Contribution to the optimization of energy withdrawn from a PV panel using an Embedded System

Centre d’Etudes Doctorales : Sciences et Techniques de l’Ingénieur
Soutenance de thèse
en vue de l’obtention du
Doctorat en Sciences et Techniques de l’Ingénieur
Spécialité
Génie électrique
Laboratoire
Laboratoire de Productique Energie et Développement Durable
Au sein de
Presentée et soutenue publiquement par
Mr. Saad Motahhir
Contribution to the optimization of energy withdrawn from a PV panel using an
Embedded System
Sous la direction de
Pr. Abdelaziz El Ghzizal
Pr. Aziz Derouich
31/03/2018

1
2
3
Context and challenges
Thesis contributions
Conclusions & perspectives
Plan
2
 Why Renewable energy ?
 PV Energy
 Problematic
 Low-cost Embedded system based control for PV system
 MIL/SIL/PIL tests for MPPT algorithm
 Improvement of INC algorithm for fast variation of irradiation and test by SIL method
 Design Reliable and robust PV system using Kalman filter through SIL method

Context and challenges Thesis contributions Conclusions & perspectives
3
Why Renewable energy ? PV Energy Problematic
Demand for
electricity will
double to 2060
Per capita energy
demand will peak
before 2030
The council said that
fossil energy will be
able to provide just 50
percent to 70 percent
of energy demand by
2060.
The phenomenal rise of
solar and wind energy
will continue

4
Why Renewable energy ? ProblematicPV Energy

5
High cost of solar panels
Consequences
Low operational cost (cost of maintenance)
Reducing future CO2 emissions
Inexhaustible resource
One load can absorb the maximum PV power

6
Problem
R
+
-
V
PVpanel

7
α
I
V
MPPT Controller
Converter
DC/DC
Load
 1
O
V
V


 1O
I I  
 
 
1 ²
1 ²O
in
O
VV
R R
I I



   MPPT find the optimum α to have Rin= Rmpp
Solution

8
Experimental study Low-cost Embedded system based control for PV system
Design Reliable and robust PV system using Kalman filter through SIL method
Review on the most used MPPT algorithms
Model PV panel on Proteus
Model PV panel on PSIM
Improvement of INC algorithm for fast variation of irradiation using SIL method
MIL/SIL/PIL tests for MPPT algorithm

MPPT
Direct
methods
Perturb &
Observe
Incremmental
conductance
Indirect
méthods
Fractional
Short-Circuit
Current
Fractional
Open-Circuit
Voltage
artificial
intelligence
méthods
Fuzzy Logic
Artificial
Neural
Network
9

10
Choice of MPPT algorithm
Efficiency
Steady-state
oscillations
Implementation
complexity
Tracking Speed
True MPPT
Cost

11
The most used MPPT algorithms
MPPT
Category
True MPPT
Steady-
state
oscillations
Efficiency
Tracking
Speed
Analog/digital
Implementati
on Complexity
Sensors Cost
Fractional Short Circuit
Current
Indirect [1] No [3] No [5] Low [1] Fast [2] Both [2] Simple [2] I [6] Cheap [2]
Fractional open circuit
voltage
Indirect [1] No [3] No [5] Low [1] Fast [2] Both [2] Simple [2] V [6] Cheap [2]
P&O and hill climbing
method
Direct [1] Yes [3] Yes [5]
Medium
[1]
Slow [2] Both [2] Simple [2]
I and V
[6]
Medium [2]
Incremental conductance
method
Direct [1] Yes [3]
Sometimes
[5]
Good [1]
Medium
[2]
Digital [2] Medium [2]
I and V
[6]
Expensive
[7]
Fuzzy logic
Soft
computing [1]
Yes [3] No [4]
Very good
[1]
Fast [2] Digital [2] Complex [2]
I and V
[6]
Very
Expensive
[2]
Neural network
Soft
computing [1]
Yes [3] No [4]
Very good
[1]
Fast [2] Digital [2] Complex [2] Varies [6]
Very
Expensive
[2]

12
Incremental conductance
∆𝑰
∆𝑽
≅ −
𝑰
𝑽
𝑽 𝑷𝑽 (𝑽)
𝑷 𝑷𝑽 (𝑾)
∆𝑰
∆𝑽
< −
𝑰
𝑽
∆𝑰
∆𝑽
> −
𝑰
𝑽
∆𝑰
∆𝑽
≅ −
𝑰
𝑽
𝑽 𝑷𝑽 (𝑽)
𝑷 𝑷𝑽 (𝑾)
∆𝑰
∆𝑽
< −
𝑰
𝑽
∆𝑰
∆𝑽
> −
𝑰
𝑽

13
Drawbacks of Incremental conductance
1. Its complexity to be implemented due to the.
mathematical division calculations used in its
construction.
2. Fixed step size.
3. Incorrect decision under sudden increase of
irradiation.

14
PV panel model on PSIM
Modeling the PV panel on PSIM
Modeling Iph
Id
I
Ish
+
-
VRsh
Rs
D
𝑰 = 𝑰 𝒑𝒉 − 𝑰 𝒐 𝒆𝒙𝒑
𝒒 𝑽 + 𝑹 𝒔 𝑰
𝒂𝑲𝑻𝑵 𝒔
− 𝟏 −
(𝑽 + 𝑹 𝒔 𝑰)
𝑹 𝒔𝒉
Where:
𝑰 𝒑𝒉 = 𝑰 𝒔𝒄 + 𝑲𝒊 𝑻 − 𝟐𝟗𝟖. 𝟏𝟓
𝑮
𝟏𝟎𝟎𝟎
𝑰 𝟎 =
𝑰 𝒔𝒄 + 𝑲𝒊(𝑻 − 𝟐𝟗𝟖. 𝟏𝟓)
𝐞𝐱𝐩
𝒒 𝑽 𝒐𝒄 + 𝑲 𝒗 𝑻 − 𝟐𝟗𝟖. 𝟏𝟓
𝒂𝑲𝑻𝑵 𝒔
− 𝟏
The I-V characteristic PV panel is represented by the following equations :
1
2
3

15
Results
I‐V and P‐V characteristics of model and experimental data.

16
Results
I‐V and P‐V curves for different values of irradiance

17
Results
I‐V and P‐V curves for different values of temperature

18
PV panel model on Proteus
Modeling the PV panel on Proteus

19
Measurement setup

20
Results
I-V and P-V characteristics for simulation and experimental data

21
Low-cost Embedded system based control for PV system
I I
V V

 

I I
V V

 

I I
V V

 

at MPP
left to MPP
right to MPP
   0)/()(  VVVIIV
   0)/()(  VVVIIV
   0)/()(  VVVIIV   0)(  VIIV
  0/)(  VVIIV
  0/)(  VVIIV
and 0V
0Vand
and
and
at MPP
left to MPP
left to MPP
right to MPP
right to MPP
  0)(  VIIV
0V
0V
  0)(  VIIV
  0)(  VIIV
  0)(  VIIV
  0)(  VIIV

22
Low-cost Embedded system based control for PV system on Proteus
Prix : 2 $

23
Simulationresults
INC
Mod INC
87 µs
0.27 s

24
Experimental results
INC
Mod INC

25
Comparison between our work and some experimental works published recently
Paper, Publication
year
PV Power at
STC
MPPT algorithm Controller used
Power
ripples
Efficiency
Response
time
Cost of
controller
[14], (2014) 80 W Modified INC Xilinx XC3S400 FPGA 2.7 W 98.8 % 2.5 ms 38.5 $
[15], (2014) 210 W
Adaptive P&O-
fuzzy MPPT
DSP TMS320F28335 1 W 95.2 % 20 ms 21.17 $
[16], (2014) 40 W
TS fuzzy-based
INC
Embedded controller
dsPIC33fJ128MC802
1W 97.5 % 2 s 4.46 $
[17], (2015) 87 W Modified INC
Microcontroller
PIC18f4520
1.3 W 99 % 0.275 s 4.26 $
[18], (2017) 10 W
FLC
MPPT
dSPACE-1103 0.9 W 97.295 % 0.264 s 38 $
[18], (2017) 10 W
Improved
INC
dSPACE-1103 1 W 91.93 % 0.254 s 38 $
Our work 20 W Modified INC Atmega 328 0.5 W 98.5% 0.1 s 2 $

26
0
P
Offset Ofsset
V

 

Modified INC algorithm (Variable step size)

27
At voltage source region, ΔV is very low; as a result the
ΔP/ΔV steps will be large.
At current source region:
dP dI
I V
dV dV
 
SC
dP
I
dV

High steady-state power oscillations
dP/dV is unable to achieve adaptive stepping

28
To overcome this problem, a modified variable step-size is used, which depends only on (ΔP).
1Offset Ofsset P  

29
Model in the loop/Software in the loop/Processor in the loop tests for embedded system

30
Result of Model in the loop test

31
Result of Software in the loop test
C code

32
Result of Processor in the loop test

33
Comparison between the proposed work and some existing works in the area of PV
Reference,
Publication
year
Variable step Controller
used
Power
ripples
Response
time
Efficiency
[11], (2014) Choose between ΔD1
and ΔD2
Xilinx
XC3S400
FPGA
2.7 W 2.5 ms 98.8 %
[12], (2015) Step=N* abs (ΔP/ΔV) PIC18F4520 2 W 0.4 s 97.97 %
[13], (2015) Step=N* abs (ΔP/(ΔV- ΔI)) dsPIC30F4011 2 W 0.5 s 98 %
Our Work Offset=Offset1* abs (ΔP) STM32F407VG neglected 0.02 s 98.8%

34
INC algorithm (Incorrect decision under sudden increase of irradiation)
Improvement of INC algorithm for fast variation of irradiation

35
Modified INC algorithm

36
Software-in-the-loop test for the Mod INC using PSIM software
Software-in-the-loop test for the Mod INC using PSIM software

37
Results & comparison
P&O INC
Mod INC

38
Results & comparison
Work Oscillations
level
Efficiency Response time during
sudden increase in
irradiation
Incorrect decision
under sudden increase
of irradiation
Conventional 2.5 W 96 % Slow Yes
[8], 2016 1 W 96.40 % Fast No
[9], 2013 1.5 W 98.5 % Fast Yes
[10], 2014 1 W 97.5 % Medium Yes
Our work Neglected 98.8 % Very fast No

39
Kalman filter
Design the MPPT algorithm using Kalman filter
Important and used everywhere: GPS (predict update location), machine vision (track targets) ,radar and more.

40
Kalman filter
 Q : Process noise
 𝑥 𝑘
−
: State estimate at k given by
former iterations.
 𝑥 𝑘−1: State estimate at k-1 given
by measurement output.
 𝑃𝑘
−
: Priori error covariance.
 𝑃𝑘−1: Posteriori error covariance.
 A & B & H : Constants.
 R : Measurement noise
covariance.
 𝐾𝑘: Kalman gain.
 𝑢 𝑘−1: Input.

41
Kalman filter based MPPT
1
1 1
k
k k k
P
V V M
V

 

 

The prediction state:
1k kP P Q 
 
1
k k kK P P R

 
 ,k k k in k kV V K V V   
 1k k kP K P  
The measurement update

42
Software-in-the-loop test for the Kalman filter based MPPT using PSIM software
Software-in-the-loop test for the Kalman filter based MPPT using PSIM software

43
Results
Results under stable weather condition
INC Kalman

44
Results
Results under variable solar irradiation
200W/m²
800 W/m²
1000
W/m²
1000
W/m²
500 W/m²
800 W/m²
200W/m²
1000 W/m²
800 W/m²
500 W/m²
800 W/m²
1000 W/m²
INC Kalman

45
Results comparison
 Results comparison between the proposed method and INC algorithm
Irradiance (W/m2)
Kalman filter based MPPT INC algorithm
Response time
(ms)
Efficiency (%) Oscillations (W)
Response time
(ms)
Efficiency (%)
Oscillatio
ns (W)
1000 5 99.38 0.8 30 96.64 3
500 4 99.25 0.4 24 96.72 1.6
800 5 99.23 0.7 29 96.62 2.7

46
Results comparison
MPPT
Category
True MPPT
Steady-
state
oscillations
Efficiency
Tracking
Speed
Analog/digital
Implementation
Complexity
Sensors Cost
Fuzzy logic
Soft
computing [1]
Yes [3] No [4]
Very good
[1]
I and V
[6]
Very
Expensive
[2]
Neural network
Soft
computing [1]
Yes [3] No [4]
Very good
[1]
Varies
[6]
Very
Expensive
[2]
Kalman basaed MPPT Direct [1] Yes [3] No
Very Good
[1]
Very fast Digital [2] Medium [2] I and V Expensive

47
Low-cost Embedded system based
control for PV system
MIL/SIL/PIL test for
MPPT algorithm
Reliable and robust PV
system using Kalman filter
Conclusion
47
1
2
3
4
PV panel Model on Proteus

 Propose a flexible PV panel model on Proteus.
 Design an improved MPPT method for tracking the MPP under partial
shading condition.
 Extended Kalman filter based MPPT.
48
Suggestions for Future Work

49
Journal Publications
1. Motahhir, S., Hammoumi, A. E., Ghzizal, A. E., & Derouich, A. (2019). Open hardware/software test bench for
solar tracker with virtual instrumentation. Sustainable Energy Technologies and Assessments (Elsevier), 31, 9-
16.
2. Motahhir, S., Chalh, A., El Ghzizal, A., & Derouich, A. (2018). Development of a Low-cost PV System using an
improved INC algorithm and a PV panel Proteus model. Journal of cleaner Production (Elsevier), 204, 355-365.
3. Motahhir, S., El Hammoumi, A., & El Ghzizal, A. (2018). Photovoltaic system with quantitative comparative
between an improved MPPT and existing INC and P&O methods under fast varying of solar irradiation. Energy
Reports (Elsevier), 4, 341-350.
4. El Ouanjli, N., Motahhir, S., Derouich, A., El Ghzizal, A., Chebabhi, A., & Taoussi, M. (2019). Improved DTC
strategy of doubly fed induction motor using fuzzy logic controller. Energy Reports, 5, 271-279.
5. EL Hammoumi A., Motahhir S., EL Ghzizal A., Chalh A., Derouich A. (2018). A simple and low-cost active dual-axis
solar tracker. Energy Science & Engineering (Wiley). 6(5), 607-620.
6. Chalh, A., Motahhir, S., El Hammoumi, A., El Ghzizal, A., & Derouich, A. (2018). Study of a Low-Cost PV Emulator
for Testing MPPT Algorithm under Fast Irradiation and Temperature Change. Technology and Economics of
Smart Grids and Sustainable Energy (Springer Nature), 3(1), 11.
7. Motahhir, S., Aoune, A., El Ghzizal, A., Sebti, S., & Derouich, A. (2017). Comparison between Kalman filter and
incremental conductance algorithm for optimizing photovoltaic energy. Renewables: Wind, Water, and Solar
(Springer Nature), 4(1), 8.

50
Journal Publications
8. El Hammoumi, A., Motahhir, S., Chalh, A., El Ghzizal, A., & Derouich, A. (2018). Low-cost virtual instrumentation of
PV panel characteristics using Excel and Arduino in comparison with traditional instrumentation. Renewables:
Wind, Water, and Solar (Springer Nature), 5(1), 3.
9. Motahhir, S., El Ghzizal, A., Sebti, S., & Derouich, A. (2017). MIL and SIL and PIL tests for MPPT
algorithm. Cogent Engineering (Taylor and Francis), 4(1), 1378475.
10.Motahhir, S., El Ghzizal, A., Sebti, S., & Derouich, A. (2018). Modeling of photovoltaic system with modified
incremental conductance algorithm for fast changes of irradiance. International Journal of Photoenergy
(Hidawi), 2018.
11.Motahhir, S., Chalh, A., Ghzizal, A., Sebti, S., & Derouich, A. (2017). Modeling of photovoltaic panel by using
proteus. Journal of Engineering Science and Technology Review, 10, 8-13.
12.Motahhir, S., El Ghzizal, A., Sebti, S., & Derouich, A. (2016). Shading effect to energy withdrawn from the
photovoltaic panel and implementation of DMPPT using C language. International Review of Automatic Control
(Prize), 9(2), 88-94.

51
Conferences
Motahhir, S., El Ghzizal, A., Sebti, S., & Derouich, A. (2015). Proposal and Implementation of a novel perturb and
observe algorithm using embedded software. 3rd International Renewable and Sustainable Energy Conference
(IRSEC), (pp. 1-5). IEEE.
Aoune, A., Motahhir, S., El Ghzizal, A., Sebti, S., & Derouich, A. (2016). Determination of the maximum power point in
a photovoltaic panel using Kalman Filter on the environment PSIM. International Conference on Information
Technology for Organizations Development, (pp. 1-4). IEEE.
Chalh, A., Motahhir, S., EL Hammoumi, A., El Ghzizal, A. and Derouich A. (2017). A low-cost PV Emulator for testing
MPPT algorithm, The International Conference on Renewable Energy and Energy Efficiency.
Motahhir, S., El Ghzizal, A., Sebti, S., & Derouich, A. (2015, November). Une ressource pédagogique pour
l'enseignement par simulation: cas des panneaux photovoltaïques. International Workshop on Pedagogic Approaches
& E-Learning.
Motahhir, S., El Ghzizal, A., & Derouich, A. (2015, May). Modélisation et commande d'un panneau photovoltaïque
dans l'environnement PSIM. Congrès International de Génie Industriel et Management des Systèmes.
IEEE Conference Publications
International Conferences

53
References
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[3] El-Khozondar, H. J., El-Khozondar, R. J., Matter, K., & Suntio, T. (2016). A review study of photovoltaic array maximum
power tracking algorithms. Renewables: Wind, Water, and Solar, 3(1), 3.
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performance of different MPPT techniques used for solar PV system under various operating conditions. Renewable and
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methods for solar PV system. Solar Energy, 136, 236-253.
[6] Bendib, B., Belmili, H., & Krim, F. (2015). A survey of the most used MPPT methods: Conventional and advanced
algorithms applied for photovoltaic systems. Renewable and Sustainable Energy Reviews, 45, 637-648.
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[14] Faraji, R., Rouholamini, A., Naji, H. R., Fadaeinedjad, R., & Chavoshian, M. R. (2014). FPGA-based real time incremental
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[15] Zainuri, M. A. A. M., Radzi, M. A. M., Soh, A. C., & Rahim, N. A. (2013). Development of adaptive perturb and observe-
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[16] Sekhar, P. C., & Mishra, S. (2014). Takagi–Sugeno fuzzy-based incremental conductance algorithm for maximum power
point tracking of a photovoltaic generating system. IET Renewable Power Generation, 8(8), 900-914.
[17] Soon, T. K., & Mekhilef, S. (2015). A fast-converging MPPT technique for photovoltaic system under fast-varying solar
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[18] Boukenoui, R., Ghanes, M., Barbot, J. P., Bradai, R., Mellit, A., & Salhi, H. (2017). Experimental assessment of Maximum
Power Point Tracking methods for photovoltaic systems. Energy, 132, 324-340.

Centre d’Etudes Doctorales : Sciences et Techniques de l’Ingénieur
Soutenance de thèse
en vue de l’obtention du
Doctorat en Sciences et Techniques de l’Ingénieur
Spécialité
Génie électrique
Laboratoire
Laboratoire de Productique Energie et Développement Durable
Au sein de
Presentée et soutenue publiquement par
Mr. Saad Motahhir
Contribution à l’optimisation de l’énergie soutirée des panneaux photovoltaïques
par un système embarqué
Sous la direction de
Pr. Abdelaziz El Ghzizal
Pr. Aziz Derouich
31/03/2018

56
Remerciments
 Aux personnes
 Aux instituts

57
Please cite this work as:
Motahhir, S.(2018). Contribution to the optimization of energy withdrawn
from a PV panel using an Embedded System. (doctoral dissertation). Sidi
mohammed ben abdellah University, Fez, Morocco.
For more papers and works please visit :
https://www.researchgate.net/profile/Saad_Motahhir

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