Proceedings of the 1st International Conference on Trends in Renewable Energy

Recent Innovations in Electrical, Electronics and OPEN ACCESSISSN:2376-2144

Communication Systems (RIEECS 2017) futureenergysp.com/index.php/tre

Design of Solar System by Implementing ALO Optimized

PID Based MPPT Controller
Raj Kumar Sahu* and Binod Shaw

Department of Electrical Engineering, NIT, Raipur, C.G, India.

Received January 5, 2018; Accepted March 15, 2018; Published April 26, 2018

This paper is a strive approach to design offgrid solar system in

association with DC-DC boost converter and MPPT. The tuned PID
based MPPT technique is adopted to extract maximum power from the
solar system under certain circumstances (temperature and irradiance).
The design parameters of PID controller play an imperative aspect to
enhance the performance of the system. Ant lion Optimizer (ALO)
algorithm is adopted to optimize PID parameters to contribute relevant
duty cycle for DC-DC boost converter to maximize output power and
voltage. P and O based MPPT technique is implemented to validate the
supremacy of PID based MPPT to enhance the response of the system.
In this paper, the proposed ALO optimized PID controller based MPPT
technique is performed better over conventional P & O technique by
conceding the oscillation, time response, settling time and maximum
values of voltage, current and power of the solar system.

Keywords: Photovoltaic system (PV); Maximum Power Point Tracking (MPPT); Perturb and Observe (P
& O); Proportional-Integral-Derivative (PID) controller; Ant lion Optimizer (ALO) algorithm

INTRODUCTION

In the present scenario, renewable energy source plays a significant approach to

meet the fast-growing load demand. Solar is an imperative concern among renewable
energy due to its noise free, eco-friendly, and easy maintenance with impressive life span.
Solar power is nonlinear and tough to guess. Solar energy falling on solar photovoltaic
(PV) system can precisely disciple into electrical energy. Irradiation and temperature
enormously influence the voltage and current which make them nonlinear. PV systems
need to minimize cost, reduce the size and increase the efficiency. The maximum power
point (MPP) is the extraction of power from solar cell under specific circumstances. Load
current primarily relies upon radiation, ambient temperature, and cell temperature.
Maximum Power Point Tracking (MPPT) is the process to track the maximum power by
optimizing the load resistance properly in any environmental condition.

LITERATURE SURVEY

Many researchers have implemented various techniques to enhance the efficiency

of the solar cell by enhancing the MPPT techniques during last few decades. Esram and
Chapman [1] have contributed 19 different MPPT techniques and provided a fair

*Corresponding author: shashikantkaushaley20@gmail.com 44

Tr Ren Energy, 2018, Vol.4, No.3, 44-55. doi: 10.17737/tre.2018.4.3.0049
Peer-Reviewed Article Trends in Renewable Energy, 4

interpretation for researchers to adopt relevant techniques. The P & O of variable step
size is proposed by Al-Diab and Sourkounis [2], and the step size is tuned automatically
and compared with the conventional method. Ishaque and Salam [3] have contributed a
brief literature to the MPPT design by adopting soft computing methods during partial
shading. The variable CS MPPT algorithm is validated by comparing with conventional P
& O and PSO MPPT algorithm in three distinct case studies and is described in [4]. The
efficiency of the partial shading PV module is enhanced up to 32% by using the current
of non-shaded module in [5]. The fuzzy logic controller based MPPT algorithm with 8-bit
microcontroller is compared with conventional P & O MPPT algorithm in [6]. Neural
network based MPPT is implemented in 230-watt PV system in [7]. A brief literature
survey on MPPT design is described beautifully in [8] and [9]. The P & O algorithm is
optimized to enhance the efficiency of the MPPT technique in [10]. Adaptive Fuzzy-PI
controller is implemented as MPPT and the role of climate change on PV module is well
established in [10] and [11], respectively. Application of improved optimization
techniques such as Adaptive Symbiotic Organism Search (ASOS) and Modified Group
Hunting Search (MGHS) are validated in power system to tune controller parameters [12,
13]. Various soft computing techniques and optimization techniques are adopted to
enhance the performance of MPPT of PV module in [14-19].
In this paper, Ant lion Optimizer (ALO) algorithm [20] optimized PID controller
based MPPT technique is strived to validate over P & O technique to enhance the power
and voltage of the system by contributing gate pulse of DC-DC boost converter. The
proposed experiment is executed in MATLAB/SIMULINK environment.

Figure 1. Simulink model of PID based solar system

Tr Ren Energy, 2018, Vol.4, No.3, 44-55. doi: 10.17737/tre.2018.4.3.0049 45

Peer-Reviewed Article Trends in Renewable Energy, 4

SYSTEM INVESTIGATED

The Simulink model of PV module with PID based MPPT controller is portrayed
in Figure 1. The proposed isolated solar system is portrayed in Figure 2, basically
consisting of PV array, DC-DC Boost converter and MPPT controller. MPPT controller
regulates gate pulse of boost converter by conceding the voltage and current of the PV
module. The regulated pulse of the converter enhances the efficiency of the solar system.

Irradiance Vpv

PV DC-DC
LOAD
Module Converter
Temperature Ipv

MPPT
Controller

Figure 2. Block diagram of solar system

PV Module

Figure 3. Equivalent circuit of solar cell

PV cells are associated in series and parallel to enhance the voltage and current.
The equivalent circuit is portrayed in Figure 3.
The equivalent solar system may be explained through equations (1)-(4).
I rr = I scr e( qVoc / KNs ATrk )−1 (1)
3 [( Eg K / KA)(1/ Trk −1/ Tak )]
I d = I rr (Tak / Trk ) e
(2)
I PH = I scr + ( K i (Tak − Trk ))S / 1000
(3)
( q / N s AKTak )(Vo + I o Rs )]
I o = N p I PH − N p I d {e − 1}
(4)
Where Io = PV module current
Vo =PV module voltage
Trk = Reference temperature in Kelvin
Tak = Operating temperature in Kelvin

Tr Ren Energy, 2018, Vol.4, No.3, 44-55. doi: 10.17737/tre.2018.4.3.0049 46

Peer-Reviewed Article Trends in Renewable Energy, 4

S = Irradiance W/m2
q = Charge of electron, 1.6×10-19 C
A = Ideality factor, 1.3
K = Boltzman constant
Eg = Band Gap
Iscr = S.C current
Ns = Cells connected in series
Np = Cells connected in parallel
Ki = S.C temperature co-efficient
Rs = Series Resistance
IPh = Light generated current
Irr = Reverse Saturation current

DC-DC boost converter

The basic purpose of design of boost converter is to boost the output voltage of
the dc system. The output of the converter is enormously influenced by the switching
frequency (gate pulse). Figure 4 represents the boost converter and the output of the
converter may be characterized in equation (5)
1
Vout = Vin
1− D (5)
D is the duty cycle of the converter and is characterized in equation (6).
ton
D=
ton + toff
(6)
On time and off time of the switch are expressed in equations (7) and (8), respectively, by
conceding switching period (Ts).
ton = DTs
(7)
toff = (1 − D)Ts
(8)

Figure 4. Boost converter

Tr Ren Energy, 2018, Vol.4, No.3, 44-55. doi: 10.17737/tre.2018.4.3.0049 47

Peer-Reviewed Article Trends in Renewable Energy, 4

The parameters of boost converter are tabulated in Table 1.

Table 1. Boost converter parameters

Model Components Parameters
Inductance, L 1 µH
Capacitance, C 3000 µF
Load, R 24 Ω
DC voltage, Vdc 12 V
Switching frequency 10000 Hz

PID based MPPT controller

The primary purpose of MPPT technique is to track the maximum power from the
PV module by concerning the array voltage and power. In this paper, reference voltage
(Vref) is developed by correlating the instant power (Pk) and previous power (Pk-1) as
portrayed in Figure 5.
The error signal achieved by comparing reference voltage with output voltage of
boost converter is fed to the PID controller. The output of PID controller is used as gate
pulse of the switch to enhance the power of the solar cell. The structure of PID controller
is illustrated in Figure 6 and can be expressed as in equation (9).
Start

Measure V(k) and I(k)

P(k)=V(k)*I(k)
dP=P(k)-P(k-1)

no yes
dP  0

V(k)  V(k − 1)
V(k)  V(k − 1)

yes no no yes
Decrease Increase Decrease Increase
Module Module Module Module
Voltage Voltage Voltage Voltage

Update Histo0ry
V(k-1)=V(k)
I(k-1)=I(k)

Figure 5. Vref calculation algorithm

Tr Ren Energy, 2018, Vol.4, No.3, 44-55. doi: 10.17737/tre.2018.4.3.0049 48

Peer-Reviewed Article Trends in Renewable Energy, 4

KP
+ U (t )
e (t ) +
KI
+

d
KD
dt
.
Figure 6. PID controller structure
t
d
u (t ) = K p .e(t ) + K i  e(t ).dt + K d e(t ) (9)
0
dt

ANT LION OPTIMIZER ALGORITHM

The affiliation among predator (ant lion) and prey (ant) is intelligently portrayed
as optimization technique by S. Mirjalili [21]. ALO algorithm is derivational from the
planning of hawking of ant as food by ant lion to sustain and become capable. Ant lion
creates reversed pyramid trap for the randomly moving ants to be captured into. Ant lion
downtime in the ground of the soil constructs hole to trap ant or other bugs. Ants move
randomly for searching food and sleep into the hole due to the pointed edge and loose
sand of the hole. Here and there preys try to protect out from the opening however ant
lion impels sands to the edge of the gap to make the prey slip into its jaw. The extent of
opening is specifically relying on the starvation of antlion. The upgrade of the span of
opening improves the likelihood to get nourishment. The steps followed for ALO
algorithm is described as
1. The component of the framework which holds the places of preys is introduced
arbitrarily with estimate [M Pr ey ] NPD .

So introduction of ant lion position grid is resolved arbitrarily with same size [ M Antlion ]NPD ,
where NP and D are the population and measurement of plan factors, respectively. For
this issue irregular in statement is in the middle of 0 to 2.
Useful estimations of the ant lion and prey are dictated by
FPrey = f (M Prey )
FAntlion = f ( M Antlion )
Where FPrey is a variety of wellness estimations of arbitrarily introduced MPrey
and FAntlion is the variety of wellness estimations of MAntlion.
2. The antlion with the best fitness is allocated as the best.
3. Roulette wheel is utilized to choose antlions which give higher likelihood of fitting ant
lions to chase preys.

Tr Ren Energy, 2018, Vol.4, No.3, 44-55. doi: 10.17737/tre.2018.4.3.0049 49

Peer-Reviewed Article Trends in Renewable Energy, 4

g g
4. The base and greatest vector of ith factors ci and di individually are modified as in
equation (10) and (11) respectively.
cig = ALig + c g (10)
dig = ALig + d g (11)
g
Where ALi is the position of ith antlion at gthgeneration. cg and dg might be
described as
cg dg g
cg = dg = I = 10w
I , I , and n

Where w is a round number chosen between 2 to 6 based on new generation.

5. The activities of preys are random in nature and may be shown in equation (12).
X(g)=[0,cumsum(2r(g1)-1),cumsum(2r(g2)-1),...,cumsum(2r(gn)-1) (12)
Where cumulative sum is found by cumsum. g and n are the generation and peak
generation number respectively. r is a random probability distribution function
described in equation (13).
1 if rand  0.5
r( g) = 
0 if rand  0.5 (13)
6. The status of preys is modified by equation (14).
RPg + REg
Pi g =
2 (14)
g g
Where RP and RE are the strange changes around antlion and best respectively.
7. The functional values of preys are determined.
8. The antlion is updated by its analogous fitter prey as described in equation (15).
ALig = Pi g if f ( Pi g )  f ( ALig ) (15)
9. The practical value of preys is determined as explain previously.
10. Elite value is updated by the antlion with fitness value better than elite.
11. Steps from 4 to 10 are repeated until maximum generation reached.
In the present work, the gains of PID controllers are tuned by ALO algorithm to
enhance the power of the PV module.

RESULTS AND DISCUSSION

ALO algorithm is executed for 50 iterations with 50 populations to resolve the

steps to discover the optimal gain parameters of PID controllers. The objective of the
algorithm is to hunt the parameters within a specified limit as described in equation (16).

Tr Ren Energy, 2018, Vol.4, No.3, 44-55. doi: 10.17737/tre.2018.4.3.0049 50

Peer-Reviewed Article Trends in Renewable Energy, 4

0.001  K p , K I and K D  2
(16)
The optimal values of Kp, KI, and KD are 0.0711, 0.9079 and 1.1260, respectively. The
performance of PV module by conceding power, voltage and current are portrayed in
Figure 7, Figure 8 and Figure 9, respectively.

Figure 7. Power vs Time graph

Figure 8. Voltage vs Time graph

Tr Ren Energy, 2018, Vol.4, No.3, 44-55. doi: 10.17737/tre.2018.4.3.0049 51

Peer-Reviewed Article Trends in Renewable Energy, 4

Figure 9. Current vs Time graph

The performance parameters of the output of PV module are tabulated in Table 2
to provide a fair supremacy of PID based MPPT controller over P & O technique.

Table 2. Performance response of output

Performance P&O PID
Parameters based MPPT based MPPT
P V I P V I
Maximum 30.55 12.385 2.477 48.02 15.465 3.094
Overshoot 2.94 0.56 0.12 0 0 0
Settling time 0.072 0.075 0.06 0.018 0.017 0.017
Rise time 0.032 0.0307 0.038 0.017 0.016 0.016
Delay time 0.019 0.012 0.013 0.012 0.011 0.010
Oscillation 2.432 0.492 0.098 0.061 0.012 0.002

Figure 10. I-V and P-V characteristics of PV module

Tr Ren Energy, 2018, Vol.4, No.3, 44-55. doi: 10.17737/tre.2018.4.3.0049 52

Peer-Reviewed Article Trends in Renewable Energy, 4

The I-V and P-V characteristics are illustrated in Figure 10. The settling time, rise
time, delay time and oscillation of output responses (power, voltage and current) of PV
module with ALO optimized PID based MPPT controller are lower than the P & O based
MPPT technique. Overshoot evaluated by considering the difference between steady state
maximum power and maximum power. Implementation of proposed MPPT technique
enhances the responses of PV module remarkably. Finally, ALO optimized PID based
MPPT technique is validated as a better technique over P & O based MPPT technique to
enhance the efficiency of the PV module.

CONCLUSION

The purpose of this paper is to design a solar system and to enhance the efficiency
of the system by implementing PID based MPPT technique. The PID based MPPT
controller optimized by ALO algorithm is validated as an improved controller over P & O
based MPPT controller of the solar system. Temperature, irradiance and load are the
imperative factors which influence the current, voltage and power of the solar cell. This
PID and P & O based MPPT controller is executed by diverging the irradiance (1000
W/m2 to 700 W/m2) and with constant temperature (45ºC) and load (24 Ω). The error
signal is evaluated by the contrast of reference voltage and measured process voltage to
achieve relevant gate pulse of the converter. The output voltage is enormously influenced
by duty cycle of the gate pulse. The ALO optimized PID based MPPT techniques is
validated over P & O technique to achieve maximum power, current and voltage with
minimum oscillation, settling time, rise time and delay time.

CONFLICTS OF INTEREST

The authors declare that there is no conflict of interests regarding the publication
of this paper.

REFERENCES

[1] T. Esram and P. L. Chapman, “Comparison of Photovoltaic Array Maximum Power

Point Tracking Techniques,” IEEE Trans. Energy Convers., vol. 22, no. 2, pp. 439–
449, 2007.
[2] A. Al-Diab and C. Sourkounis, “Variable step size P&O MPPT algorithm for PV
systems,” Proc. Int. Conf. Optim. Electr. Electron. Equipment, OPTIM, pp. 1097–
1102, 2010. DOI: 10.1109/OPTIM.2010.5510441
[3] K. Ishaque and Z. Salam, “A review of maximum power point tracking techniques of
PV system for uniform insolation and partial shading condition,” Renew. Sustain.
Energy Rev., vol. 19, pp. 475–488, 2013.
[4] J. Ahmed and Z. Salam, “A Maximum Power Point Tracking (MPPT) for PV system
using Cuckoo Search with partial shading capability,” Appl. Energy, vol. 119, pp.
118–130, 2014.

Tr Ren Energy, 2018, Vol.4, No.3, 44-55. doi: 10.17737/tre.2018.4.3.0049 53

Peer-Reviewed Article Trends in Renewable Energy, 4

[5] M. Z. Ramli and Z. Salam, “A simple energy recovery scheme to harvest the energy
from shaded photovoltaic modules during partial shading,” IEEE Trans. Power
Electron., vol. 29, no. 12, pp. 6458–6471, 2014.
[6] Y. R. Yang, “A Fuzzy Logic Controller for Maximum Power Point Tracking with 8-
Bit Microcontroller,” In: Proc., IECON 2010 - 36th Annual Conference on IEEE
Industrial Electronics Society, pp: 2895-2900, 2010. DOI:
10.1109/iecon.2010.5675084.
[7] Y.H. Liu, C.L. Liu, J.W. Huang, and J.H. Chen, “Neural-network-based maximum
power point tracking methods for photovoltaic systems operating under fast changing
environments,” Sol. Energy, vol. 89, pp. 42–53, 2013.
[8] N. A. Kamarzaman and C. W. Tan, “A comprehensive review of maximum power
point tracking algorithms for photovoltaic systems,” Renew. Sustain. Energy Rev.,
vol. 37, pp. 585–598, 2014.
[9] A. Reza, M. Hassan, and S. Jamasb, “Classification and comparison of maximum
power point tracking techniques for photovoltaic system: A review,” Renew. Sustain.
Energy Rev., vol. 19, pp. 433–443, 2013.
[10] N. Femia, G. Petrone, G. Spagnuolo, and M. Vitelli, “Optimization of Perturb and
Observe Maximum Power Point Tracking Method,” IEEE Transactions on Power
Electronics, vol. 20, no. 4, pp. 963–973, 2005.
[11] S. Hoffmann and M. Koehl, “Investigation on the Impact of Macro- and Micro-
Climate on the Potential Induced Degradation,” In: Proc., 2013 IEEE 39th
Photovoltaic Specialists Conference (PVSC), pp: 1822-1825, 2013. DOI:
10.1109/pvsc.2013.6744496
[12] J. R. Nayak, B. Shaw, and B. K. Sahu, “Application of adaptive-SOS (ASOS)
algorithm based interval type-2 fuzzy-PID controller with derivative filter for
automatic generation control of an interconnected power system”, Int. J. Engineering
Science and Technology, 2018. DOI: 10.1016/j.jestch.2018.03.010.
[13] J.R. Nayak, B. Shaw, S. Das, and B.K. Sahu, Design of MI fuzzy PID controller
optimized by Modified Group Hunting Search algorithm for interconnected power
system, Microsyst. Technol., 2018. DOI: 10.1007/s00542-018-3788-3.
[14] H. Shareef, A. H. Mutlag, and A. Mohamed, “A novel approach for fuzzy logic PV
inverter controller optimization using lightning search algorithm,” Neuro computing,
vol. 168, pp. 435–453, 2015.
[15] P.C. Cheng, B.R. Peng, Y.-H. Liu, Y.S. Cheng, and J.W. Huang, “Optimization of a
Fuzzy-Logic-Control-Based MPPT Algorithm Using the Particle Swarm
Optimization Technique,” Energies, vol. 8, no. 6, pp. 5338–5360, 2015.
[16] A. S. Oshaba, E. S. Ali, and S. M. AbdElazim, “PI controller design for MPPT of
photovoltaic system supplying SRM via BAT search algorithm,” Neural Comput.
Appl., vol. 28, no. 4, pp. 651–667, 2017.
[17] R. Nagarajan, R. Yuvaraj, V. Hemalatha, S. Logapriya, A. Mekala, and S. Priyanga,
“Implementation of PV - Based Boost Converter Using PI Controller with PSO
Algorithm,” Int. J. Eng. Comput. Sci., vol. 6, no. 3, pp. 20479-20484, 2017. DOI:
10.18535/ijecs/v6i3.14
[18] L. ZAGHBA, N. TERKI, A. BORNI, A. BOUCHAKOUR “Robust maximum power
point tracking technique and PI current controller design for grid connected PV
system using MATLAB/SIMULINK,” Journal of Electrical Engineering, vol. 15, pp.
315-320, 2015.

Tr Ren Energy, 2018, Vol.4, No.3, 44-55. doi: 10.17737/tre.2018.4.3.0049 54

Peer-Reviewed Article Trends in Renewable Energy, 4

[19] A. Neçaibia, S. Ladaci, A. Charef, and J. J. Loiseau, “Fractional order extremum

seeking approach for maximum power point tracking of photovoltaic panels,” Front.
Energy, vol. 9, no. 1, pp. 43–53, 2015.
[20] E. S. Ali, S. M. AbdElazim, and A. Y. Abdelaziz, “Ant Lion Optimization Algorithm
for renewable Distributed Generations,” Energy, vol. 116, pp. 445–458, 2016.
[21] S. Mirjalili, “The Ant Lion Optimizer,” Adv. Eng. Softw., vol. 83, pp. 80–98, 2015.

Article copyright: © 2018 Raj Kumar Sahu and Binod Shaw. This is an open access
article distributed under the terms of the Creative Commons Attribution 4.0 International
License, which permits unrestricted use and distribution provided the original author and
source are credited.

Tr Ren Energy, 2018, Vol.4, No.3, 44-55. doi: 10.17737/tre.2018.4.3.0049 55