a novel improved variable step size of digital mppt controller … · 2017. 12. 27. · a novel...

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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



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.


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.


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.


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.


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.




(a)

(b)

(c)

Fig.5 (a),(b),(c). PV panel voltage, current, and load


controller with battery load for LCA algorithm.

(a)

(b)

(c)

Fig.6(a),(b),(c). PV panel voltage, current, and load


controller with battery load for 1% FXS algorithm.




(a)

(b)

(c)

Fig.7.(a),(b),(c). PV panel voltage, current, and load


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.




(a)

(b)

(c)

Fig.9. (a),(b),(c) PV panel voltage, PV panel current, and


of controller with resistive load for LCA algorithm.

(a)

(b)

(c)

Fig.10(a),(b),(c) PV panel voltage, PV panel current, and


of controller with resistive load for 1% FXS algorithm.




(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.




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

inverter topology for solar PV systems with maximum

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

based on DC bus,” IEEE Trans. Power Electron., vol. 26,

no. 2, pp. 523–531, Feb. 2011.

[6] Z. Liang, R. Guo, J. Li, and A. Q. Huang, “A high-

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energy harvest in FREEDM systems,” IEEE Trans. Power

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[7] L. Zhang, W. G. Hurley, and W. H. W¨olfle, “A new

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Electron., vol. 26, no. 4, pp. 1031–1037, Apr. 2011.

[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.

a novel improved variable step size of digital mppt controller … · 2017. 12. 27. · a novel...

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