Tài Liệu Đồ Án 5 (Quan Trọng)
Tài Liệu Đồ Án 5 (Quan Trọng)
Tài Liệu Đồ Án 5 (Quan Trọng)
Article
Decreasing the Battery Recharge Time if Using a Fuzzy Based
Power Management Loop for an Isolated Micro-Grid Farm
Habib Kraiem 1 , Aymen Flah 2 , Naoui Mohamed 2 , Mohamed H. B. Messaoud 2 , Essam A. Al-Ammar 3 ,
Ahmed Althobaiti 4 , Abdullah Alhumaidi Alotaibi 5, *, Michał Jasiński 6 , Vishnu Suresh 6 ,
Zbigniew Leonowicz 6 and Elżbieta Jasińska 7
Keywords: wind energy; photovoltaic energy; energy storage; fuzzy logic control; converters; simulation;
online control; energy management; micro-grid
Copyright: © 2022 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
1. Introduction
Attribution (CC BY) license (https:// Advanced energy storage systems are becoming a necessity in isolated regions where
creativecommons.org/licenses/by/ no grid connection exists. An isolated farm cannot only need energy from renewable
4.0/). energy or a diesel generator. Electrical equipment, such as electrical tractors, electrical
fluid irrigation, and a variety of other electrical machines, are used on major agriculture
farms [1–3]. According to international statistics on the usage of electrical equipment in
farming, more than 1,000,000 electrical tractors will be employed until 2025 [4]. Because
these farms are not connected to the power grid, they rely heavily on renewable energy
sources like solar panels, wind turbines, or a mix of the two [5–7]. The decision to use one
of such energy sources is influenced by many criteria, including the farm’s budget, location,
and the amount of power required daily [8,9]. A battery pack is often employed on these
farms in addition to these energy sources, each of which has its own set of flaws and issues.
This will serve as a substitute for power outages and offer electrical energy for 24 h. As a
result, a hybrid energy source exists, therefore controlling the power flow is required to
protect the battery pack or other devices [10].
G
IPh = [ Isc + Ki .( T − Tn )]. (2)
Gn
k.T.ns
Vt = (4)
q
The diode saturation current I0 depends on the temperature of the panel. IP is the
current passing through the parallel resistance of the circuit. All of these parameters may
be found in Equations (5) and (6), respectively.
3
q.Eg
T 1 1
I0 = I0,n . . exp . − (5)
Tn a.k Tn T
V + IPV .Rs
IP = (6)
Rp
Sustainability 2022, 14, 2870 4 of 21
As a result, once the number of panels in serial or parallel is determined, the whole
PV system voltage and current can be stated as in Equations (8) and (9), respectively.
Np .I ph − I pv + Np .Is
a.K.T Ns
VPV = Ns . ln − . I pv .Rs (8)
q Np .Is Np
Ns )
(V+ IPV .Rs. N Ns
p V + IPV .Rs N
IPV = IPh .Np − I0 .NP − e a.Vt Ns −1− p (9)
Ns
Rp. N p
The maximum power equals 1.5 kW that can be given by the built PV model. The
model was built using the datasheet’s accessible information. Table 1 illustrates the PV
module’s essential specifications. To develop a model of a 250 W solar photovoltaic module,
a real PV module SUN EARTH SOLAR POWER TPB156x156-72-P 250 W was considered
as a standard module.
1
Pm = Pw . C p = .ρ.S.Vw3 .C p (λ, β) (10)
2
Cp represents the power coefficient of the wind turbine. It is expressed in Equation (11).
C
C2 − 5
C p (λ, β) = C1 . − C3 .β − C4 .e λi + C6 .λ (11)
λi
1
λi = 1 0.035
(12)
λ+0.08.β − β3 + 1
R. Ω
λ= (13)
Vv
Generally, the wind turbine specification can be analyzed by the curve, which draws
the evolution of the power coefficient C p in concordance to the angle of orientation β of
propellers and the specific speed of the main shaft of the propellers noted λ. Figure 2 gives
this relationship. According to the law of Betz, this coefficient can reach a maximum value
of 59% in theory but practically it can reach 40% for the most efficient wind turbines [25].
The mechanical angular speed of the turbine is calculated using the relationship
between the electrical torque and the mechanical torque, which includes the mass of the
generator and the turbine as in Equation (14).
dωm
J = Te − Tm − F.ωm (14)
dt
depend on equations related to the rotor winding. For this, we can develop the dynamic
model of the PMSG by using the equations of Voltage and flux. The voltage system of
the equation noted (Vsabc ) and magnetic flux system of the equation noted (Øsabc ) can be
visualized in Equations (15) and (16). All of these systems are created in a three-phase
process (ABC) [27,28].
dØabc
Vsabc = rs .isabc . s (15)
dt
dωm
J = Te − Tm − F.ωm (16)
dt
The rotor reference frame can be used to characterize the PMSG’s dynamic model. As
a result, the voltage system of equations in the d and q axes is:
diqs
Vqs = rs .iqs .Lq . + ωr . Ld .ids .Øm (17)
dt
dids
Vds = rs .ids + − ωr .Lq .iqs (18)
dt
The electromagnetic torque is given by:
3 P
Te = . . Øm . iqs + Ld − Lq .iqs . ids (19)
2 2
dV1 V I
=− 1 + (20)
dt R1 .C1 C1
dV2 V I
=− 2 + (21)
dt R2 .C2 C2
Vt = Voc [SOC (t)] − V1 − V2 − I.R0 (22)
I is the current, R0 represents the Ohmic resistance of the storage device, V1 and
V2 denote the polarization voltage over R1 .C1 and R2 .C2 , respectively Vt is the terminal
voltage, Voc represents the open-circuit voltage (OCV), which is a function of SOC.
The SOC value can be expressed as in Equation (23), where Qn is the battery rated
capacity [33].
Z t
1 I ( t ) dt
SOC (t1 ) = SOC (t0 ) − (23)
0 Qn
The battery rated capacity depends on the effect of the capacity fading. The remaining
battery capacity available is expressed in Equation (24) [34].
where CCF represents the Capacity correction factor and Qinit is the Initial battery capacity
[Ah]. From the other side, the functioning of the battery system can be summarized by the
following Equations (25) and (26).
Figure 4. Cont.
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Parameter Value
Pn 1500 Wh
Vn 150 V
Qn 10 Ah
R0 0.15 Ω
2.4. Converters
Basically, numerous inverters must be used for having a DC flow from the major
energy sources. A three-phase static converter based on a diode bridge rectifier (not
controlled) is used to convert the alternating type (AC) electrical energy produced by the
wind turbine into continuous type electrical energy (DC), connected directly by a boost
converter (DC/DC) to reach the MPPT [35,36]. This last converter has the same objective for
the photovoltaic system. On the other hand, a buck-boost converter (DC/DC) is installed
to control the charge and discharge of the battery pack.
Based on these specific works, cited as [37,38], related to the electronic converters, it is
easy to express the input and output voltage of the boost converter as in Equation (27). Vint
is the input voltage, Vout is the output voltage, D is duty cycling.
Vout
Vint = (27)
1−D
The buck-boost converter has two operation modes. It depends on the position of the
Isolate Gate Bipolar Transistor (IGBT), which can be found inside. Supposing that these
two switches have the nomination of K10 and K11 . The relationship between these two
parameters can be expressed in Equation (28).
As this chopper will be connected to a battery pack from one side and a DC bus
line from the other side, Vbat denotes the battery voltage side and Vdc denotes the DC
bus voltage side. So, the relationship between the two voltages can be expressed as in
Equation (29) and the output current can be formulated as in Equation (30).
dibat
Lbat . + rbat .ibat = Vbat − Vdc .(1 − K10 ) (29)
dt
Iout = Ibat .(1 − K10 ) (30)
sun, maximum power can be achieved only if the chopper for both systems is managed
using the MPPT approach. There are several methods for the control of the MPPT, such as
the P&O, the Incremental Counter, and Particle Swarm Optimization. Each of them has
some specifications and based on these works, which give a priority for the PSO method,
this solution was used for controlling the PV and wind choppers. From the other side, a
principal controller placed between all of these sources and load parts for supervising the
energy flow from the different positions is mandatory and necessary. This step is carried
out using a fuzzy logic controller, as stated in the paper’s purpose. The overall control
loop’s concept may be observed in Figure 5, which depicts the principal control loop and
shows the energy flow diagram clearly.
The idea is to continue feeding the load part from the wind system and the PV solar
panels. If there is any excess energy, it is time to store the energy in the battery pack. This
is true whenever the PV and the wind system can provide the required power, else it is
time for the battery to be used. For other situations where no load charge is connected, the
battery will be totally charged using the given power from the two sources. The power
management control loop, which is based on fuzzy logic controllers and controls, the
charging and discharging phases of the battery include four input signals, as shown in
Figure 5. This is accomplished by determining the duty cycle of each pulse width modulator
signal used to drive the buck-boost inverter.
the triangular function is used for defining each state, three points A, B, and C define the
triangular points, like the right, center, and left points, respectively. For example, the first
state of the SOC, denoted as small, has 100% equivalence at 0.25 ratio and 0% if it is 0 or
0.55. However, it will be 100% equivalent to the medium level at that point.
The active load power was meant to be consistent throughout the rest of this project,
and it was attempted to lessen the complexity of the rules configuration (15 kW). As a
result, only three input signals will be established for the fuzzy controller to be built.
Before being given the rules that will regulate the whole system, a defuzzification
stage must be configured in addition to the fuzzification phase. The fuzzy controller sends
out the control signal. It has two output data points. Each one contains the required
duty cycle for controlling the buck-boost converter’s related component. The comparable
duty cycle factors for each of the output signals are listed in Table 5 according to their
linguistic designation. Similar to the input vectors, each output vector, which defines the
corresponding duty cycle of each of the IGBT components into the buck-boost converter, is
divided into three states. Little, medium, and high are those states. The triangular function
is the used activation model and the three limit points of the triangular form for each of
these inputs and states are denoted A, B, and C.
The corresponding rules that will manage the different input and output signals are
summarized in Table 6 and Figure 6, which were concluded after several configuration
tests to have satisfactory performance.
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Input Vector
Rules Out1/K10 Out2/K11
SOC Pm PPV
1 Small Big Small High Little
2 Small Big Medium High Little
3 Small Big Big Medium medium
4 Small Medium Small High Little
5 Small Medium Medium medium Little
6 Small Medium Big medium Little
7 Small Small Small medium Medium
8 Small Small Medium medium Medium
9 Small Small Big medium Little
10 Medium Big Small High Little
11 Medium Big Medium medium Medium
12 Medium Big Big medium Medium
13 Medium Medium Small medium High
14 Medium Medium Medium medium High
15 Medium Medium Big Little Medium
16 Medium Small Small medium Medium
17 Medium Small Medium Little High
18 Medium Small Big Little Little
19 Big Big Small medium High
20 Big Big Medium little High
21 Big Big Big little High
22 Big Medium Small medium Medium
23 Big Medium Medium little Medium
24 Big Medium Big little High
25 Big Small Small medium High
26 Big Small Medium little High
27 Big Small Big little High
load and the battery pack. The same inputs were used in order to make a real comparison
between the two controllers and power management strategies. The neural network
controller has these specifications: The learning phase was applied using a database of
5000 points for each of these inputs and outputs. There was a three-layer block for the
overall architecture of this controller. The sigmoid activation function was implemented in
each cell. The internal architecture was as follows: three input layers in the first stage, two
cells in the middle layer, and one cell in the last layer. The training epoch number was 500.
The similarity value was evaluated to be 98%. It is important to mention that the database
was obtained after several variations on the solar radiation, wind speed, and battery initial
SOC. The corresponding control loop can be visualized in Figure 5.
be charged or be discharged. So, the gain of energy in the battery pack will be the result of
this comparison between the fuzzy control solution and the other control loops.
Figure 7. The given wind and solar radiation forms for generating wind power and solar power.
Sustainability 2022, 14, 2870 14 of 21
In addition, the photovoltaic power was shown under four different irradiation condi-
tions. From 0 to 1 s, the irradiation factor is 500 and then the irradiation value increases to
1000. Correspondingly, the obtained PV power is 1.5 kW and then moves to 5 kW. Then,
the obtained power decreases more as the solar radiation form decreases.
Remark 1. The obtained PV and wind power results are based on the PSO-MPPT technique that was
used for generating and stabilizing the maximum output energy from these renewable energy sources.
This studied case has supposed that the load part will rest constantly during the simula-
tion time and be equal to 15 kW per hour. The obtained statistics, illustrated in Table 7, give
an approximation of the contribution of the PV and wind systems and evaluate what kind of
energy will be available. As mentioned in Table 7, 10,500 W is needed as additional power for
the first simulation second and 8500 W is the needed power in the fourth simulation second.
However, extra power exists from the first to the third simulation second.
Table 7. The Given power by the PV and Wind systems for the first studied case (a load of 15 kW).
Because the load component requires 15 kW of active power, the battery will contribute
10.5 kW, demonstrating why the battery SOC drops from 100% to 99.63% if both sources
are present. Otherwise, the coming power will reach 20kW when the irradiation factor
and wind speed grow. After feeding the load component, the battery can be charged with
the remaining energy. This demonstrates an increase in battery SOC starting at 1.2 s. The
provided power from the hybrid generator crosses the 15 kW mark after 1.2 s. The battery
SOC will continue to decrease if just the PV or wind systems are used. The battery SOC
becomes stable when the wind system starts feeding the load directly and at the maximum
supported wind speed, which is 1.5 to 2 s, as illustrated in Figure 8. It is necessary to point
out that the battery SOC slope in the wind system is greater than in the PV system. This is
owing to the battery’s reactive power, which powers the PMSG generator.
Figure 8. Cont.
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Figure 8. SOC form for three different situations: Case of only wind system is active, Case of only
PV generators are valid and the Case of the hybrid energy system in reaction. (a) SOC form for
four control topologies: cases of only Wind generator case under a constant load. (b) SOC form for
four control topologies: cases of only PV generator case under a constant load. (c) SOC form for four
control topologies: cases Hybrid source case under a constant load.
The difference between the four control topologies can be visualized in the various
figures in Figure 8. Each source was evaluated by each control method and the hybrid
combination was also inspected. The majority of the obtained results show that with the
proposed control topology, the battery SOC will be higher than the three other control
topologies at the end of the simulation, which proves the benefit of this proposal.
The corresponding duty cycle for each control method such as the relay control, PI
control, neural control, and fuzzy control loops are illustrated in Figure 9. It is clear that:
the duty cycle evolution is dynamic in the fuzzy control loop, the neural network loop,
and with the PI control topology. This proves the situation of the battery charge evolution.
However, the duty cycle performance for the relay control loop, illustrated in the same
figure, is still constant and was fixed to 75%. Approximately, it is the same situation with
the PI control method. However, some perturbation appears when the given power from
the hybrid energy source crosses the needed load power. Similar to the fuzzy control
Sustainability 2022, 14, 2870 16 of 21
performances, the neural network has a good impact on the given duty cycle form and it
touches the maximum value as it is with the fuzzy controller.
Based on the obtained results from the majority of the implemented controllers, such as
the relay controller, the neural network controller, the PI controller, and the proposed fuzzy
controller, the evolution of the battery state of charge can be regrouped into Table 8. Table 8
shows the SOC trough for 4 s as the simulation time and for constant load comportment.
If the hybrid energy source is used, the SOC drops by 0.97% if using the fuzzy controller.
However, the SOC will decrease by 1.42% if the relay control technique is used. 1.27% is
the difference between the initial and final SOC for the case of the neural controller and
1.37% is the SOC difference if the PI controller is used. Similar to when only one energy
source is used, the fuzzy comportment can give an enhanced reaction.
Table 8. A comparison between the SOC losses for the four control methods (case of constant load).
SOC (%) Loss Only PV Generator Only Wind Generator PV and Wind Generators
with fuzzy controller 3.62% 3.35% 0.97%
With neural controller 4.29% 4.00% 1.27%
With PI controller 5.08% 4.82% 1.37%
with Relay controller 5.15% 4.74% 1.42%
Based on these results, the best proposal is related to the fuzzy control loop and the
worst solution is related to the relay control method. Therefore, the rest of the comparison
will be concentrated on these two specifications and will try to show the benefit of the best
control method compared to the worst control loop.
It is necessary to mention that the PI control loop and the neural network control
loop can be updated in future endeavors, improving their specific performances and this
can improve the global system performance and have a different impact on the power
management loop.
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So, based on the past specifications of the wind and photovoltaic generators and the
preceding battery specifications, it is possible to have a fully recharged battery in 0.46 h
using the fuzzy control topology and 1 h using the relay control loop, demonstrating that
the fuzzy control loop has a better performance.
Figure 10. Djerba Island (33◦ 480 N, 10◦ 510 E): location and climatic specification.
This study has supposed that at the start of the day, the battery charge is 50%. The
results were depicted for the first month of the year in this island location, whose climatic
conditions are illustrated in Table 9. For example, in the first month of the year, the solar
radiation will be maximum (e.g., 1000 w/m2 ) only for one hour per day, and the wind will
be existing all day and maximal (e.g., 23 km/h) for 23 h per day. So, the hybrid system will
be available with its maximum yield only for one hour per day.
Therefore, the applied test in this section was made only for one day in this month
(i.e., January), and the load part was supposed constant at 7.5 kW/h all day.
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Table 9. Average hours of solar radiation and wind: the case of Djerba/Tunisia island location.
Figure 11 shows one battery cell SOC evolution when the hybrid energy system can
generate its maximum power, concerning the maximum solar radiation and maximum
supported speed of the installed wind system. This is shown in Figure 11, from 1.2 to 2 s.
Figure 11. One cell battery SOC evolution, for the studied case in Djerba Island for a constant
load demand.
Figure 11 shows two solar radiation and wind speed profiles and gives the state of
charge of the battery for each case. As the given power from the hybrid energy system
is less than 7.5 kW/h, the battery will participate in feeding the load with the necessary
power. So, the battery SOC will decrease. However, it is clear that with the fuzzy logic
controller, it is possible to have a gain of 0.31% in 1.2 s.
In the curve of Figure 11, the excess energy will be available for 0.8 s (from 1.2 to 2 s)
and this will help charge the battery from 48.58% to 48.78% in 0.8 s if using the relay control
and from 48.89% to 49.34% if using the fuzzy controller. It is possible then to conclude that
in 0.8 s the battery cell will be charged by 0.20% with the relay control and by 0.45% with
the fuzzy controller.
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Even, when the hybrid energy system is operating with its maximum capacity, the
battery will be then charged by the excess energy given by the source, which is equivalent
to 2.54 kW per hour if the fuzzy solution is used and 0.98 kW per hour if the TOR control is
implemented. These results were depicted after one hour of simulation time.
Based on the obtained results in Figure 11, it is possible to charge one battery cell
having a capacity equivalent to 0.12 kWh, in 176.4 s from 0 to 100% with the fuzzy control
method and 440.8 s with the TOR control loop. So, for a battery pack having 6 kWh as
battery capacity (The equivalent to 50 cells), it is mandatory to use 8820 s (the equivalent to
2.45 h) for fully charging the battery pack if the fuzzy controller is used. The same method
is used for estimating the needed time for fully charging the battery pack with the TOR
technique and the needed time is 22,040 s (the equivalent to 6.12 h).
5. Conclusions
The proposed research presents a new control topology for charging a battery pack
coupled to an isolated micro-hybrid grid that includes a solar generator, a wind system,
and a battery storage system. When the micro-hybrid grid is partially or not operational,
the battery pack is linked to feeding the load component. One of the basic objectives of
this work is to assure a full recharge situation as rapidly as it is possible. Minimizing the
recharge time in such a situation, on the other hand, can help improve battery capacity
and provide a bigger power margin for an isolated farm using renewable energy. As a
result, the benefits of the suggested control topology have been proven by the provided
data and statistics, demonstrating that it is possible to enhance battery capacity by 100%
and reduce the recharge time by 50%. The proposed control method was evaluated for four
control topologies—the neural network control method, the PI control solution, and the
basic relay ON-OFF control loop. It was evaluated for a fixed load demand for various
climatic conditions. After proving the benefit of the proposed solution, a statistical analysis
was carried out for a real case in Tunisia. For 6 kWh battery capacity, the statistics show
that it is possible to fully charge the battery pack in 1.18 h if using the proposed approach,
which is 50% of the needed time when the conventional relay ON-OFF controller is used
From the other side, it is important to mention that the real efficiency of this proposed
control topology, must be validated for a dynamic load case. This will prove and validate
the importance of this solution. Also, as a future endeavor, the optimization of the relay
control technique can be a useful solution for the simple control loop and challenging
meta-heuristic-based frameworks can be used for that objective. After this optimization, a
new comparison for this proposed control loop and the optimized relay control strategy
can be used as a perspective for future work.
Author Contributions: All authors contributed equally to this research project. All authors have
read and agreed to the published version of the manuscript.
Funding: This research was funded by Ministry of Education in Saudi Arabia under grant number
(IF-2020-NBU_406) and by Taif University under grant number (TURSP-2020/277).
Acknowledgments: The authors extend their appreciation to the Deputyship for Research & Innova-
tion, Ministry of Education in Saudi Arabia for funding this research work through the project number
(IF-2020-NBU_406). The authors gratefully thank the Prince Faisal bin Khalid bin Sultan Research
Chair in Renewable Energy Studies and Applications (PFCRE) at Northern Border University for
their support and assistance. Also, the authors thank Taif University for funding this work through
the Taif University Research Supporting, Project number (TURSP-2020/277), Taif University, Taif,
Saudi Arabia.
Conflicts of Interest: The authors declare no conflict of interest.
Sustainability 2022, 14, 2870 20 of 21
List of Symbols
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