Comparison Of Perturb & Observe and Incremental Conductance Algorithms for MPPT Controller

International Journal of Advance Foundation and Research in Computer (IJAFRC)

Volume 2, Issue 9, September - 2015. ISSN 2348 – 4853, Impact Factor – 1.317

15 | © 2015, IJAFRC All Rights Reserved www.ijafrc.org

Comparison Of Perturb & Observe and Incremental

Conductance Algorithms for MPPT Controller

Mr. Bhalchandra V. Chikate Prof. D. R. Dandekar Prof. Mrs. Y. A. Sadawarte

P.G.Student Associate Professor Assistant professor

B.D.C.O.E,SEVAGRAM,WARDHA

[email protected], [email protected], [email protected].

ABSTRACT

The term photovoltaic refers to the phenomenon involving the conversion of sunlight into

electrical energy via a solar cell. There is various control techniques used for this conversion. In

Photovoltaic power generation there are two big problems which are less conversion efficiency of

PV modules & amount of power generation depends on weather conditions. And also, the PV cell I-

V characteristic be non-linear due to complex relationship between voltage and current and vary

with change in temperature or insolation. There is single point on I-V or P-V characteristics curve

knows as Maximum Power Point where PV system gives highest efficiency and produces highest

output power. Hence it is essential to use Maximum Power Point Tracking (MPPT) for PV system.

P&O and INC algorithms are most commonly used to track MPP. In this algorithm the duty cycle of

DC-DC converter is adjusted.

Recently microcontroller or DSP controller is used to implement MPPT algorithm. Number of

advantages provided by FPGA over sequential machine microcontroller or DSP as FPGA is faster

and instructions executed continuously and simultaneously.

Index Terms— MPPT, Photovoltaic System, P&O & INC Algorithm, DC-DC Converter, FPGA.

I. INTRODUCTION

Energy which comes from natural resources such as sunlight, wind, rain, geothermal heat etc. is called

renewable energy. Renewable energy is very important because the non-renewable energy such as

petrol, diesel, and fossil fuels are limited. Solar energy is the most easily available source of energy. Most

important it is non-conventional source of energy because it is non-polluting, clean etc.[7] The

photovoltaic (PV) systems are most effective at remote sites off the electrical grid. In this system, there is

required a storage battery. Additional energy generate during times with no or low loads charges the

battery, so at times with no or too low solar radiation the loads are met by discharging it. A charge

controller supervises the charging/discharging process in order to ensure a long battery lifetime.

Power generation and storage control mechanism is one of the significant and an essential factor in the

generation as well as controlling system. There are number of classical methods are available in the

electronics. In such areas controlling the system in remote way with efficiency is becoming a hurdle and

to overcome it in last few years numerous alternative control techniques, have been developed. There

are various methods of controlling of solar power generation and battery storage system on which

efficiency is being worked out as a major issue.




The term photovoltaic refers to the phenomenon involving the conversion of sunlight into electrical

energy via a solar cell. In Photovoltaic power generation there are two big problems which are less

conversion efficiency of PV modules & amount of power generation depends on weather conditions. And

also, the PV cell I-V characteristic be non-linear due to complex relationship between voltage and current

and vary with change in temperature or insolation. There is single point on I-V or P-V characteristics

curve knows as Maximum Power Point where PV system gives highest efficiency and produces highest

output power. The main source of the power loss is the failure to track MPP. So, Maximum Power Point

Tracking is essential to operate PV system at MPP. The most commonly used methods to track MPP are

the INC algorithm and P&O algorithms by adjusting duty cycle of DC to DC converter. Performance of a

photovoltaic-based system strongly depends upon the capability to determine an optimal operating point

of the solar array at which the maximum power can be drawn for any given load. Under certain

temperature and light intensity, there is only single maximum-power point in a normal cell. Therefore,

maximum power point tracking (MPPT) of the solar cell is essential as far as the system efficiency is

concerned.

There are many MPPT methods available in the literature; the most widely-used techniques are

described in the following sections. There are total seven MPPT methods found in literature, out of these

seven methods in this work we studied two algorithms.

1. Constant Voltage Method

2. Short-Current Pulse Method

3. Open Voltage Method

4. Fuzzy Logic Control

5. Neural Network Control

6. Perturbation and Observation Method

7. Incremental Conductance Method

In this project we taken two most well known algorithm i.e. Perturb and Observed algorithm and

Incremental Conductance algorithm.

II. PHOTOVOLTAIC ARRAY

PV arrays essentially consist of a number of internal silicon based photovoltaic cells combined in series

and in parallel, depending on the voltage or current requirements. These cells are used to convert solar

energy into electricity. This occurs when the photovoltaic cells are exposed to solar energy causing the

cells electrons to drift which, in turn, produces an electric current. This current varies with the size of

individual cells and the light intensity.[2][3]

Photovoltaic cells, or solar cells as they are more commonly referred to, are available commercially in a

number of different semiconductor materials. The most common materials are monocrystalline silicon,

polycrystalline silicon, amorphous silicon and copper-indium selenide (CIS). These technologies consist

of p-n junction diodes capable of generating electricity from light sources and usually have efficiencies of

6% - 20% in commercial use.

1. Equivalent Circuit Of A PV Cell

The equivalent circuit of a PV cell is demonstrated below in Figure 1.




Fig. 1 Equivalent circuit of a PV cell[8]

Derived from Kirchhoff’s first law (also referred to as Kirchhoff’s current law), the output current is given

by

I = I�� − I� − I�

I = I�� − I��. (exp (q. (V� + I. R�))(n. K. T��. N�) − 1) −V� + I. R�

R�

Where

I Output current

Iph Photo current

Isat Diode reverses saturation voltage

Vo Output Voltage

Rs Series resistance (Representing voltage loss on the way to external connectors)

Rp Parallel resistance (Representing leakage currents)

k Boltzmann’s constant

q Charge on electron

Ns Number of cells in series

N Ideality factor

Tcell Solar panel temperature

The I-V characteristics of a typical solar cell are as shown in the Figure 2.

Fig. 2. I-V characteristics of a solar panel[2]

2 Maximum Power Point Tracking Algorithm

A typical solar panel converts only 30 to 40 percent of the incident solar irradiation into electrical energy.

Maximum power point tracking technique is used to improve the efficiency of the solar panel.




There is single point on I-V or P-V characteristics curve knows as Maximum Power Point where PV

system gives highest efficiency and produces highest output power. The main source of the power loss is

the failure to track MPP. So, Maximum Power Point Tracking is essential to operate PV system at MPP.

The most commonly used methods to track MPP are the INC algorithm and P&O algorithms by adjusting

duty cycle of DC to DC converter. Performance of a photovoltaic-based system strongly depends upon the

capability to determine an optimal operating point of the solar array at which the maximum power can

be drawn for any given load. Under certain temperature and light intensity, there is only single

maximum-power point in a normal cell. Therefore, maximum power point tracking (MPPT) of the solar

cell is essential as far as the system efficiency is concerned.

There are different techniques used to track the maximum power point. [6] Few of the most popular

techniques are:

1) Perturb and Observe (hill climbing method)

2) Incremental Conductance method

3) Fractional short circuit current

4) Fractional open circuit voltage

5) Neural networks

6) Fuzzy logic

The choice of the algorithm depends on the time complexity the algorithm takes to track the MPP,

implementation cost and the ease of implementation.

III. PERTURBATION AND OBSERVATION METHOD

P&O method [3], [6], [9] is the most frequently used algorithm to track the maximum power due to its

simple structure and fewer required parameters. This method finds the maximum power point of PV

modules by means of iteratively perturbing, observing and comparing the power generated by the PV

modules. It is widely applied to the maximum power point tracker of the photovoltaic system for

its features of simplicity and convenience. According to the structure of MPPT system shown in Fig. 3, the

required

parameters of the power-feedback type MPPT algorithms are only the voltage and current of PV modules.

Shown in Fig.5 is the relationship between the terminal voltage and output power generated by a PV

module. It can be observed that regardless of the magnitude of sun irradiance and terminal voltage of PV

modules, the maximum power point is obtained while the condition dP/dV = 0 is accomplished. The slope

(dP/dV) of the power can be calculated by the consecutive

output voltages and output currents, and can be expressed as follows,

�� (!) =

�(")#�("#$) (")# ("#$) (1)

Where P (n) = V(n) I(n)




Fig.3 A structure of PV system with MPPT function

Fig. 4 P-V characteristic of a PV module

Basically, two MPPT algorithms discussed in this paper have to achieve the condition (dP/dV = 0) to find

the maximum power point of PV modules. The difference among the selected two MPPT algorithms is the

method used to meet the condition.

The basic operating procedure of P&O method is shown in Fig. 5. In a fixed period of time, the load of the

PV system is adjusted in order to change the terminal voltage and output power of the PV modules. The

variations of the output voltage and power before and after changes are then observed and compared to

be the reference for increasing or decreasing the load in the next step. If the perturbation in this time

results in greater output power of PV modules than that before the variation, the output voltage of PV

modules will be varied toward the same direction. Otherwise, if the output power of PV modules is less

than that before variation, it indicates that the varying direction in the next step should be changed. The

maximum output power point of a PV system can be obtained by using these iterative perturbation,

observation and comparison steps.

The advantages of the P&O method are simple structure, easy implementation and less required

parameters. The shortcomings of the P&O method can be summarized: (a) The power tracked by the P&O

method will oscillate and perturb up and down near the maximum power point. The magnitude of

oscillations is determined by the magnitude of variations of the output voltage. (2) There is a

misjudgement phenomenon for the P&O method when weather conditions change rapidly. From the

diagram shown in Fig. 6, the starting point is point A, and a + ΔV voltage perturbation will move the

operating point from A to B and cause a decreasing power when the weather condition is steady.




According to the judgment rules of the P&O method, the next perturbation should be changed to − ΔV in

the opposite direction. However, if the sun irradiance increases in one sampling period, the power curve

will be moved from P1 to P2, and the operating point will be moved from A to C instead of A to B. This

results in the power to be increased continuously, and the voltage perturbation still moves toward + ΔV

direction. The operating point is then farther away from the maximum power point. If the sun irradiance

continuously increases, the distance between operating point and maximum power point will be farther.

Consequently, the power loss of PV modules will increase, and the efficiency of the PV system will reduce.

Fig. 5 The flow diagram of the P&O method

Fig. 6 The separation diagram of the maximum power point for the P&O method

IV. INCREMENTAL CONDUCTANCE METHOD




The theory of the incremental conductance method [3]-[13] is to determine the variation direction of the

terminal voltage for PV modules by measuring and comparing the incremental conductance and

instantaneous conductance of PV modules. If the value of incremental conductance is equal to that of

instantaneous conductance, it represents that the maximum power point is found. The basic theory is

illustrated with Fig. 7.

Fig. 7 The schematic diagram of the incremental conductance method

When the operating behaviour of PV modules is within the constant current area, the output power is

proportional to the terminal voltage. That means the output power increases linearly with the increasing

terminal voltage of PV modules (slope of the power curve is positive, dP/dV > 0). When the operating

point of PV modules passes through the maximum power point, its operating behavior is similar to

constant voltage. Therefore, the output power decreases linearly with the increasing terminal voltage of

PV modules (slope of the power curve is negative, dP/dV < 0). When the operating point of PV modules is

exactly on the maximum power point, the slope of the power curve is zero (dP/dV = 0) and can be further

expressed as,

�� =

�( %)� = & � � + '

�%� = & + ' �%

� (2)

By the relationship of dP/dV = 0, (2) can be rearranged as follows,

(&(' = −

&'(3)

dI and dV represent the current error and voltage error before and after the increment respectively. The

static conductance (Gs) and the dynamic conductance (Gd, incremental conductance) of PV modules are

defined as follows,

*+ =− &' (4)

*� = (&('(5)

The maximum power point (operating voltage is Vm) can be found when




*� =*+(6)

When the equation in (3) comes into existence, the maximum power point is tracked by MPPT system.

However, the following situations will happen while the operating point is not on the maximum power

point:

(&(' >−

&' ;1*� >*+,

(3(' > 05(7)

(&(' <−

&' ;1*� <*+,

(3(' < 05(8)

Equations (7) and (8) are used to determine the direction of voltage perturbation when the operating

point moves toward to the maximum power point. In the process of tracking, the terminal voltage of PV

modules will continuously perturb until the condition of (3) comes into existence.

Fig. 8 is the operating flow diagram of the incremental conductance algorithm. The main difference

between incremental conductance and P&O algorithms is the judgment on determining the direction of

voltage perturbation. When static conductance Gs is equal to dynamic conductance Gd, the maximum

power point is found [8].

From the flow diagram shown in Fig. 8, it can be observed that the weather conditions don’t change and

the operating point is located on the maximum power point when dV = 0 and dI = 0. If dV = 0 but dI > 0, it

represents that the sun irradiance increases and the voltage of the maximum power point rises.

Meanwhile, the maximum power point tracker has to raise the operating voltage of PV modules in order

to track the maximum power point. On the contrary, the sun irradiance decreases and the voltage of the

maximum power point reduces if dI < 0. At this time the maximum power point tracker needs to reduce

the operating voltage of PV modules.




Fig. 8 The flow diagram of the incremental conductance method

Furthermore, when the voltage and current of PV modules change during a voltage perturbation and

dI/dV > －I/V (dP/dV > 0), the operating voltage of PV modules is located on the left side of the maximum

power point in the P-V diagram, and has to be raised in order to track the maximum power point. If dI/dV

< －I/V (dP/dV < 0), the operating voltage of PV modules will be located on the right side of the maximum

power point in the P-V diagram, and has to be reduced in order to track the maximum power point.

The advantage of the incremental conductance method, which is superior to those of the other two MPPT

algorithms, is that it can calculate and find the exact perturbation direction for the operating voltage of

PV modules. In theory, when the maximum power point is found by the judgment conditions (dI/dV =

－I/V and dI = 0) of the incremental conductance method, it can avoid the perturbation phenomenon

near the maximum power point which is usually happened for the other two MPPT algorithms. The value

of operating voltage is then fixed. However, it indicates that perturbation phenomenon is still happened

near the maximum power point under stable weather conditions after doing some experiments. This is

due to the reason that the probability of meeting condition dI/dV =－I/V is extremely small.

V. ANALYSIS AND DISCUSSION OF RESULTS

In order to compare the accuracy and efficiency of the two MPPT algorithms selected in this paper,

modelsim software is used for simulation and FPGA for Implementation. In this project I have completed

the simulation work in Modelsim software and implementation work in FPGA by using QuartusII




software for both the algorithms i.e. Perturb and Observe algorithm and Incremental Conductance

algorithm. For different voltage and current reading following are the PWM output waveforms. For

different voltage and current the width of output PWM waveform is varies as shown in figure below.

Fig. 9 PWM Output Waveform for P&O algorithm

Fig. 10 PWM Output Waveform for INC algorithm

The output summary and comparison of FPGA Implementation for both P&O and INC algorithm is shown

on below figure.

As shown in figure 11 the comparison of device utilization summary for Perturb and Observe algorithm

and Incremental Conductance algorithm. It is found that in P&O algorithm the number of component

used is less as compared to the incremental conductance algorithm. It is nearly just one percent

components used in P&O algorithm.

As shown in figure 12 the comparison of power dissipation for both algorithms. Form this comparison it

is found that power dissipation is nearly equal for both algorithms i.e. the difference in between these

two algorithms is just 0.01.




Fig. 11 Device Utilization comparisons

Fig. 12 Power Dissipation Comparisons

As shown in figure 13 below the comparison of propagation delay for both algorithms. It is found that the

propagation delay is quite large in case of Perturb and Observe algorithm as comparison to Incremental

Conductance algorithm.

Fig. 13 Propagation Delay Comparisons

As shown in figure 14 the comparison of maximum power obtained for both algorithms. From

comparison it is found that in case of P&O algorithm we found maximum power as compared to INC

algorithm. As shown in figure 15 the comparison of average power for both algorithms. From this it is

found that we getting large average power in case of Perturb and Observe algorithm as compared to

Incremental Conductance algorithm.




Fig. 14 Maximum Power Obtained comparisons

Fig. 15 Average Power Comparisons

VI. CONCLUSION

In this paper we studied the detailed analysis and implementation on FPGA work for both Perturbed &

Observe algorithm and Incremental Conductance algorithm. After the simulation and implementation of

both algorithms it is found that P&O algorithm is best as compare to the INC algorithm. Device utilization

in FPGA for P&O algorithm is very less (1%) as compared to INC algorithm. Also average power for P&O

algorithm is high as compared to INC algorithm; all other result is very good as compared to INC

algorithm. So from this comparison we said that the P&O algorithm is best for implementation.

VII. REFERENCES

[1] Sihem Amara, Adel Bouallegue, Adel Khedher, “Theoretical and Practical Study of a Photovoltaic

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Perturb-and-Observe and Incremental Conductance MPPT Methods for PV Systems” IEEE

JOURNAL OF PHOTOVOLTAICS, VOL. 3, NO. 3, JULY 2013.

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AUTHORS PROFILE

Mr. Bhalchandra V. Chikate student M.Tech IVth Sem Electronics Engineering B.D.C.O.E

Sewagram, Wardha.




Prof. D. R. Dandekar Associate Professor in Electronics Engineering, P.G. Department

B.D.C.O.E Sewagram, Wardha.

Prof. Mrs. Y. A. Sadawarte Assistant Professor, M.Tech Coordinator in Electronics

Engineering, P.G. Department B.D.C.O.E Sewagram Wardha.

Documents

Comparison Of Perturb & Observe and Incremental Conductance Algorithms for MPPT Controller