Particle swarm optimization (PSO) and its application in MPPT

controller and propose two new algorithms: Discrete Particle Swarm

Optimization (DPSO) and Parallel Particle Swarm Optimization (PPSO) by

developing based on the classic PSO algorithm and applying them in MPPT

controller to improve the performance of PV system. MPPT control technique

based on DPSO and PPSO algorithms has been successfully tested on the

experience system. The simulation results show that the output power of the

proposed algorithm is over 99% with a few iterations in all environmental

conditions. Moreover, these results are compared with those obtained from

MPPT sets using traditional algorithms, to demonstrate the ability to

eliminate the disadvantages of applying traditional algorithms to MPPT

controllers. of the PV system.

35 trang |

Chia sẻ: honganh20 | Ngày: 01/03/2022 | Lượt xem: 299 | Lượt tải: 0
Bạn đang xem trước 20 trang tài liệu **Tóm tắt Luận án Research and development a solar power system**, để xem tài liệu hoàn chỉnh bạn click vào nút DOWNLOAD ở trên

el
PWM
Fig 2.5. The MPPT control diagram of PV
2.3 Conclusion of chapter 2
In this chapter, the author presented an overview of the solar power
system including the main components of the system and the characteristics
of PV solar. The author also points out the central outlines to develop solar
power systems.
The solar energy system has a big size so we have many problems to
solve and develop for the whole perfect system. In this thesis, the author
focuses on researching and developing the algorithm to find out the
maximum power point of the MPPT to help the whole system can work
efficiently and stably.
Finally, the author also designed and manufactured an experimental
system of grid-connected solar systems following the direction of developing
the DC/DC and DC/AC power transformations.
7
CHAPTER 3
RESEARCH AND DEVELOPMENT OF CONTROLLER
MAXIMUM POWER POINT TRACKING OF SOLAR POWER SYSTEM
3.1 Factors affecting MPP
3.1.1 The effect of solar irradiation
Current according to irradiation as the formula (3.1).
𝐼 = (
𝐺
𝐺0
) 𝐼𝑆𝐶 − 𝐼0 [𝑒
𝑞𝑉𝑑
𝑛𝐾𝑇 − 1] (3.1)
Fig 3.1. I - V, P - V and P - I characteristic curve with different radiation levels
3.1.2 The influence of temperature
The short-circuit current is given by a formula of temperature (T) as (3.2).
𝐼𝑆𝐶(𝑇) = 𝐼𝑆𝐶(𝑇𝑟)[1 − 𝛼(𝑇 − 𝑇𝑟)] (3.2)
I - V, P - V and P - I characteristic curve with different temperature
levels as shown in Fig 3.2. As the temperature increases, the I - V
characteristic shifts to the left, meaning the voltage decreases. The maximum
power point (MPP) of the photovoltaic battery system has also changed.
Therefore, it is required to have an algorithm to track MPP so that the system
can work at MPP to reduce losses and improve performance for the whole
system [111].
Fig 3.2. I - V, P - V and P - I characteristic curve with different temperature levels
8
3.1.3 The influence of shading
Shade phenomena are defined when photovoltaic (PV) is partially
covered, which can severely affect the performance of PV. Shading not only
reduces the capacity of photovoltaic cells but also changes the open-circuit
voltage Voc, short-circuit current Isc and their performance.
Fig 3.3. I - V, P - V and P - I characteristic curve under partial shading condition
3.2 Maximum power point tracking
An important component of the PV system is the maximum power
point tracking (MPPT), which helps the PV system to generate the maximum
output power of the system, reduce power loss and solve economic problems
for the PV system. It acts as a power device that links the photovoltaic cell to
the load, controlling the operating point of the photovoltaic battery system to
obtain maximum power from the photovoltaic battery system with changing
environmental conditions such as temperature, radiation, shade ... so the
system performance is improved.
3.3 Research and development of incremental conductance algorithm
for maximum power point tracking of photovoltaic system
3.3.1 MPPT by using Conventional Incremental Conductance algorithm
INC algorithm diagram as shown Fig 3.4 [5]:
Fig 3.4. Flowchart of the INC algorithm
9
If D is considered as a fixed control variable, the traditional INC
algorithm flowchart is rewritten as shown Fig 3.5.
Start
Read V(k), I(k)
dV = V(k) - V(k-1); dI = I(k) - I(k-1)
dP = V(k)*I(k) - V(k-1)*I(k-1)
D D(k) = D Dmax
dV = 0
YesNo
dI = 0
Yes
D(k) = D(k-1)D(k) = D(k-1)
Yes
No
dI > 0
Yes
D(k) = D(k-1) + D D(k) D(k) = D(k-1) - D D(k)
No
V(k-1) = V(k); I(k-1) = I(k)
Return
No
D(k) = D(k-1) - D D(k) D(k) = D(k-1) + D D(k)
No
Yes
dI/dV = - I/V
dI/dV > - I/V
Fig 3.5. Flowchart of the INC algorithm with a fixed D variable
In the algorithm flowchart of Fig 3.5 the step size is fixed. The fixed
size jump affects the efficiency of the MPPT control algorithm INC. As
mentioned above, the conventional MPPT methods based on fixed step size
has a good performance. However, they are characterized by slow
convergence; oscillations in the PV power around the MPP, operation fail
under rapidly changing atmospheric conditions and they can get lost and track
the MPP in the wrong direction during rapidly changing atmospheric
conditions. Speedy tracking can be achieved with larger step size but
excessive steady state oscillations are unavoidable, smaller step size can
reduces the oscillations with slower dynamics. This limitation is overcome
by the development of the INC algorithm with self-adjusting hop sizes that
will be presented in the next section.
3.3.2 Proposed Variable Step Size INC algorithm to achieve faster
response time
The variable step size method proposed is given as follows:
10
𝐷(𝑘) = 𝐷(𝑘 − 1) ± 𝑁 ∗ |
𝑑𝑃
𝑑𝑉 − 𝑑𝐼
| (3.3)
where N is the scaling factor adjusted at the sampling period to regulate the
step size, which is manually adjusted in INC algorithm. The flowchart
diagram of the MPPT INC algorithm has a change step shown in Fig 3.6.
Start
Read V(k), I(k)
dV = V(k) - V(k-1); dI = I(k) - I(k-1)
dP = V(k)*I(k) - V(k-1)*I(k-1)
DD(k) = N*abs(dP/(dV-dI))
dV = 0
YesNo
dI = 0
Yes
D(k) = D(k-1)D(k) = D(k-1)
Yes
No
dI > 0
Yes
D(k) = D(k-1) + DD(k) D(k) = D(k-1) - DD(k)
No
V(k-1) = V(k); I(k-1) = I(k)
Return
No
D(k) = D(k-1) - DD(k) D(k) = D(k-1) + DD(k)
No
Yes
dI/dV = - I/V
dI/dV > - I/V
Fig 3.6. The flowchart of the INC algorithm with variable step size
aims to converge quickly
3.3.3 Proposed Variable Step Size INC algorithm to achieve less
oscillations
When the system is in the steady state, the power variation values are
not large, step size should make a satisfactory tradeoff to increased
oscillations, will reduce the power loss for the system. In this proposed
algorithm, the thesis also applies a jump value of decreasing size to zero, to
reduce the fluctuation of capacity when reaching MPP.
|𝑑𝑃| ∆𝐷𝑘 =
∆𝐷𝑘−1
𝛼
(3.4)
11
With the condition as shown in the expression (3.4), the next size
step will be determined based on the variability of the previous step, and if
the power varies slightly, then the step value will decrease with a division
factor α, and this α value is usually chosen as 2.
Start
Read V(k), I(k)
dV = V(k) - V(k-1); dI = I(k) - I(k-1)
dP = V(k)*I(k) - V(k-1)*I(k-1)
dV = 0
YesNo
dI = 0
Yes
D(k) = D(k-1)D(k) = D(k-1)
Yes
No
dI > 0
Yes
D(k) = D(k-1) + D D(k) D(k) = D(k-1) - D D(k)
No
V(k-1) = V(k); I(k-1) = I(k)
Return
No
D(k) = D(k-1) - D D(k) D(k) = D(k-1) + D D(k)
No
Yes
dI/dV = - I/V
dI/dV > - I/V
dP
Yes
No
D D(k) = D D(k-1)/2
Yes
D D(k) = N*abs(dP/(dV-dI))
Fig 3.7. The flowchart of the INC algorithm with variable step size in order
purpose of reducing oscillation at MPP
3.4 Research and development Particle Swarm Optimization based
MPPT algorithm
3.4.1 Particle Swarm Optimization (PSO)
PSO is one of evolutionary computation techniques proposed by
Eberhart and Kennedy in 1995 [48,49]. It is inspired by social and
cooperative behaviour displayed by various species to fill their needs in a
12
multi-dimensional search space. In PSO, the updated positions of each
particle in the search space is given by the following two equation as[65] :
1
, , 1 1 , , 2 2 ,( ) ( )
k k k k k k
i j i j i j i j j i jV wV c r Pbest X c r Gbest X
(3.5)
1 1
, , ,
k k k
i j i j i jX X V
(3.6)
A detailed flowchart of PSO considering the above steps is shown in Fig.3.8.
k = k + 1 If k ≤ maxite?
No
Yes
Set PSO parameters
Initialize population of particles
with positions and velocity
Evaluate initial fitness of each
particle and select
Pbest and Gbest
Set iteration count k = 1
Update velocity and position of
each particle
Evaluate fitness of each particle
and update Pbest and Gbest
Print optimum values of
generator output as Gbest
Fig 3.8 Flowchart of PSO algorithm
13
3.4.2 Development Particle Swarm Optimization algorithm
3.4.2.1 Differential Particle Swarm Optimization (DPSO)
The DPSO is a modified version of PSO, in which particles are
capable of escaping from local minima in order to find a better optimization
solution in the search space.
The proposed DPSO considers an additional feature in the classical
PSO. The additional feature is the opinion of one of the particles selected
randomly from the swarm. The randomly-scaled difference of the particle and
its opinion-giver particle is included in the velocity equation of the particle
necessary to escape from local minima. Mathematically, the concepts of
DPSO can be expressed as follows:
1 ( ) ( ) ( ), , , , , ,11 2 2 3 3 ,
k k k k k k k kV wV c r Pbest X c r Gbest X c r X Xp q p q p q p q q p q p ql q
(3.11)
1 1
, , ,
k k kX X Vp q p q p q
(3.12)
In eqn. (3.11), c3 is the scaling factor and r3 is a randomly-generated
random number between 0 and 1, whereas l represents the expert particle
corresponding to target particle p. In this equation, l varies from 1 to N but l
≠ p. Fig.3.9 shows the search mechanism of the proposed DPSO in a
multidimensional search space.
Gbestk
Xp
k
Xp
k+1
Vp
Gbest
Pbestp
k
Vp
k
Vp
Pbest
Vp
k+1
Vp
Diff
Fig 3.9 Proposed DPSO search mechanism of pth particle at kth iteration in a
multi-dimensional search space
In Fig. 3.9, Pbestkp,q represents personal best qth component of pth
individual, whereas Gbestkq represents qth component of the best individual
of population up to iteration k.
It is found from Fig. 3.9 that the proposed DPSO is performed by
adding one more term (VpDiff) in the velocity equation, thus the MPP can be
obtained much sooner than that using the classical PSO. Furthermore, this
additional feature allows the particles to escape from a local optimum in order
to search for a better solution in the other reasons in the search space.
14
Application of DPSO to MPPT
As mentioned in chapter 2, photovoltaic cell characteristics are non-
linear, The output of power system is varied with the variation of light
radiation and temperature, therefor reducing the efficiency of PV solar. In
addition, the effectiveness of these photovoltaic modules is not satisfactory,
thus requiring the assistance of intelligent algorithms to overcome this
problem. In this thesis, a model using the DPSO-based MPPT algorithm is
proposed to improve the performance of PV.
The complete flowchart for the proposed method is shown in Fig. 3.10:
Yes
Set w, c1 , c2,c3 , n
Sense V(i), I(i). Calculate initial fitness of each
particle: P(i)=V(i)*I(i)
Evaluate initial fitness of each particle and
select Pbest and Gbest
Set iteration count k =1
Update velocity and position of each particle
Evaluate fitness of each particle and update
Pbest and Gbest
If k <= Maxite ?
Print optimum values of the duty cycle as Gbest
k = k+1
No
Initialize positions and velocity of each particle
d=d0; v=0.1*d0
Fig 3.10. Flowchart of the proposed MPPT - DPSO
15
3.4.2.2 Perturbed Particle Swarm Optimization Algorithm (PPSO)
The PPSO is a modified version of PSO, perturbation in the velocity
vector of each particle needs to be performed whenever the particles get
struck into a local optimum. Normally, this situation occurs when the solution
(Gbest of the swarm) does not be improved for a pre-specified number of
iterations, such as the tolerance or a stopping criterion (other than maximum
iteration) is met. For this, the velocity vector of each particle needs to be reset,
so that particles can get a big thrust to push them to escape from the local
optimum. Mathematically, the perturbation concepts of velocity for each
particle can be expressed as follow:
1
, ,
k k
p q p qV pbest
(3.13)
Besides, the tolerance limit needs to be relaxed, so that the swarm can
get another chance to resume the search for a certain minimum number of
iterations. Also, such perturbation may be allowed for a certain number of
times. Consequently, the proposed perturbation in the velocity vector of each
particle allows the particles to keep exploring the search space in order to
escape from the local optimum. A typical distribution pattern of each particle
with larger length of arrows in PPs is shown in Fig. 3.11.
Fig 3.11 Dynamics of particle in the multi-dimensional search space in PSO
algorithm
Application of PPSO to MPPT
The overall flowchart of the proposed PPSO-based MPPT approach is
shown in Fig. 3.12.
Firstly, the duty cycle value d of the dc–dc converter is selected as
the particle position in the search space. Meanwhile, the PV module output
power is defined as the fitness value evaluation function. It can be noted that
the number of particles should be selected as the number of the series
LCP1
LCP2
LCP3
LCP4
LCP5
IP2
IP1
IP3
IP5
Local optimum
IP4
PP5
IP4
PP1
PP3
Global optimum
PP3
PP4
IP: Initialized Particle
PP: Perturbed Particle
LCP: Locally Converged Particle
Arrow: Represents direction of move
Length of arrow: Values of velocity
16
connected cells in PV array. Next, the PPSO algorithm will start the
optimization process from a random initial value that is chosen in the
following range:
Dik = [D1, D2, D3, DN]
Yes
Set w, c1 , c2,c3 , n
Sense V(i), I(i). Calculate initial fitness of each
particle: P(i)=V(i)*I(i)
Evaluate initial fitness of each particle and
select Pbest and Gbest
Set iteration count k =1
Update velocity and position of each particle
Evaluate fitness of each particle and update
Pbest and Gbest
If k <= Maxite ?
Print optimum values of the duty cycle as Gbest
k = k+1
No
Initialize positions and velocity of each particle
d=d0; v=0.1*d0
If tol<tol_max
Yes
tol=1
V=Pbest tol = Fb
k
- Fb1
k+1
No
Hình 3.12 Flowchart of the proposed PPSO-MPPT
17
Where N is the number of particles and k is the number of iterations.
It means the particles are initialized on random positions, which cover the
search space [Dmin, Dmax]. Dmax and Dmin are the maximum and minimum duty
cycle of the utilized dc-dc converter, respectively. Then, the digital controller
sends the PWM command according to the duty cycle, which also represents
the position of particle i, the PV voltage VPV and current IPV are defined.
These values are the basis of calculating the fitness evaluation of particle i
(the PV module output power). The fitness of each particle is evaluated
k kp pF f X , ∀p and then finding the best particle index b. Finally, the
PPSO algorithm updates Pbest, Gbest until the optimum value of the duty
cycle, known as Gbest is obtained. It can be noted that once tol < tol_max
then V = Pbest and tol =1, and else tol = Fbk - Fb1k+1.
3.5 Conclusion of chapter 3
In the PV system, it is always desirable whether in any weather
condition, the power output from the PV array to the load is always the
maximum, which is the goal of MPPT control problem. Chapter 3 the author
studies the common maximum power control algorithms and makes
development improvements for the algorithm to apply in MPPT control of
PV system to make the system operate better performance.
In this chapter, the author introduces some algorithms for MPPT
controller and research INC algorithm and makes development
improvements for INC algorithm to make the system reach the maximum
power point faster, fluctuate around the point, maximum power is narrower
and less, minimizing the loss of transmitted power due to fluctuations around
the maximum power point. In this chapter 3, the author also focused on the
PSO algorithm applied in MPPT control and proposed new algorithms by
developing based on the classic PSO algorithm that is DPSO algorithm and
PPSO algorithm to improve the operational efficiency of the PV system.
18
CHAPTER 4
SIMULATION VERIFICATION MPPT ALGORITHMS
EVELOPMENT SYSTEM FOR SOLAR ENERGY POWER
4.1. Simulation results of improve INC algorithm
4.1.1. Simulation diagram
Fig 4.1. Simulation of INC algorithm
4.1.2 Simulation results
4.1.2.2 INC algorithm to achieve faster response time
The author conducted the simulation according to the proposed theory,
resulting in the response of the PV system compared to the conventional INC
as shown in Fig 4.2.
a) Comparison of current response
b) Comparison of voltage response
0 2 4 6 8 10 12 14
0
5
10
15
20
25
Time(s)
I(
A
)
CURRENT
Traditional I
Variable I
0 2 4 6 8 10 12 14
0
50
100
150
200
250
300
350
400
Time(s)
V
(V
)
VOLTAGE
Traditional V
Variable V
19
c) Comparison of power response
Fig 4.2. Compare the responses of the conventional INC algorithm and
variable size step
Fig 4.3 D response of the conventional INC algorithm and variable size
step at start
Fig 4.4 P response of the conventional INC algorithm and variable size
step at start
Fig 4.5 P response of the conventional INC algorithm and variable size
step when the radiation changes from G = 700W / m2 to G = 900W / m2
Through simulation results, it is easy to observe the speed of MPP
reaching at the beginning of INC operation. The variable step case much
faster than the conventional INC shown in Fig 4.4. Even in the case of a
0 2 4 6 8 10 12 14
0
1000
2000
3000
4000
5000
6000
7000
8000
Time(s)
P
(W
)
POWER
Theory P
Traditional P
Variable P
0 0.5 1 1.5 2 2.5 3 3.5 4
0.1
0.15
0.2
0.25
0.3
0.35
0.4
0.45
0.5
0.55
0.6
Time(s)
D
u
ty
DUTY CYCLE
Traditional D
Variable D
0 0.5 1 1.5 2 2.5 3 3.5 4
0
1000
2000
3000
4000
5000
6000
7000
Time(s)
P
(W
)
POWER
Theory P
Traditional P
Variable P
4 4.05 4.1 4.15 4.2 4.25 4.3 4.35 4.4 4.45 4.5
5800
6000
6200
6400
6600
6800
7000
7200
7400
7600
7800
Time(s)
P
(W
)
POWER
Theory P
Traditional P
Variable P
20
radiation change from G = 700W / m2 to G = 900W / m2, the improve INC
algorithm is MPPT better, as shown in Fig 4.5, we can see the variable step
INC fluctuating changes around MPP are lighter than conventional INC.
4.1.2.3 INC algorithm to reduce oscillations
The results obtained from Matlab software simulation as shown in Fig
4.6 show that this proposed plan has a fast convergence rate and a significant
reduction in oscillation at MPP.
a) Comparison of current response
b) Comparison of voltage response
c) Comparison of power response
Fig 4.6. Compare the responses of the conventional INC algorithm and
variable size step to reduce oscillations
0 2 4 6 8 10 12 14
0
5
10
15
20
25
Time(s)
I(
A
)
CURRENT
Traditional I
Modify Variable I
0 2 4 6 8 10 12 14
0
50
100
150
200
250
300
350
400
Time(s)
V
(V
)
VOLTAGE
Traditional V
Modify Variable V
0 2 4 6 8 10 12 14 16
0
1000
2000
3000
4000
5000
6000
7000
8000
Time(s)
P
(W
)
POWER
Theory P
Modify Variable P
21
a) D response at start
b) D response when radiation change
Fig 4.7. Graph D when transient and increasing radiation
From Fig 4.7 a) we see that, with a variable step value, the system will
disturb longer steps, limited to 10%, and as close to the maximum power
value, the small step value is gradually and approaching zero, where the
descending value α is chosen as 2, which means that the algorithm will
continuously reduce the hop value for 2 until it reaches a value near zero. It
rudece significantly the oscillation at MPP
In order to compare the efficiency among the methods, the author
redraws the power lines at the start-up time as shown in Fig 4.8 a), radiation
change as shown in Fig 4.8 b) and the stabilization system in Fig 4.8 c).
a) P response at start
0 0.5 1 1.5 2 2.5 3 3.5 4
0.1
0.15
0.2
0.25
0.3
0.35
0.4
0.45
0.5
0.55
0.6
Time(s)
D
u
ty
DUTY CYCLE
Traditional D
Modify Variable D
3.9 3.95 4 4.05 4.1 4.15 4.2 4.25 4.3 4.35 4.4
0.46
0.47
0.48
0.49
0.5
0.51
0.52
0.53
0.54
0.55
Time(s)
D
u
ty
DUTY CYCLE
Traditional D
Modify Variable D
0 0.5 1 1.5 2 2.5 3 3.5 4
0
1000
2000
3000
4000
5000
6000
7000
Time(s)
P
(W
)
POWER
Theory P
Traditional P
Variable P
Modify Variable P
22
b) P response when radiation change
c) P response when stabilization state
Fig 4.8 Compare power response of three algorithms and theory
From the comparision of simulation results, it has been proved that the
two proposed algorithms have the ability to improve the performance of the
conventional INC algorithm in terms of response time, oscillation at MPP and
power loss. This proves the suitability of the proposed algorithm compared
with conventional algorithm.
4.2. Simulation results of PSO algorithm and improve PSO algorithm
4.2.1 Simulation results of MPPT-PSO
Following the flowchart of PSO algorithm in Fig 3.8, the author
simulates the output power responses of the PV system and compares with
the case without MPPT controller as shown in Fig 4.9. From the output
response we see with the PSO algorithm that the output power response is not
fluctuated as the INC or P&O algorithm .
Hình 4.9 The response time of output power using the PSO
4 4.1 4.2 4.3 4.4 4.5 4.6 4.7 4.8 4.9 5
5800
6000
6200
6400
6600
6800
7000
7200
7400
7600
7800
Time(s)
P
(W
)
POWER
Theory P
Traditional P
Variable P
Modify Variable P
11 11.2 11.4 11.6 11.8 12 12.2 12.4 12.6 12.8 13
6660
6680
6700
6720
6740
6760
6780
6800
6820
6840
6860
Time(s)
P
(W
)
POWER
Theory P
Traditional P
Variable P
Modify Variable P
23
4.2.2 Simulation results of MPPT-DPSO
The proposed DPSO algorithm has been tested on five benchmark
problems to prove the effectiveness of the developed algorithms in solving
complex optimization problems.
Fig. 4.10 shows the response time of output power under constant
environmental condition (the solar insolation G = 900 W/m2 and the
temperature T = 25°C) in case of without MPPT controller, using DPSO and
the conventional methods.
Hình 4.10. The response time of output power using the DPSO and the
conventional methods
To demonstrate the superiority of the DPSO, a comparison of
tracking the optimization point using the PSO is performed for the test PV
system, as shown in Fig. 4.11. It can be clearly seen that both of the AI
algorithms can find the optimization of centralized arrays without fluctuation
around the MPP, but the improved tracked faster. The optimization time of
the DPSO and PSO is 0.57 s and 2.12 s, respectively.
Fig 4.11. The response time of output power using the DPSO and PSO
Fig 4.12 shows the dynamic characteristic of the output power under
solar radiation varied between 700 W/m2 and 800 W/m2 at a fixed
temperature of 25°C.
24
Fig 4.12 The dynamic characteristic of output power during rapidly increasing
radiation values
Fig 4.13 The dynamic characteristic of output power during rapidly decreasing
radiation values
The results of the different MPPT controllers under fluctuations of
irradiation are summarized in Table 4. It is observed that the power produced
by using the proposed algorithm was greater than 99% under all test
conditions.
Table 4.1 The obtained results of output power without/with the MPPT
controller
G
(W/m2)
Without
MPPT
P&O InCond PSO DPSO The
theoretical
value of
PV
600 4567.0 5137.0 5137.4 5157.2 5157.5 5157.7
700 5913.0 5994.8 5995.0 6009.0 6009.2 6009.7
800 6820.0 6812.0 6812.3 6849.1 6849.5 6850.0
900 7360.0 7655.0 7656.0 7677.2 7678.0 7678.3
4.2.3 Simulation results of MPPT-PPSO
The proposed PPSO algorithm has been tested on five benchmark
problems to prove the effectiveness of the developed algorithms in solving
complex optimization problems, and then it is used to detect the global
25
maximum power point considering the phenomenon of shade (PSC). In
addition, these results are compared with the traditional PSO algorithm to
prove the effectiveness of the proposed method.
For the purpose of tracking MPP, the proposed PPSO-MPPT
algorithm is performed on a model that consists of a PV solar panel connected
to a resistive load through a boost converter with MPPT controller. The PV
array consists of the four PV modules which were connected in series, as
shown Fig. 4.14.
Fig 4.14. Simulation model of four PV models connected in series performed in
Simulink
Firstly, the simulation of a PV array under PSC, in which the PV array
is not connected to a boost converter and MPPT algorithms is performed. The
corresponding maximum powers generated by the PV array under different
combinations of solar irradiance patterns are shown in Table 4.2.
Table 4.2. The solar irradiance patterns for G1 to G4
Case
The solar irradiance
G1

Các file đính kèm theo tài liệu này:

- tom_tat_luan_an_research_and_development_a_solar_power_syste.pdf