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www.ijatir.org
ISSN 2348–2370
Vol.07,Issue.01,
January-2015,
Pages:0065-0072
Copyright @ 2015 IJATIR. All rights reserved.
A Novel Improved Variable Step Size of Digital MPPT Controller
For A Single Sensor in Photo Voltaic System
K.MURALIDHAR REDDY1, K.MEENENDRANATH REDDY
2, G.VENKATA SURESH BABU
3
1PG Scholar, Dept of EEE (EPS), SITS, Kadapa, Andhrapradesh, India.
2Assistant Professor, Dept of EEE, SITS, Kadapa, Andhrapradesh, India.
3Associate Professor & HOD, Dept of EEE, SITS, Kadapa, Andhrapradesh, India.
Abstract: Recent researches focus mainly on the solar
energy that almost all the part of this world receives
abundantly with variation in its potential. Many studies have
made it possible to convert these energies in to more
efficient electrical energy. The interference of power
electronics in almost of all the fields have made more
sophistication in industries with loads that require the most
efficient and accurate amount of supply. In this paper a new
Digital Control Technique Based MPPT is proposed to
Track and Maximum Power. MPPT is a method to obtain
the maximum power from a module in any weather
condition. As solar energy is varying in nature, the MPPT is
the main focus of energy conservation. By the V - I
characteristics of solar energy, there is only one point in its
curve where the maximum power is achieved. The Digital
Controllers used in this paper are an adaptive step- size and
adaptive-perturbation-frequency algorithm, by utilizing a
variable step-size algorithm, the speed, accuracy, and
efficiency of the PV system MPPT are improved when
compared to the fixed step-size load-current-based
algorithm. Tracking that particular point with accuracy has
developed many algorithms in this field. Furthermore, the
proposed adaptive algorithm utilizes a novel variable
perturbation frequency scheme which further improves the
controller speed. Matlab Simulink Software is used to solve
the Project and both the Controller are Compared.
Keywords: Adaptive-Perturbation-Frequency Perturb And
Observe (P&O) Algorithm, Adaptive Step Size, Dc–Dc
Converters, Maximum Power Point Tracking (MPPT),
Photovoltaic (PV), Solar Energy.
I. INTRODUCTION
Photovoltaic (PV) panels are used to convert solar energy
into electric power. The solar PV panel output
characteristics are dependent on operating conditions such
as surrounding temperature and irradiance level. Maximum
power points (MPPs) exist on the PV panel characteristic
curves at which point the output power from the solar panel
is maximum. Maximum power point tracking (MPPT)
algorithms and techniques such as perturb and observe
(P&O) algorithm, incremental conductance (InCond)
algorithm, ripple correlation control (RCC) algorithm,
fractional voltage/current MPPT method and neural-network
(NN)-based MPPT control has developed to extract the
maximum power from the PV panel. The P&O method,
which identifies the MPP using the slope of the P–V
characteristics curve, it is widely used due to its minimalism
and ease of implementation. A main disadvantage of the
P&O algorithm is that the PV panel operation points
oscillate through the MPP which occurs energy loss. InCond
algorithms overcome the drawbacks of P&O algorithms by
removing the oscillations around the MPPs. However, the
InCond MPPT algorithm needs real time calculation of the
slope of the PV panel power curve, it is more complicated to
be implemented in controller compared to the P&O
algorithm.
The RCC MPPT algorithm uses the derivatives of the
power converter’s voltage and current ripples to determine
the position of the PV panel operating point. One of its
drawbacks in this method is that if the power converter’s
switching frequency varies, it has to redesign the high pass
filter circuit which is used to attain time derivatives of PV
panel voltage and current. The fractional voltage/current
methods sets the optimal voltage/current reference as a
fraction of the PV solar panel’s open-circuit voltage or
short-circuit current, and therefore, it does not track the real
MPP. Even though this method has an acceptable tracking
performance under steady state conditions, it may fail to
converge to new MPP under transient conditions. The NN-
based MPPT controller improves the tracking efficiency of
the system by utilizing a multilayer control structure;
however, this method involves computational iterations and
increases the calculation load of the controller. All the
MPPT methods discussed above require the sensing of PV
panel voltage and current they need multiplication function
to attain the PV panel power values which increase the size
and the power consumption of the controller.
The load-current-based MPPT method with the fixed step
size (FXS) perturbation P&O algorithm has been proposed
to realize MPPT functionality by sensing only the load
current. Which limts the need for a multiplier that is
required to attain the power value in the conventional
power-based MPPT methods. Adaptive-perturbation-step-
K.MURALIDHAR REDDY, K.MEENENDRANATH REDDY, G.VENKATA SURESH BABU
International Journal of Advanced Technology and Innovative Research
Volume.07, IssueNo.01, January-2015, Pages: 0065-0072
size algorithms are studied to provide fast dynamic
convergence speed and high steady-state tracking efficiency.
Different from, in which a fixed scaling factor and a fixed
MPPT frequency algorithm is used, utilize an adaptive
scaling factor and the fixed frequency MPPT algorithm to
optimize the controller speed during transient. However, the
algorithm becomes more complicated: the algorithm
requires the information of the location of the PV panel
operation point and the controller is switching between
adaptive duty cycle control and fixed duty cycle control.
Generally, in a PV system with MPPT control, large
perturbation requires longer settling time after perturbation
is triggered and small perturbations require shorter settling
time. In previous works, which either implement the FXS
MPPT algorithm or an adaptive-step-size MPPT algorithm,
the period of perturbation is fixed.
This fixed perturbation time period is selected to ensure
that the system has sufficient time to settle down when the
largest perturbation is triggered. Though, this results in
longer MPPT controller response time when the operating
point is near to MPP. This issue is addressed to the proposed
LCASF MPPT algorithm by using an adaptive-perturbation
frequency scheme with higher perturbation frequency when
the perturbation is smaller, and vice versa. Digital
controllers are increasingly being used in a renewable
energy system control because of their ability to perform
advanced control algorithms among other advantages such
as easy to be reconfigured and upgraded. Therefore, the
LCASF MPPT controller is realized by a digital controller.
This digital controller is implemented in this paper by using
a microcontroller (MCU), but can be also implemented by
field programmable gate array.
II. LCASF MPPT ALGORITHM
Fig. 1 shows a PV solar system block diagram with the
proposed LCASF MPPT controller. The power conversion
process from the PV panel to the load (battery load or
resistive load) interfaced through a dc–dc converter with
efficiency equal to η. The dc–dc converter regulates the
voltage and current of the solar panel and thus it regulates
the output power. The MPPT controller keeps adjusting the
duty cycle of the power converter to reach the MPP of the
solar panel.
Fig.1. Block diagram of a PV solar power system with
load current MPPT control.
In the conventional power-based P&O MPPT algorithm,
the derivative of power to voltage dP/dV of a PV panel is
used as a tracking parameter. The tracking of zero slope at
MPP is valid in the system with a resistive load. The
relationship between input power and output current with
power stage duty cycle is illustrated in Fig. 2. In the
conventional load-current-based P&O algorithm, the duty
cycle perturbation step size ΔD is a fixed value. During
steady-state MPPT operation, small ΔD reduces the power
losses caused by the oscillations around the MPP. During
transient MPPT operation, larger ΔD is preferred for faster
convergence to the new MPP.
Fig.2. Relationship between input power and output
current with power converter duty cycle.
Variable step-size algorithms are generally developed in
order to attain swapping between the speed and the accuracy
of the tracking. The proposed LCASF MPPT digital
controller algorithm flowchart is shown in Fig. 3. The
control tactic of this algorithm is to continuously adjust the
duty cycle perturbation values and adjust the perturbation
frequency while observing the load current Io.
Fig.3 LCASF MPPT algorithm flowchart.
A Novel Improved Variable Step Size of Digital MPPT Controller For A Single Sensor in Photo Voltaic System
International Journal of Advanced Technology and Innovative Research
Volume.07, IssueNo.01, January-2015, Pages: 0065-0072
The two types of schemes are mainly present in this
algorithm they are, adaptive determination of the
perturbation values ΔD and the adaptive determination of
perturbation periods T. After ΔD and T values are attained,
the duty cycle of the power stage is perturbed by ΔD and
after waiting T period of time, the MPPT controller starts
the next perturbation.
III. ADDITIONAL COMPARISONS WITH OTHER
MPPT ALGORITHMS
The following is a summary between the proposed
LCASF MPPT controller algorithm and the other MPPT
algorithms discussed in Section I:
General comparison between the proposed LCASF
MPPT control and the other MPPT algorithms in
terms of MPPT tracking speed: Unlike the LCASF
MPPT controller, all the other existing MPPT
controllers that are discussed in the literature use a
fixed MPPT perturbation period, even when they
utilize variable duty cycle or references voltage.
perturbation step size, which results in extensively
longer MPP tracking time because this fixed
perturbation period is set long enough for the case
when the duty cycle perturbation step size is largest.
Additional comparison between the proposed LCASF
MPPT control algorithm and the conventional P&O
control algorithm. The conservation of P&O MPPT
has necessary as sensing the both PV panel current
and voltage.
Additional comparison between the proposed LCASF
MPPT control algorithm and the conventional
InCond MPPT algorithm. Which has both
conventional InCond MPPT algorithm and tracking
speed of LCASF MPPT controller utilizes the
proposed variable MPPT perturbation. Even though
same can be seen.
Additional comparison between the proposed LCASF
MPPT control algorithm and the conventional RCC
MPPT algorithm. The RCC MPPT is same as P&O
MPPT algorithm except in execution in frequency
switching.
Additional comparison between the proposed LCASF
MPPT control algorithm and the conventional
fractional voltage and fractional current method. This
methods need only one sensor i.e either voltage or
current they provide approximation to the MPP,
which tracks low MPP efficiency.
Additional comparison between the proposed LCASF
MPPT control algorithm and the NN-based MPPT
algorithm. The LCASF MPPT control algorithm
MPP tracking speed is faster than NN-based MPPT
algorithm because NN-based algorithm use fixed
algorithm update period i,e 100Hz.
V. SIMULATION RESULTS
The power converter topology used is a synchronous dc–
dc buck converter, operating in continuous conduction mode
with 100-kHz switching frequency and PWM control. PV
panel ratings and power converter parameters are identical
with the parameters in the system response time analysis in
the last section and results as shown in Figs.4 t0 12.
(a)
(b)
(c)
Fig.4.(a),(b),(c) PV panel voltage, current, and load
current waveforms under input transient response of
controller with battery load for LCASF algorithm.
K.MURALIDHAR REDDY, K.MEENENDRANATH REDDY, G.VENKATA SURESH BABU
International Journal of Advanced Technology and Innovative Research
Volume.07, IssueNo.01, January-2015, Pages: 0065-0072
(a)
(b)
(c)
Fig.5 (a),(b),(c). PV panel voltage, current, and load
current waveforms under input transient response of
controller with battery load for LCA algorithm.
(a)
(b)
(c)
Fig.6(a),(b),(c). PV panel voltage, current, and load
current waveforms under input transient response of
controller with battery load for 1% FXS algorithm.
A Novel Improved Variable Step Size of Digital MPPT Controller For A Single Sensor in Photo Voltaic System
International Journal of Advanced Technology and Innovative Research
Volume.07, IssueNo.01, January-2015, Pages: 0065-0072
(a)
(b)
(c)
Fig.7.(a),(b),(c). PV panel voltage, current, and load
current waveforms under input transient response of
controller with battery load for 5% FXS algorithm.
(a)
(b)
(c)
Fig.8(a),(b),(c). PV panel voltage, PV panel current, and
load current waveforms under input transient response
of controller with resistive load for LCASF algorithm.
K.MURALIDHAR REDDY, K.MEENENDRANATH REDDY, G.VENKATA SURESH BABU
International Journal of Advanced Technology and Innovative Research
Volume.07, IssueNo.01, January-2015, Pages: 0065-0072
(a)
(b)
(c)
Fig.9. (a),(b),(c) PV panel voltage, PV panel current, and
load current waveforms under input transient response
of controller with resistive load for LCA algorithm.
(a)
(b)
(c)
Fig.10(a),(b),(c) PV panel voltage, PV panel current, and
load current waveforms under input transient response
of controller with resistive load for 1% FXS algorithm.
A Novel Improved Variable Step Size of Digital MPPT Controller For A Single Sensor in Photo Voltaic System
International Journal of Advanced Technology and Innovative Research
Volume.07, IssueNo.01, January-2015, Pages: 0065-0072
(a)
(b)
(c)
Fig.11. (a),(b),(c) PV panel voltage, PV panel current,
and load current waveforms under input transient
response of controller with resistive load for 5% FXS
algorithm.
(a)
(b)
(c)
(d)
Fig.12. (a),(b),(c),(d) PV panel voltage, current, load
current, and voltage waveforms under load voltage
transient with LCASF controller.
K.MURALIDHAR REDDY, K.MEENENDRANATH REDDY, G.VENKATA SURESH BABU
International Journal of Advanced Technology and Innovative Research
Volume.07, IssueNo.01, January-2015, Pages: 0065-0072
V. CONCLUSION
This paper presented a load-current-based variable-step-
size and variable-perturbation-frequency MPPT digital
controller. In addition to utilizing a function to adapt the
duty cycle perturbation, the proposed MPPT controller
adapts its perturbation frequency as a function of the
variable duty cycle perturbation value. As the results
presented in this paper showed, the duty cycle adaptive-
step-size scheme used in the proposed MPPT controller
yields a good tradeoff between the convergence speed and
tracking efficiency compared to the FXS algorithm.
Furthermore, the novel adaptive perturbation frequency
scheme used in the proposed controller results in faster
convergence speed compared to existing adaptive-step-size
algorithms. The proposed adaptive perturbation frequency
scheme could also be used with other MPPT algorithms.
VI. REFERENCES
[1] H. S.-H. Chung, K. K. Tse, S. Y. R. Hui, C. M. Mok,
and M. T. Ho, “A novel maximum power point tracking
technique for solar panels using a SEPIC or Cuk converter,”
IEEE Trans. Power Electron., vol. 18, no. 3, pp. 717–724,
May 2003.
[2] A. I. Bratcu, I. Munteanu, S. Bacha, D. Picault, and B.
Raison, “Cascaded DC–DC converter photovoltaic systems:
Power optimization issues,” IEEE Trans. Ind. Electron., vol.
58, no. 2, pp. 403–411, Feb. 2011.
[3] Y. H. Ji, D. Y. Jung, J. G. Kim, J. H. Kim,T. W. Lee,
and C. Y.Won, “A real maximum power point tracking
method for mismatching compensation in PV array under
partially shaded conditions,” IEEE Trans. Power Electron.,
vol. 26, no. 4, pp. 1001–1009, Apr. 2011.
[4] S. Jain and V. Agarwal, “A single-stage grid connected
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power point tracking,” IEEE Trans. Power Electron., vol.
22, no. 5, pp. 1928–1940, Sep. 2007.
[5] L. Zhang, K. Sun, Y. Xing, L. Feng, and H. J. Ge, “A
modular gridconnected photovoltaic generation system
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[6] Z. Liang, R. Guo, J. Li, and A. Q. Huang, “A high-
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[7] L. Zhang, W. G. Hurley, and W. H. W¨olfle, “A new
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[8] L. Zhou, Y. Chen, K. Guo, and F. Jia, “New approach
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[9] G. Petrone, G. Spagnuolo, and M. Vitelli, “A
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[10] N. Femia, G. Petrone, G. Spagnuolo, and M. Vitelli,
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Author’s Profile:
K. Muralidhar Reddy has received
the B.Tech (Electrical And Electron-
ics Engineering) degree from Madina
engineering college, Kadapa in 2010
and persuing M.Tech (Electrical
Power Systems) in Srinivasa Institute
of Technology and Science, Kadapa,
AP, India.
K. Meenendranath reddy has 4 years
of experience in teaching in Graduate
and Post Graduate level and he
Presently working as Assistant
Professor in department of EEE in
SITS, Kadapa, AP, India.
G.Venkata Suresh Babu has 12
years of experience in teaching in
Graduate and Post Graduate level and
he Presently working as Associate
Professor and HOD of EEE department
in SITS, Kadapa, AP, India.
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