A Comparison Between Temperature and CurrentSensing in Photovoltaic Maximum Power Point

Tracking

Daniel BurmesterSchool of Engineering and

Computer Science

Victoria University of

Wellington

PO Box 600 Wellington

New Zealand

Email: burmesdani@ecs.vuw.ac.nz

Dr Ramesh Rayudu, SMIEEESchool of Engineering and

Computer Science

Victoria University of

Wellington

PO Box 600 Wellington

New Zealand

Email: Ramesh.Rayudu@ecs.vuw.ac.nz

Prof Winston SeahSchool of Engineering and

Computer Science

Victoria University of

Wellington

PO Box 600 Wellington

New Zealand

Email: Winston.Seah@ecs.vuw.ac.nz

Abstract—Maximum power point tracking algorithms, forphotovoltaic modules, commonly use current and voltage sensingas a means of tracking the maximum power point (MPP). Thispaper experimentally compares the use of a temperature sensingin place of the current sensing for MPP tracking. It does soby implementing two commonly used current sensor algorithms(perturb and observe and incremental conductance) and temper-ature sensing algorithm (MPPT-temp [1]) to determine whichis faster and more accurate. The paper shows, the standarddeviation of the two current tracking algorithms is much greaterthan that of a temperature based algorithm. The temperaturesensor also reduces the complexity of circuitry and is a morecost effective solution to maximum power point tracking ofphotovoltaic modules.

I. INTRODUCTION

Since the 1970’s, scientists have grown increasingly aware

of global climate change and its environmental effects. These

effects have manifested as increased average global air and

ocean temperatures, widespread melting of snow and ice and

rising average global sea level [2].

This warming is caused by greenhouse gases in the atmo-

sphere, and although this ”greenhouse effect” occurs naturally,

human activities in recent times have increased this effect [3].

Greenhouse gasses consist of 16% methane (CH4), 6% nitrous

oxide (N2O), 2% F-gases and is dominated by carbon dioxide

(CO2), which accounts for 76% of these greenhouse gases [4].

A break-down of these emissions into the main sectors, reveals

the largest source of CO2 is electricity and heat Generation,

responsible for 41% in 2010 [5].

This is a repercussion of conventional electricity production

where power is generated by burning fossil fuels, producing

CO2 . Despite their negative effects on the environment, fossil

fuels are responsible for more than two-thirds of the total

global electricity production [6].

Encouraged by the Kyoto protocol 1, there has been a global

shift towards renewable energy in an attempt to reduce these

statistics [2].

Renewable energy can reduce greenhouse gasses, utilising

energy sources such as wind, water and sun to produce

electricity. This is a carbon neutral process, meaning no carbon

is produced in the conversion from energy source to electricity.

One such method of generating electricity is photovoltaic (PV)

modules, which convert energy from the sun into electricity.

As of 2012, the world’s cumulative PV capacity surpassed an

impressive 100 gigawatt (GW) installed electrical power mark.

Each year these PV installations save more than 53 million

tons of CO2 [7].

Although PV modules come with the advantage of zero carbon

emissions, and an abundant source (the sun), they also have

their disadvantages.

A PV is a semiconductor device that converts solar radiation to

electrical energy, but does so very inefficiently. The modules

power generation is heavily dependent on environmental con-

ditions such as ambient temperature and sun irradiance [8].

This makes the PV a non-linear, time-variant power source

with a current voltage (I-V) curve similar to that shown in

figure 1 [9].

Figure 1 shows how the PV’s I-V curve varies with changing

solar irradiance and ambient temperature. It reveals, for

a specific temperature and solar irradiance, there is only

one maximum power point (MPP). This maximum power

is located at the ’knee’ of the I-V curve. To maximise the

efficiency of a PV module, this point must be dynamically

tracked using a maximum power point tracker (MPPT).

1An international treaty that sets legal limits and reduction goals ongreenhouse gas emissions978-1-4799-5141-3/14/$31.00 c© 2014 IEEE

Fig. 1. I-V curve of PV module: Left) Under constant temperature. Right)Under constant irradiance

A. Goal and layout of this Paper

This paper will compare current sensing algorithms for

photovoltaic MPPT with a temperature sensing algorithm. It

will look at the speed and accuracy of the MPPT system using

each algorithm.

The paper layout will see section II discuss the functionality

of a maximum power point tracker before the experimental

procedure undertaken for this research is presented in III. The

results of the research are then discussed and concluded in

sections IV and V respectively.

II. MAXIMUM POWER POINT TRACKING

An MPPT consists of a DC/DC converter which forces

the PV to operate at its maximum power point, it does so

by presenting a variable load to the PV [10]. This load is

varied by altering the duty cycle of the pulse width modulation

controlling the switching elements of the DC/DC converter.

This duty cycle is in turn controlled by an algorithm that

uses information gathered by sensors to track the PV modules

MPP. Typically algorithms use current and voltage sensing

to track the PV’s maximum power point. Two examples of

this is the commonly used algorithms, ”perturb and observe”

and ”incremental conductance”. These rely on the ability to

calculate the PV’s output power, a brief description of each

algorithm follows:

A. Perturb and Observe (Hill-climbing)

The output power of the PV is measured (P1), then the

operating point of the PV module is shifted and re-measured

(P2). if P2 > P1 then the algorithm will continue to shift the

operating point in this direction. If not, it will reverse. The

main fault with this algorithm is that it oscillates around the

maximum power point rather than settling directly on it.

B. Incremental Conductance

This algorithm works on the basis that dP/dV = 0 at the

maximum power point. This is achieved by correcting the

operating point, whenever it falls to one side of the maximum

power point. Programming this algorithm is more complex

than Perturb and observe, however it has a faster response

and better accuracy.

The main problem with current sensing arises when you

observe the behavior of the input current to the DC/DC

converter. Figure 2 shows the characteristic current ripple of

the DC/DC converter. To convert this to a usable input for

the algorithm, additional circuitry is required increasing the

complexity and cost of the system.

Fig. 2. DC/DC inductor current

C. MPPT-temp

An alternative to current sensing is proposed in [1] and [9].

It is based on temperature and voltage sensing, eliminating the

need for a current sensor. The algorithm is based on equation

1 which shows the dependence of the maximum power point

on the ambient temperature of the PV module. This equation

takes the datasheet’s voltage at the maximum power point

Vmpp(TRef ) which is measured at a reference temperature

TRef . It then compares the PV’s actual temperature T to

TRef to find how far the temperature has deviated. Multiplying

this by µv , the temperature coefficient (∆V per degrees),

the new Vmpp is found. Figure 3 displays the flow diagram

of the MPPT-temp algorithm, showing how equation 1 is

implemented. It can be seen from figure 3 that the duty

cycle (D(n − 1)) is incremented by δD to give a new duty

cycle D(n). The variable δD takes the difference between the

measured voltage and the voltage at the MPP, then multiplies

this value by k which determine the incremental step size.

Safety checks are made to ensure the algorithm does not

exceed an upper or lower duty cycle limit before incrementing

the SEPIC.

Vmpp = Vmpp(TRef ) + (T − TRef )µv (1)

The disadvantage to this algorithm is the additional

information required from the PV’s datasheet (µv , Vmpp(TRef )

and TRef ), as this information is not always quoted.

D(n)>Dmax

Start

Measure I(t) and V(t)

Vmpp(T) = Vmpp(Tref) + µV(MPP)(T-Tref)

ΔD = [Vmodule-Vmpp(T)]k

D(n) = Dmax

D(n) = Dmin

Return

D(n)<Dmin Yes

D(n) = D(n-1)+ΔD

No

No

Fig. 3. Flow diagram of MPPT-temp

III. EXPERIMENTAL PROCEDURE

So an accurate comparison could be made between current

and temperature sensing, a controlled environment was re-

quired. This would ensure repeatability of conditions for each

system and enable calibration of the PV’s MPP. It was also

important to have multiple conditions so the systems behavior

during transitions could be observed. To achieve this, four

500W lights were used as shown in Figure 4. This gave the

option of either two or four lights being active at any given

time, allowing the simulation of ”full sun” or ”cloud cover”

conditions.

Fig. 4. Test Conditions

Once the controlled environment were implemented, a cal-

ibration of the PV was undertaken. This calibration was to

provide the maximum power output of the PV under each

condition. Figure 5 shows the I-V curve of the test PV and

figure 6 displays the output power as a function of output

voltage. With the MPP of the PV now being a known quantity,

the accuracy of the current and temperature sensing approach

to maximum power point tracking could be assessed.

0 5 10 15 200

0.2

0.4

0.6

0.8

1

1.2

1.4Current vs voltage of photovoltaic module

Voltage (V)C

urr

ent

(A)

Two lights on

Four lights on

Fig. 5. I-V curve of PV module

0 5 10 15 200

5

10

15

20Voltage vs power of photovoltaic module

Voltage (

V)

Power (W)

Two lights on

Four lights on

Fig. 6. Power vs Voltage Curve of PV Module

A. System hardware

A single ended primary inductor converter (SEPIC) was

used as the DC/DC converter of the MPPT. This was due

to its ability to produce and output voltage greater or less

than the input voltage and its non-inverted output. The

control mechanism chosen was an arduino(TM) uno, which

had the required input and output ability and the advantage

of a simple programming environment. The MPPT was then

connected to the 12V, 120W PV and a 10Ω load as shown in

figure 7.

Fig. 7. Block Diagram of Test Rig

1) Current Sensor: A Hall effect current sensor was

employed to sense the panels output current of 0A to 6A. The

output from the sensor was a linear voltage, with a change

of 10 mV/A. Due to the current ripple, a peak detector

circuit was implemented, and this signal was amplified before

analogue to digital conversion.

2) Temperature Sensor: The temperature sensor used was

an LM35 precision temperature sensor. This also gave a

linear output of 10 mV/oC. This output was also amplified to

meet the arduino’s analogue input of 0-5V.

IV. RESULTS

The comparison between the two approaches to maximum

power point tracking, current and temperature, was a measure

of speed and accuracy. The faster the system transitioned

between states, and finds the MPP, the greater the output

power. Likewise, a power increase was gained with increased

MPP tracking accuracy.

This led to the test procedure of switching between “full

sun” and “cloud cover” conditions and observing the systems

output.

Figures 8 and 9 show the plots of the algorithms under varying

conditions. At t = 0 all four lights are switched on, the

algorithms ascend to the maximum power point each reaching

the expected power of ≈ 16W . Two lights are switching off at

t ≈ 19s and the algorithms descend to ≈ 7W , at t ≈ 35s the

additional lights switched back on again and the algorithms

return to their previous state.

While testing, it became apparent that there were three

main regions where the algorithms behaved in a similar

fashion repeatedly. The first of these was the rise from zero

to the direct sunlight MPP.

0 5 10 15 20 25 30 35 40 45 500

10

20Perturb and observe algorithm

Pow

er

(W)

0 5 10 15 20 25 30 35 40 45 500

10

20MPPT−temp algorithm

Pow

er

(W)

0 5 10 15 20 25 30 35 40 45 500

10

20Incremental conductance algorithm

Seconds (s)

Pow

er

(W)

Fig. 8. Individual Responses of MPPT Algorithms

0 5 10 15 20 25 30 35 40 45 500

2

4

6

8

10

12

14

16

18

20Response of MPP algorithms

Seconds (s)

Pow

er

(W)

Perturb and Observe

MPPT−Temp

Incremental Conductance

Fig. 9. Response of MPPT Algorithms

A. Region one: Zero to the direct sunlight MPP

In real world applications region one would only occur

once a day, at sun rise, so is less important than the following

two regions. Another consideration is that early in the

morning, sun irradiance on the PV is low due to the suns

angle [11]. It is also expected that the sun will rise slower

than the algorithms can respond making region one of little

significance.

The incremental conductance algorithm was the slowest of

the three algorithms, deviating on its ascent. When analysis

was performed on the incremental conductance, a fault was

diagnosed. The fault was the algorithms reliance on the ∆I∆V

condition. As the current and voltage only changed a small

amount at the extreme ends of the PV’s I-V curve, ∆I∆V

often

threw “not a number” (NaN) or infinity (Inf). This would

result in the algorithm not shifting the PV’s state, which

caused the algorithm to get stuck at this point. Due to the use

of ∆I∆V

in the algorithm, and its coupling to future iterations

of the loop, amending the code was unachievable.

This displays one disadvantage to current sensing over

temperature sensing, however increasing the resolution of the

current and voltage sensor would some what remedy this

problem.

Despite the P and O being quoted as the slower of the three

algorithms, in this region it consistently had the fastest rise

time with the MPPT-temp a close second.

As one current sensing algorithm was faster, and one

slower than the temperature sensing algorithm, region one was

inconclusive.

B. Region two: On the MPP

Region two is a test of how closely the system remained

on the MPP. Neglecting the rise and fall regions of figure 9,

the standard deviation was taken. Table 1 shows the numerical

results which are plotted with the systems output and mean for

“full sun” and “cloud cover” in figures 10 and 11 respectively.

The results show the temperature sensing algorithm having

the smallest deviation from the MPP with the incremental

conductance a close second. The P and O algorithm had a

largest deviation which was more than twice that of the MPPT-

temp in both four and two light tests.

TABLE ISTANDARD DEVIATION OF ALGORITHMS

Algorithm Two Lights Four LightsP and O 1.48 0.66Incremental Conductance 0.46 0.39MPPT-temp 0.16 0.25

0 1 2 3 4 5 6 7

14

16

18

Pow

er

(W)

Mean and standard deviation of MPPT−temp

0 1 2 3 4 5 6 7

14

16

18

Pow

er

(W)

Mean and standard deviation of incremental conductance

0 1 2 3 4 5 6 7

14

16

18

Pow

er

(W)

Seconds (s)

Mean and standard deviation of P and O

Fig. 10. Algorithm response to “full sun” conditions, where the dotted lineis the standard deviation

A comparison of each algorithm’s mean power output is

plotted in figure 12. It demonstrates which sensing approach

0 1 2 3 4 5 6 7

5

10

Mean and standard deviation of MPPT−temp

Pow

er

(W)

0 1 2 3 4 5 6 7

5

10

Mean and standard deviation of incremental conductance

Pow

er

(W)

0 1 2 3 4 5 6 7

5

10

Mean and standard deviation of P and O

Seconds (s)

Pow

er

(W)

Fig. 11. Algorithm response to “cloud cover” conditions, where the dottedline is the standard deviation

can obtain the highest average power for each given input. This

shows the temperature sensing algorithm’s output power giv-

ing the highest amplitude, with the incremental conductance

second and P and O much lower.

All the algorithms, however, had a higher output power than

the reference tests for the direct sunlight condition. The MPPT-

temp and incremental conductance algorithms also achieved

this for the cloud cover condition. The P and O algorithm’s

mean value for cloud cover was less than the reference MPP,

due to the deviations being large.

The reason the algorithms achieved a higher output power than

the reference, was that discrete resistor values were used to

calibrate the PV module. This meant the MPP was between

the achievable loads, due to these discrete values. A variable

load could be used to test the PV modules output, however to

dissipate high power, the financial cost was the limiting factor.

It is expected that this region will occur regularly, for short

periods of time, on a cloudy day and for longer periods on

a sunny day. This makes it an important region for accuracy

and precision.

In region 2 the temperature sensing algorithm showed

an advantage over the current sensing algorithms with the

lowest standard deviation. The temperature sensor also had

the highest mean power output, making it the best performing

system of the three through this region.

C. Region three: Fall and rise from cloud cover to direct

sunlight

The third region is a better indication, than region one, of

the systems speed. The region will occur more regularly in a

day, as the rise from zero is only expected to occur once a

day. Figure 13 shows the rise and fall of the system between

“full sun” and “cloud cover”. It shows that the current and

temperature sensing algorithms descent is much the same.

However, the temperature sensing algorithm has the fastest

0 1 2 3 4 5 6 7

16

16.5

17Mean value of P and O vs MPPT−temp with four lights

Seconds (s)

Pow

er

(W)

MPPT−temp

Perturb and observe

Incremental conductance

0 1 2 3 4 5 6 74

6

8

10

Mean value of P and O vs MPPT−temp with two lights

Seconds (s)

Pow

er

(W)

MPPT−temp

Perturb and observe

Incremental conductance

Fig. 12. Mean of algorithms

ascent, followed by incremental conductance. The P and O

algorithm dips before it ascends which regularly occurred

when testing the algorithm. The time it took each algorithm

to rise from cloud cover to direct sunlight is listed in table II.

17 18 19 20 21 22

8

10

12

14

16

18

Decreased solar irradiance

Seconds (s)

Pow

er

(W)

32 34 36 38 40 42

8

10

12

14

16

18

Increased solar irradiance

Seconds (s)

Pow

er

(W)

Fig. 13. Transition between states

TABLE IIRISE TIMES FROM CLOUD COVER TO DIRECT SUNLIGHT

MPPT-temp Incremental conductance P and OTime 1.5s 2.75s 5.85s

In region 3 all three algorithms descended at the same

rate. However, the temperature sensing algorithm ascended

at a much faster rate. This meant, through this region, the

temperature sensing algorithm was again the superior of the

three.

V. CONCLUSIONS

This paper compares the use of a current sensor for maxi-

mum power point tracking with a temperature sensor. To do so,

two environmental conditions were simulated in a controlled

environment, “full sun” and “cloud cover”. This enabled the

switching between conditions so the system response could be

observed transitioning to, and remaining on the MPP.

The test was broken into three regions, rise from zero to

direct sunlight, On MPP and fall and rise from cloud cover

to direct sunlight. It was decided that region one was of little

significance and the remaining two regions would decide the

success of the algorithm.

In both cases the temperature sensor out performed the cur-

rent sensor, with faster tracking and better accuracy. It was

also noted the current sensor required additional circuitry so

complexity and cost was higher than that of the temperature

sensor.

VI. FUTURE WORK

The future work would see continued repeatability of the

algorithms so long term power increase could be quantified.

Also external testing to make sure the sensor and algorithm

can withstand dynamic environmental conditions.

VII. ACKNOWLEDGMENTS

The authors would like to acknowledge the NZIRI India

Studies Research Grant for its support. The first author would

also like to thank the Victoria University Doctoral Scholarships

and ECS technical staff member Tim Exley for his help.

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