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DE LA FACULTAD DE INGENIERÍA
REVIST
A TÉCNICAREVISTA TÉCNICA
“Buscar la verdad y aanzar
los valores transcendentales”,
misión de las universidades en
su artículo primero, inspirado
en los principios humanísticos.
Ley de Universidades 8 de
septiembre de 1970.
“Buscar la verdad y aanzar
los valores transcendentales”,
misión de las universidades en
su artículo primero, inspirado
en los principios humanísticos.
Ley de Universidades 8 de
septiembre de 1970.
VOLUME 43
SEPTEMBER - DECEMBER 2020
NUMBER 3
Rev. Téc. Ing. Univ. Zulia. Vol. 43, No. 3, 2020, September-December, pp. 114 - 176
Smart PV Solar Concentration Arrays for MPPT using FPGA
Technology
Sandoval-Ruiz, Cecilia E.
Facultad de Ingeniería, Universidad de Carabobo, Venezuela. cesandova@gmail.com
https://doi.org/10.22209/rt.v43n3a02
Received: 10/02/2020 | Accepted: 29/06/2020 | Available: 01/09/2020
Abstract
The present research includes the study of photovoltaic systems and current techniques for maximum power point
of actuator components in the arrangement solar monitoring and regenerative heat recovery circuits, applying neural
to generalize the optimization model on a parameterized ANN. The VHDL description of the architecture was made for
technical-environmental feasibility of the design. The results present an alternative technique based on self-similar circuits,
Keywords: photovoltaic systems; optical concentrators; maximum power point tracking; programmable gate arrangement
Arreglo Inteligente de Concentración Solar FV para MPPT
usando Tecnología FPGA
Resumen
La presente investigación comprende el estudio de los sistemas fotovoltaicos y las actuales técnicas para el
la complejidad computacional, procesamiento paralelo y factibilidad técnica-ambiental del diseño. Entre los resultados se
presenta una técnica alernativa, basada en circuitos auto-similares, con etapas de ganancia adaptativa, almacenamiento
Palabras Clave: sistemas fotovoltaico; concentradores ópticos; seguimiento de punto de máxima potencia; arreglo de
Rev. Téc. Ing. Univ. Zulia. Vol. 43, No. 3, 2020, 122-133
Rev. Téc. Ing. Univ. Zulia. Vol. 43, No. 3, 2020, September-December, pp. 114 - 176
123
MPPT para Sistemas Fotovoltaicos con FPGA
Introduction
conventional renewable energy systems (NCRE), in
relation to the energy density of the converters and the
other hybrid methods [1]. In this research we have
(ANN), arrangements with Linear Feedback Shift Register
(LFSR) structure applied in renewable energy [2-9] and
adaptive algorithms [10].
The study starts from the behavior of the system,
in order to detect technological gaps in the generalization
of optimization strategies based on the model. Analyzing
the dynamics of NCRE sources (based on intermittency),
they present challenges for their control [2-3], such as
computing capacity and concurrent processing, where
emerging as an alternative to solution to implement, in an
language (VHDL), which allows supporting circuit training
and dynamic adaptation.
optimization of functional stages is possible, in terms of
independent variables, such is the case of the irradiance,
power received per unit area, and temperature of the
modules, giving rise to combinations of optimization
methods [11-12]. In this area, the need for a mathematical
model has been detected that incorporates the system
parameters, describes the components and their behavior,
between each of the subsystems.
The importance of the proposed method is given
[13-14], with LFSR architecture. As well as the hardware
the synchronization between passive optimization
techniques: array interconnection, solar concentration,
hardware technology to the power system. In addition
in order to obtain the greatest incident solar radiation
on its surface. However, this scheme incorporates a set
of motors and moving elements to the arrangement that
increase its complexity and probable system failures.
Therefore, the study of alternatives is proposed, to
improve irradiance conditions, in order to extend the
of components) , whose function will be the directing
of solar radiation, towards the distributed photovoltaic
incorporation of thermal storage and transient storage in
ultra-condensers (considering the useful time of batteries
the models in stages.
Optical arrangement, this corresponds to the
mechanisms, luminescent solar concentrators (LSC) [17],
for each conversion element, multiplexed in space, or
or adaptive) on the input signal, that is, the effect of the
optical device on the path and magnitude of the incident
solar radiation.
An adaptive arrangement is proposed, in
correspondence with the concept of smart antennas [10],
which incorporate lenses with a selective function in two
stages: heliostatic targeting and panel concentration, sub-
range for energy contributions (see Table 1), which seeks
the correspondence of the model with the LFSR structure
w
UV
ultraviolet, w
FV
visible light, w
IR
which can then be generalized in the integrated model,
according to each of the components of the received
irradiance x
i
, for concentration / attenuation / collectors
function.
Photovoltaic Arrangement comprises the
Rev. Téc. Ing. Univ. Zulia. Vol. 43, No. 3, 2020, September-December, pp. 114 - 176
124
Sandoval-Ruiz
Table 1. Solar Energy and Storage
Radiación Technology Direct Storage Formula Wavelength
Ultraviolet
10,49 %
Sterilization, UV-C
Conservation and post-harvest
treatment of biomass
w
UV
∙
x
i
+
b
i
10 nm ≤ λ ≤ 400 nm
Photons
Visible Light
42,74 %
Photovoltaic Capacitors / Batteries
w
FV
∙
x
i
+
b
i
401 nm ≤ λ ≤ 750 nm
Photoelectric batteries
Separation of photoactive
chemical compounds
Chem photosynthesis Industrial plants (algae)
FVPGA synthesis HW program for photones
I.R.
46,77 %
Thermo-Solar
Thermal storage salts
w
iR
∙
x
i
+
b
i
751 nm ≤ λ ≤ 4000 nm
Thermo-Electric
losses due to the Joule effect and the effect of panels with
connects n
p
modules, due to the dynamic nature of this parameter,
depending on the conditions of the photovoltaic panels at
array (MCA). The photovoltaic modules will be responsible
incident radiation to electrical energy, which is associated
cells and panel properties, at the technology level [20].
Power Electronics Arrangement, consists of
electronic optimizers, DC-DC converters, transient storage
inverter modules. The electronic modules comprise the
levels of the system output, at optimal values, applying
external factors of temperature and irradiance, as shown
in the Figure 1.
(a) Temperature P-V (b) Irradiance P-V
(c) Temperature I-V (d) Irradiance I-V
Figure 1. Effect of environmental conditions on
photovoltaic conversion [21]
The curves show the relationship between the
independent variables: irradiance and temperature with
respect to the electrical parameters of the photovoltaic
panel: power, current and voltage, which represents an
input for the ANN training and optimization.
Energy Feedback Arrangements, these comprise
regenerative energy recovery subsystems, where the
feedback model for unconverted photons is proposed,
collectors and conversion using thermoelectric material,
The feedback residual energy can be managed through
concepts such as energy harvesting to power electronic
devices, applicable for the supply of the optimization
stage, maintaining sustainability and low environmental
coincides with the structure of the stages, where weighting
of contributions, intermediate storage of energy and
selective feedback by energy components are presented,
giving rise to the parameterizable descriptive equation.
Conceptual Development of the LFSR Model for Photo-
voltaic Systems
The incorporation of a satellite system (made up of
optimization of parameters (depending on topographic
on the distributed surface of the photovoltaic array, opti-
(see Figure 2).
Rev. Téc. Ing. Univ. Zulia. Vol. 43, No. 3, 2020, September-December, pp. 114 - 176
MPPT para Sistemas Fotovoltaicos con FPGA
Figure 2.
The proposed technique is based on an ANN for
From the inputs / outputs of the system: irradiance
S
x
(S
UV
, S
LV
, S
IR
),
current at the point of maximum power
I
MPP
V
MPP
, panel temperature T
P
and thermal
energy ET, applying convolutional neural network
centralized monitoring system control (driver_motors),
panel temperature control (forced ventilation), wasted
the performance of the photovoltaic array is presented
elsewhere [19]. The methods vary in their complexity,
necessary sensors, speed of convergence, effectiveness,
etc. in relation to the dynamics of the system, requiring
the highest possible power.
Table 2.
MPPT Control Technology Technical Description / PV Arrangement Solar Concentrator Ref.
FPGA – Control Method study using FPGA - [1]
ANN
High Concentrator Photovoltaic (HCPV)
on the module [22]
RTRL, O&P, Hybrids algorithm - [23-28]
Fuzzy Logic Three Stage / FPGA Controller - [29-30]
DCS Distributed photovoltaic modules - [31]
ANFIS – FPGA Neuro-adaptive systems of diffuse inference - [32-35]
This variation of load is controlled by a DC-DC con-
verter, which has the characteristic of raising or reducing
a voltage by modifying the duty cycle and the equivalent
load of the circuit, achieving such a load, that it consumes
it consumes the maximum power of the panel. Where a
-
pacitor.
temperature and equivalent load are related to the photo-
voltaic generator, a monitoring algorithm is required [11],
This is how, depending on the environmental conditions,
the connection or disconnection of panels would be done
using switches. In the optimization stage, one of the points
of interest is the power electronics responsible for cou-
pling the photovoltaic generator to the load. The booster
DC-DC converter can be controlled by current, voltage,
based on the useful cycle of operation or by magnetic con-
trol (MC), based on variable inductance.
Experimental
For the design of the hardware-based
optimization model, sustainability criteria are proposed,
in this sense, regenerative systems must be designed, fed
back with input into the energy budget, cycles of reuse,
products or energy, in correspondence with the circular
model. For this, a qualitative and quantitative analysis of
the optimization methods by stage is carried out in the
3.
Rev. Téc. Ing. Univ. Zulia. Vol. 43, No. 3, 2020, September-December, pp. 114 - 176
126
Sandoval-Ruiz
Table 3.
Optimization method Description of the Technological Concept
1
Concentration stage (optical) in the panel / stage prior to the photovoltaic panel
Solar radiation collectors Optical geometry elements [1],[36-37]
Solar Concentration Doping and LSC material properties [38-39]
Wave Transmission
Optical ber , Stokes Shift
Optical Capacitors Waveguide with light reection in the concentrator
2
Conversion Stage (Photovoltaic)
Photovoltaic cell technology
Selection of semiconductors, arrangement of the PV cells in the solar panel
or module, superposition of materials for optimization of efciency, as in the
case of spectral conversion [19]
Tandem arrangement Converter layers [20]
PERC Technology Insulating layer for sunlight reection on the panel
Supporting structure of the
modules
To increase the efciency, the amount of energy that reaches the photovoltaic
generator can be optimized using as supporting structures of the solar tracking
modules, with or without concentrating elements.
Storage technology
Storage in Ultra-Capacitors (UC) for energy management, MPP management
in synchronization with control logic.
Array Conguration
The selected topology for the array and its connection, xed or recongurable.
As well as the advances in R-IEDs in ERNC [4-5]
Panel cooling for temperature
effect compensation
TC Isc TC Voc Temperature effect compensation
0,044 % / °C -0,31 % / °C w
IT
* TC
ISC
* ΔT ,w
VT
* TC
VOC
* ΔT
Articial Neural Networks
Implementation of neural networks to control optimization parameters and
monitoring [2-5]
3
Signal Adaptation Stage (Power Optimizer)
MPPT search algorithm
Denition of the operating cycle of the DC-DC converter switch as a booster,
to establish the MPP impedance.
Power Optimizer (Digital Control
MPPT)
Module Level Power Electronics (MLPE), MPP detection on each PV panel,
increasing MPP accuracy and array efciency.
Ultra-capacitors Transient storage [19]
4
Inverter Stage (Power Electronics)
Investor Topology
Centralized inverters (string arrays), micro-inverters, power optimizers for
each photovoltaic module and central inverter
Investor Semiconductors Characteristics of signal switching switches for DC-AC converter
5
Mechanical Stage (monitoring of solar radiation)
Mechanism estimation Conguration Efciency Report
Arrangement Fixed Hor. Fixed Inc. HSAT VSAT HVSAT
Power (MWh) 69867,45 74818,52 95713,07 91271,15 105874,89
Plant Factor (%) 20 21 27 26 30
Rev. Téc. Ing. Univ. Zulia. Vol. 43, No. 3, 2020, September-December, pp. 114 - 176
127
MPPT para Sistemas Fotovoltaicos con FPGA
From the estimates obtained in the study of the
axis tracking (HSAT), vertical axis tracking (VSAT) or
two-axis tracking (HVSAT), applying an estimator [40],
it is observed the contribution of monitoring in the earth
station, at a cost of implementation of the set of drive motors
for the positioning of the panels in relation to the weight
of the mobile structure, in the matrix of np elements. So a
property between stages, which directs the radiation at a
certain height, with an optimal angle of incidence on the
The strategies for controlling the factors to be
).
Table 4.
Factor to optimize Optimization Techniques Technical Description
Description Hardware by
component
PV panel contamination Self-cleaning Electric shocks on the panel - coef. cleaning to 1.
Energy Density
Bifacial PV
It allows photons to enter the panel through
the back surface, where energy efciency
and density are increased, modeled as
feedback of reected energy.
y(t) <= wrf and y(t-1)_f
-- feedback
-- w_r (wrf,wrt,wre)
Retractable PV Smart surface adaptation to HSP
If HSP < opt then
-- code; end if
Thermal losses Regenerative heat
recovery
WHR integration and forced ventilation on
the back of the photovoltaic panel
w_T*TC_Isc*dT*I_pv
-- feedback Et(n-1)
y(t) <= wrt and y(t-1)_t
TiAE - Energy Amortization
Time
Panel Efciency
Optimization
Electronic control of the efciency of the
PV module
-- coef. PV
y(t) <= wpv * S(t)
HCPV concentration
Adaptable lenses formulated for maximum
concentration
-- coef. C
y(t) <= wc * S(t)
PV Array Power Losses Power Optimizers
Micro-Inverters and Power Optimizer
Technologies
-- MPPT
y(t) <= wp * Impp(t)
Electromagnetic radiation
capture (in this case solar)
Smart Beamforming
& Fractal Geometry
Antennas
Neural networks applied to the efcient
uptake and monitoring of the radiation
pattern, fractal schemes with line of sight
-- adaptive algorithm
w(n+1)
<=w(n)+u*s(n)*e(n); ---
optimal tracking
y(n) <= w(n) * s(n);
Permanent Electronics
(Programmed Obsolescence)
Application of a power
optimizer in VHDL
Update in time of the optimizer HW, on
FPGA technologies
-- Self Generation
For i in 0 to m generate
-- descriptive equation
Static Arrangements / Dynamic
Fitting for max. performance
Neural networks in
intelligent PVS design and
recongurable array
Component of a recongurable
(multiplexed) matrix based on FPGA
technology, with LFSR architecture
If Impp < cond then
-- Assign settings
array_PV <= matriz (i);
end if;
Static technology, with recycling
limitations of PV modules
Encapsulated modules
with access to inner layers
Additive manufacturing, 3D machining,
easily replaceable components, recycling
of insertable replacement layers.
-- Recongurable
hardware (HW)
The modeling was carried out through the de-
make adjustments to the mathematical model, through
the energy balance:
(1)
Where w
c
optimization model (concentration, angle of incidence),
w
PV
b
s
selective enablement, E
c
(n-1) the feed-
back energy: E
P
(n-1)E
T
(n-1) residual
thermal energy, with w
WHR
as feedback gain to the con-
version system. Thermal effects can be compensated: w
T
(1+TC
ISC
∙ΔT)∙I
FV
, with current proportional to irradiance
conversion, wT
Rev. Téc. Ing. Univ. Zulia. Vol. 43, No. 3, 2020, September-December, pp. 114 - 176
Sandoval-Ruiz
Table 5.
Application VHDL Hardware Description Codes
Refrigeration Control
If t_ref < ANN_WHR then
Bomba <= ‘1’; else Bomba <= ‘0’; end if;
-- Implementation of the WHR waste heat recovery unit.
MPPT algorithm
dI_mpp <= I_mpp – I_mpp1; dV_mpp <= V_mpp – V_mpp1
if clk =’1’ and clk’event then I_mpp1 <= I_mpp; V_mpp1 <= V_mpp; end if;
P_mpp <= I_mpp * V_mpp; -- P_mpp > P_mpp1
-- Power balance considering thermal effect:
P <= FF *((1+TC_ISC*ΔT) * I_PV)*((1+TC_VOC*ΔT) * V_OC);
if ANN_mppt = ‘1’ then s1<= ‘1’; else s1 <= ‘0’; end if;
Drive Control
-- Denes the direction of rotation of the mechanical solar tracking step motor.
If ANN_SGM1= ‘0’ then
For i in 1 to 4 loop driver_m1 (i-1) <= ‘0’; driver_m1 (i) <= ‘1’ after 10ms; end loop;
else
For i in 4 downto 1 loop driver_m1 (i+1) <= ‘0’; driver_m1 (i) <= ‘1’ after 10ms;
end loop; end if; -- was dened with Eq. LFSR selective drive mode
-- ∑wi * xi --> GF(m): yn
<= w(i) and x(i) or w(i-1) and x(i)… xor y(i-1)
In this way, the control is established from the
of the array, control signals of the motors of the solar
tracking mechanism (height, elevation and azimuth angles,
method has been selected, the hardware description
techniques are applied to manage the optimization
the circuits. In this way, the mathematical structure for
the systematized descriptions of the optimizer ANN is
standardized. Finally, the synthesis of intelligent electronic
Results and Discussion
From the analysis of the methods, complexity
of implementation and factorization of the optimization
protect the surface of this type of waves, which can have
wide application in the protection of fauna, glaciers and
forest environments. All of the formers with the purpose
of establish optimal parameters of height, concentration
by the ANN. This intelligent arrangement allows us to
simplify the solar tracking module (reduction of the
number of actuators in the arrangement), representing
a technological innovation. The proposed technique is
based on the developed model (see Table 6) and includes
of spectral concentration of visible light, conversion stage,
stage of power electronics, energy harvesting and storage,
structure for the stages studied, a regenerative system
parametric description, using the synthesis tool for VHDL
[41] and the instantiation of the neural components (see
Table 7).
In the VHDL description, the ANN has been pa-
rameterized, where the synaptic weights correspond to
learning rate, synaptic weights) are established according
to the system, environmental optimization criteria and
monitoring targets (based on a set of training, validation
-
Rev. Téc. Ing. Univ. Zulia. Vol. 43, No. 3, 2020, September-December, pp. 114 - 176
129
MPPT para Sistemas Fotovoltaicos con FPGA
Table 6.
Technical characteristics Innovation Achievements Reasons for its implementation
Hardware Oriented Synthesizable over FPGA technology Design portability
Multi-stage model
(parameterizable)
Variables of the various stages are included
for the MPPT
Optimization coefcients are dened at each stage
Generating equation Systematization of the description Parameterizable for particular systems
Commutability
Simplication of components and actuator
elements
Management of terms by incidence factor on the
arrangement.
Scalability
Ability to include modules with the same
LFSR structure
At custom optimizations can be implemented
Flexibility
Updatable and reusable components are
described.
Increased lifetime of hardware designs
Dynamic reconguration Modular / differential for stage update Distributed PVS support and model extrapolation
ANN-MPPT
Adaptation of model coefcients (considering
the panel efciency curves)
The high demand for computation of control
in NCRE by the system dynamics, requires the
optimization of the sequential algorithm disturbs and
observes for MPPT
Correspondence
Identication of LFSR structure in the PVS
and neural optimizer, where the ANN weights
have physical signicance in the optimization
coefcients.
Table 7. Description of the parameterized ANN model
ANN Hardware Description Codes in VHDL ANN Training
ANNF: For k in 1 to 3 generate -- - generation the layers of the ANN
LAYER1: For i in 0 to np| if k= 1 generate -- Layer 1
N1: neurona_layer1 port map (xn,w1(i),b1(i),s1(i)); -- instantiation n1
-- sn <= wi1*xi1 +…+ win*xin + bi;
end generate LAYER1;
yn <= s1(var_in) & … & s1(5) & s1(4) & s1(3) & s1(2) & s1(1) & s1(0);
LAYER2: For i in 1 to np | if k= 2 generate -- Layer 2
N2: neurona_layer2 port map (sn,w2(i),b2(i),y2(i)); -- instantiation n2
end generate LAYER2;
ANN <= y2(señal_ctrl) &…& y2(2) & y2(1);
end generate ANNF;
type matriz_peso is array of (m downto 0)
of std_logic_vector (7 downto 0);
-- Adaptation of the weight matrix by
iteration:
if clk’event and clk =’1’ then
e(n) <= t(n) – y(n);
wi(n+1)
<= wi(n) + u* xi(n)*e(n);
Circuit
Slices 4-LUTs FF Factor ∙ parameterized units
ANN (var_in,np,señal_ctrl)
100 198 0 n_layer (neuron ∙ synaptic_weights ∙ m bits)
Adaptive algorithm 80 148 48 (n1 ∙ var_in + n2 ∙ signal_ctrl) ∙ m bits
Descriptive expression:
ANN layers / function Layer 2,3 / Optimization Layer 1 / Modeling PVS
CS elements of centralized monitoring on panel set n
p
Rev. Téc. Ing. Univ. Zulia. Vol. 43, No. 3, 2020, September-December, pp. 114 - 176
130
Sandoval-Ruiz
optical arrangement has been incorporated for the
maximum use of incident radiation S
x
, with wadap
recovery of residual energy from the panels:
With w
CS
tracker gain (for the m elements of the
proposed arrangement), w
MPP
dynamic optimizer gain:
x(t) total irradiance, w
R
feedback enabler of residual energy components y(t -1).
Highlighting that an operator can be an LFSR circuit with
the function concatenated in its structure:
As a result, the optimization model is obtained,
based on the LFSR scheme, which allows the dynamic
Figure 3).
Figure 3. LFSR model of optimization by solar
concentration arrangement
(2)
(3)
ANN optimization. Likewise, the correspondence
(electrical, thermal, photonic).
Table 8. LFSR Model Correspondence
LFSR Operation layers Storage Feedback
Fractal ANN Neuro-Operators TDL
y(t-1)
Optics Concentrator Gain Luminescence (Shift Stokes) Photon reection
Photovoltaic Tandem PV Current Shift / Residual Heat PERC / λ irradiance
electronics DSP / PWM UC ultra-capacitors Reg. Heat Recovery
Smart Grid IEDs- Converters Energy storage E. Reversible
CSR model COD / WHR τ (radiation delay) Optimization coefcients
system, and the generation of the code in VHDL. At the
same time ANN [13] learning transfer can be applied for
contributions to the model. The results achieved can be
systems in engineering, on hardware ANN formulation
development of technologies with minimal environmental
Conclusions
LFSR architecture and adaptive algorithms responsible
arrangement, which introduces an improvement in the
Rev. Téc. Ing. Univ. Zulia. Vol. 43, No. 3, 2020, September-December, pp. 114 - 176
131
MPPT para Sistemas Fotovoltaicos con FPGA
of residual energy components, to increase the total
performance, through the contribution of each stage .
advanced, within the framework of the dynamic update of
the hardware and incorporating digitalization techniques,
migration processes.
Likewise, the developed optimization model
the physical parameters, storage and selective feedback,
given its correspondence with the LFSR architecture,
by applying conventional adaptive algorithms or
designs of hardware-oriented approaches, for intelligent
management of the contribution of solar energy, as well
of the ANN model.
monitoring mechanism and combination of methods,
photovoltaic arrangement was proposed, in combination
with the optimization. Thus, the results achieved give rise
to new areas of development and competitive techniques
in smart grid applications and virtual power plants.
Being important the understanding in the
design concepts of aspects such as socio-environmental
feasibility, which incorporate the updating of human talent
areas of sustainable development, eco-responsibility with
fauna, habitat, materials and natural resources, optimal
use of infrastructure (for reconversion of conventional
technology for dynamic adaptation / updating over time
on the model developed.
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