiance is suddenly decreasedat t2 and t6 instants. The irradiance is suddenly increased at t3and t7 instants. Initially, the boost converter is operated in theinactive mode. Therefore, the PV panel is directly connected tothe load resistance of 25 Ω. The corresponding input voltage,current, and power are 29 V, 1.16 A, and 33.6 W, respectively.Fig. 10(b) shows the voltage (vpv ), current (ipv ), and power (ppv )of the PV panel and output voltage (vo ) of the boost converter.
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8 IEEE TRANSACTIONS ON ENERGY CONVERSION
Fig. 10. Measured waveforms of proposed and conventional algorithms:(a) irradiance; (b) current, voltage, and power; (c) power; and (d) perturba-tion size.
At t1 instant, the proposed algorithm is activated. The irradi-ance measured at this instant is 454 W/m2 . According to (13),Ilow = 4.1 A for S = 454 W/m2 . But ipv = 1.16 A, which isless than Ilow . Therefore, the ACA gets activated, and set theinitial reference current to 4.68 A and ΔI to 0.082 A using (9)and (17), respectively. The CPA takes these values as inputs anddrives the operating point to MPP as shown in Fig. 10(b). It takesless than 0.1 s to reach MPP. The corresponding input voltage,current, power, and output voltage are 23.06 V, 4.28 A, 98.69W, and 44.72 V, respectively. This study shows the effectivenessof the ACA if there is an operating current violation.
At t2 instant, the irradiance is suddenly decreased from 458to 308 W/m2 . According to (24), ΔSDEC = 100 W/m2 . But theirradiance is decreased by 150 (= 458 − 308) W/m2 , which isgreater than ΔSDEC . Therefore, the ACA gets activated, andset the initial reference current to 3.17 A and ΔI = 0.056 A
using (9) and (17), respectively. The CPA drives the operatingpoint to MPP in less than 0.1 s as shown in Fig. 10(b). Thecorresponding input voltage, current, power, and output voltageare 27.33 V, 2.86 A, 78.16 W, and 40.51 V, respectively. Thisstudy shows the effectiveness of the ACA if there is a suddendecrease in the irradiance.
At t3 instant, the irradiance is suddenly increased from 310to 462 W/m2 . According to (22), ΔSINC = 87 W/m2 . But theirradiance is increased by 152 (= 462 − 310) W/m2 , which isgreater than ΔSINC . Thus, the ACA gets activated, and set theinitial reference current to 4.76 A with ΔI = 0.084 A using (9)and (17), respectively. From this instant, the CPA alone drivesthe operating point to MPP as shown in 10(b). For this inputvoltage, current, power, and output voltage are 23 V, 4.3 A, 98.9W, and 45 V, respectively. This illustrates the efficacy of theACA in the case of sudden changes in the irradiance.
At t4 instant, the boost converter is brought back to the inac-tive mode. Therefore, the PV panel is directly connected to theload resistance of 25 Ω. At t5 instant, the conventional MPPTalgorithm is activated with ΔV = 0.4 V. Comparing t1–t2 witht5–t6 regions as shown in Fig. 10(b), it is clear that the proposedalgorithm has faster response. At t6 instant, the irradiance issuddenly decreased to 306 W/m2 . Comparing t2–t3 intervalwith t6–t7 interval as shown in Fig. 10(b), it is clear that theproposed algorithm has faster response. At t7 instant, the irra-diance is suddenly increased to 457 W/m2 . The conventionalMPPT algorithm is able to track the MPP, but slower than theproposed MPPT algorithm as shown in Fig. 10(b).
To compare the proposed and conventional algorithms’ re-sponse speed, the output power of the PV panel is zoomed at t2 ,t3 , t6 , and t7 instants with equal duration of 1.8 s as shown inFig. 10(c). It was observed that the proposed algorithm reachesMPP faster with relatively lesser oscillations as compared to theconventional algorithm. Fig. 10(d) shows the variable currentperturbation size of the proposed algorithm. Since the proposedalgorithm has inherent variable current perturbation size, it fur-ther reduces the oscillations around MPP.
VI. CONCLUSION
The proposed MPPT algorithm is validated by developing ahardware prototype for sudden changes in the irradiance. Theproposed method consists of two algorithms, namely CPA andACA. These two algorithms are derived based on the conven-tional P&O algorithm and the FSCC method, respectively. TheACA moves the operating point closer to the MPP by multiply-ing the ISC with the optimal proportionality constant. The ACAgets activated only if there is a sudden change in the irradianceor sudden change in the PV current. In this proposed algorithm,the short-circuit current is estimated to determine the initialoperating point for a significant change in the irradiance ΔS.The operating point determined by this proposed method givesfaster response because the estimated ISC and measured ISC areapproximately equal. The ΔS and ΔI parameters are systemat-ically derived to ensure the fast response and minimized oscil-lations around MPP. The PI control parameters are determinedby deriving small-signal modeling of the boost converter. The
This article has been accepted for inclusion in a future issue of this journal. Content is final as presented, with the exception of pagination.
KOLLIMALLA AND MISHRA: NOVEL ADAPTIVE P&O MPPT ALGORITHM CONSIDERING SUDDEN CHANGES IN THE IRRADIANCE 9
experimental results show that the proposed algorithm givesfaster response than the conventional algorithm for suddenchanges in the irradiance.
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Sathish Kumar Kollimalla (S’12) received theBachelor’s degree from the Viswanadha Institute ofTechnology and Management College of Engineer-ing, Visakhapatnam, India, in 2003, and the Mas-ter’s degree in engineering from Andhra University,Visakhapatnam, in 2006. He is currently working to-ward the Ph.D. degree in electrical engineering atthe Indian Institute of Technology Madras, Chennai,India.
His research interests include power electronicsapplications in power systems, microgrid, renewable
energy systems, and power quality.
Mahesh Kumar Mishra (S’00–M’02–SM’10) re-ceived the B.Tech. degree from the College of Tech-nology, Pantnagar, India, in 1991; the M.E. degreefrom the University of Roorkee, Roorkee, India, in1993; and the Ph.D. degree in electrical engineeringfrom the Indian Institute of Technology, Kanpur, In-dia, in 2002.
For approximately ten years, he was with the Elec-trical Engineering Department, Visvesvaraya Na-tional Institute of Technology, Nagpur, India. He iscurrently a Professor in the Electrical Engineering
Department, Indian Institute of Technology Madras, Chennai, India. His re-search interests include power distribution systems, power electronic applica-tions in microgrid, and renewable energy systems.
Dr. Mahesh is Life Member of the Indian Society of Technical Education.
