Rapid development of an FPGA Controller for a Wind
/ Photovoltaic Power System
A. Parera-Ruiz*, M.N. Cirstea*, Senior Member, IEEE, D.N. Ilea**
*Anglia Ruskin University, Cambridge, UK
**Transilvania University of Brasov, Romania
alberto.ruiz@student.anglia.ac.uk , marcian@ieee.org, ilea@leda.unitbv.ro
Abstract-The efficient control of Distributed Energy Resources
(DERs) is the key for the optimization of renewable energy
supplies used in stand-alone micro-generators.
A Wind / Photovoltaic combined power generator, with the
possibility to incorporate a hydrogen fuel cell as energy backup
or connect the system to the grid [2], in order to achieve reliable,
self-sufficient, continuous power to variable sources and loads, is
the structure tackled in this research project and the control of
the integrated Wind/PV system is the scope of this paper. The
research work takes advantage of the new generation of
Computer Aided Design (CAD) tools, able to generate a fast
hardware implementation from synthesizable high level
modelling code [3], [4]. The proposed system uses DK5, from
Agility Design Solutions [5], design and modelling software
environment based on Handel-C programming language. The
research led to the rapid development, prototyping and testing of
an FPGA based controller for the integrated renewable power
generation system.
I. INTRODUCTION
An increasing concern about climate change due to carbon
dioxide emissions, crude oil and gas conflicts that increase
fuel prices as well as the recent debate about the revival of
nuclear power stations is demanding researchers to develop
cost effective, efficient and reliable alternative energy
sources. The control of power converters is well known in
generators and electric motors applications; however, the
control of power converters in the emerging field of
Renewable Energies and Distributed Power Generation
Systems (DPGS) is still a field of study in the industry and in
the academic world [2]. Different areas need to be developed
and a range of subjects must be addressed in order to
effectively consider the DPGS as alternatives to conventional
fossil fuelled power stations and generators responsible for
the emission of carbon dioxide (CO2) and Greenhouse Gasses
(GHGs) causing global warming.
DERs (Distributed Energy Resources) are constituting an
increasingly appealing alternative [7], [8], [9] that provides a
significant and growing contribution to the grid as well as
being widely used as stand-alone generators for relatively
small power networks, for example those associated with
isolated networks, weak grids, island systems and a whole
range of small / medium power applications. The advances in
EDA (Electronic Design Automation) techniques and VLSI
(Very Large Scale Integration, millions of transistors on a
chip) technology in recent years has created the opportunity
for the development of complex and compact high
performance control configurations for power systems [6].
This paper presents a study of an integrated (holistic)
design, model and simulation of a Wind / Photovoltaic power
system controller, based on synthesisable digital hardware
from C-like languages and FPGA rapid prototyping. The use
of synthesisable C as opposed to Hardware Description
Languages (HDL), i.e. VHDL or Verilog, accelerates design
and the use of FPGAs provides the flexibility for designers to
re-program in the field to fix bugs. An integrated DER energy
system, combining wind and photovoltaic power as main
energy sources is the structure tackled by the paper. The
introduction of a Fuel Cell as energy back up in order to
supply continuous power to variable loads from variable
sources is covered in a separate paper [14].
The control of the integrated system is the key to optimising
its efficiency. In a first stage, the energy sources are modelled
using Matlab / Simulink software tools to analyse their
dynamic behaviour and in a second stage DK5 (Agility)
design and modelling environment based on Handel-C
programming language will be used. This is a design
environment that allows all aspects of the system
simultaneously to be considered, in particular maximising
operational performance to achieve energy efficiency and
power quality and fast implement of a hardware prototype of
an FPGA using an evaluation and development board from
Agility, RC100. The employment of FPGAs for
commissioning trials provides further benefits such as: rapid
prototyping, simple hardware / software design and compact
implementation. The complete digital controller circuit design
was synthesized, implemented and tested.
II. INTEGRATED SYSTEM
The research work is focused on photovoltaic and wind
power. These renewable sources are being used for the
majority of DERs installations. The integration of a Fuel Cell
or battery, although essential for this type of variable energy
systems, is not in the scope of this paper. A brief description
of wind and PV resources is presented below.
A. Wind Energy
Wind energy [11] is the Kinetic energy
1 2
mv
2
Joules
(1)
of a flow of air, where ρ is the air density, through a unit
area A perpendicular to the wind direction which is available
over a given period of time:
V=
1
ρAv∆tv 2
2
Joules
(2)
In the majority of systems, permanent magnet synchronous
generators are used to transform the mechanical energy into
electrical energy, variable in frequency - depending on wind
speed. The mathematical model equation for the mechanical
power generated from the wind can be expressed as
dV 1
(3)
= ρAv 3 W
dt
2
where the power in the wind, Pw , is the time derivative of the
PW =
kinetic energy. The basic system is shown in Fig. 1.
Fig. 1. Wind electric system.
The power in the wind, Pw, passes through the turbine to
generate mechanical power, Pm, at a turbine angular velocity
ωm. The fraction of power extracted from the power in the
wind by a practical wind turbine is usually given the
symbol C p , standing for the coefficient of performance.
Using this notation and dropping the subscripts of equation
(3), the actual mechanical power output can be written as:
⎛1
⎞
(4)
Pm = C p ⎜ ρ Av 3 ⎟ = C p Pw [W]
2
⎝
⎠
which is then supplied to the transmission. The transmission
output power Pt is given by the product of the turbine output
power Pm and the transmission efficiency ηm:
Pt = η m Pm [W]
(5)
B. Solar energy
The photovoltaic (PV) cells [10], as fundamental elements,
convert the solar light into electrical energy, having about
1W/cm² peak power for an output voltage of about 0.5V.
They are together connected into solar panels (units) to
provide higher power. The mathematical model expression is
based on curve shifting (translation) from one measured I-V
curve to a new one, under another set of solar irradiance and
solar cell temperature conditions, using algebraic equations
(7), (8) and (9):
I 2 = I 1 + ∆I sc
(7)
⎛ L2 ⎞
∆I sc = ⎜
− 1⎟ + α (T2 − T1 )
⎝ L1 ⎠
(8)
V2 = V1 − β (T2 − T1 ) − ∆I sc Rs − KI 2 (T2 − T1 )
(9)
Where:
I = current, A
I sc = short circuit current, A
α = current temperature coefficient, A/°C
β = voltage temperature coefficient, V/°C
K = curvature coefficient, Ω //°C
Rs = series resistance, Ω
V = voltage, V
L = solar intensity, W/m²
The curve translation method is easier to use, compared to
other mathematical models, because the constants
α , β , Rs and K are easier to determine from empirical
measurements. Fig. 3 illustrates the power of the PV system
depending on the irradiance. If the irradiance level of the
photovoltaic system is changed from the standard 1000W/m2,
then the C-V characteristic will change as shown in Fig. 3.
Finally, the equation of the generator output power, Pe, is
given by the product of the transmission output power and the
generator efficiency ηg:
Pe = η g Pt [W]
(6)
Fig. 2 shows the output power of a wind turbine versus
wind speed.
Fig. 3. Voltage/Current dependence on irradiance
C. Power system architecture
Fig. 2. Wind Speed/Power curves
The integrated system presented is based on a 2.5kW
micro-wind turbine and a 215W photovoltaic array, as main
energy sources. The following discussion, which concentrates
on the combination of these two sources, applies to any
combination of renewable energy resource. Thus, the control
and management strategies do not relate to this specific
combination only, but they are also relevant, after adjusting
the necessary parameters, to any other combination using the
same power conversion topology. Referring to Fig. 4, it can
be seen that each power source connects to the DC bus
through its own DC/DC [12] converter in parallel.
Two different type of DC/DC converters are used in this
integrated system. On the Wind Turbine power source, a
Buck converter (Fig. 5) is used to reduce the 48Vdc voltage
down to the DC bus voltage level. On the PV array, a Boost
DC/DC converter (Fig. 6) is used to increase the lower
voltage generated to match the level of the DC bus.
These converters are used in order to create a constant DC
voltage on their outputs regardless of the voltage variation on
their inputs. Allowing the voltage on the inputs of the DC/DC
converters to vary almost freely, gives more flexibility to the
control of the power sources. In the case of renewable power
sources in particular, the flexibility is very important because
it allows extracting the maximum available power in various
circumstances.
Fig. 4. Diagram of an Integrated Energy System
This approach uses Handel-C in the design development
stage and a Spartan3 (Xilinx) FPGA for the circuit
implementation. Systems combining renewable energy
sources such as Photovoltaic [10] (solar energy) and Wind
energy [11] with compensatory more stable sources such as
fuel cells [14] or electric batteries are the key to minimising
the disadvantages of renewable sources and extending their
applicability in the future. The converters are individually
controlled, but they also have to be supervised by an Energy
Management System as shown in Fig. 7.
This research work identifies appropriate separate
functional models to then merge into a combined integrated
model in a unique CAD evaluation environment (DK5,
Design Suite Agility). The aim is to explore the separate
modelling and simulation of power systems topologies
employing intelligent controllers, using Handel-C language
(Agility) and FPGA for rapid prototyping. Hardware
synthesis languages can create hardware from software
(Handel-C) to target a variety of technologies.
The holistic modelling, design and simulation is first done
in Matlab/Simulink, to study the dynamic behaviour. In a
second stage, the integrated system is co-simulated using
Matlab/Simulink and DK5/Handel-C, to study the dynamic
behaviour of the prototype controller. The actual design of the
complete control system is then synthesised and uploaded into
a Xilinx Spartan 3 3S1500L-4 FPGA for rapid prototyping.
FPGAs are customized by loading configuration data into
internal static memory cells; reprogramming is possible an
unlimited number of times.
Fig. 5. Step-down DC-DC Buck converter
III.MODELLING & DESIGN METHODOLOGY
The advances in VLSI technology enable the simultaneous
development, design and implementation of compact high
performance intelligent controllers for power systems. This
paper is based on the use of Electronic Design Automation
(EDA); therefore, the methodology followed is based upon
the usual approach enabled by the use of an EDA
environment for electronic system design and development.
The procedure includes the following stages:
- Investigation, identification and / or development of
mathematical models for renewable power systems and
associated controllers.
- The holistic behavioural modelling, simulation and
evaluation of the proposed controller against outlined
system requirements.
- Development of synthesisable Handel-C design code for
the controller components.
- Simulation & verification (timing analysis) of the design.
- Rapid hardware implementation of the system using an
FPGA development board.
Fig. 6. Step-up DC-DC Boost converter
Fig. 7. Diagram of an integrated Photovoltaic / Wind power generation
control topology.
FPGAs are ideal for shortening design and development
cycles and also offer a cost effective solution. Although
Handel-C is a ‘hardware synthesizable’ language and as such
is primarily used for digital circuit design, it has the
fundamental characteristics of any software programming
language. This allows complete power system modelling and
simulation, prior to digital controller design and
implementation.
IV. CONTROL SYSTEM ARCHITECTURE
The control and power management strategy followed in
this research programme is centralised on a FPGA controller.
Parallelism allows the management of various renewable
power sources at the same time, in order to achieve an
efficient, reliable, self-sufficient, continuous power to
variable loads.
PV and Wind energy sources are connected in parallel and
controlled by a common Energy Management FPGA
Controller through their individual DC/DC converters. The
power generated by these sources is given by look-up tables
for both sources, which simulate the voltage for the PV
generator and the current for the Wind Turbine. Even though
every source has its individual control in this system, all of
them share a similar configuration: a closed-loop feedback PI
system controls the constant voltage level required from the
load as well as the current through every individual PWM
controller for each converter. A similar structure is used for
the Buck and Boost converters, as seen in Fig. 8.
reference signal for the Pulse Width Modulator, to generate
the pulses (switch status) for the dc-dc converter.
V. CO-SIMULATION RESULTS
A useful feature of DK5, from Agility Design Solutions, is
the Co-simulation Manager (Fig. 9). Co-simulation Manager
allows easily implementing and running co-simulation
between a variety of different simulators. The Co-simulation
Manager does not alter data, or perform any algorithm in
itself, but it facilitates the data transfer between different
simulators. This allows creating a single system, with
different components created in the most appropriate
simulator.
In this research project, the control system was first
designed, modelled and simulated with Matlab/Simulink.
Afterwards, the controller was modelled with Handel-C
programming language to produce and FPGA prototype code.
Then, in order to test and simulate the Handel-C code
generated, a Simulink / DK5 co-simulation was used to test
and validate the precision and accuracy of the Handel-C code
previous to being synthesized on an FPGA.
In order to verify the correct operation of the proposed
integrated renewable energy system, PV and Wind sources,
were simulated using Matlab/Simulink to behave as if the
Wind source and the PV array worked separately and
simultaneously for different periods of time. Fig. 10 and Fig.
11 represent values of PV irradiance and wind speed for a
given period of time respectively. These signals were used
during DK5 / Simulink co-simulation.
Fig. 8. Common power control strategy of DC/DC converters for Wind and
Photovoltaic Sources.
Fig. 10. Irradiance simulation signal for a Photovoltaic Array
Figure 9. Co-simulation Manager (Agility Design Solutions)
The wind turbine and the photovoltaic array voltage are
controlled based on the error signal. The error is fed into a
proportional integrator (PI) type controller, which provides
the reference current for the second PI that controls the
Fig. 11. Wind speed simulation signal for a Wind Turbine
Fig. 12, plot 1 and 2 represent Wind and PV current.
These two sources complement each other in the absence of
one of the renewable sources, to achieve a reliable, selfsufficient, continuous renewable distributed integrated PV /
Wind power system. In plots 3 and 4, the constant 24Vdc
voltage is maintained throughout the whole time when any
one of the RES is present and generating enough power.
Fig. 13 illustrates the control performed by the FPGA
controller to maintain voltage and current combining
Photovoltaic and Wind Turbine power sources. Plot 1
represents the PI output (current control) to the PWM pulses
for the switch status on the Buck converter. As seen in the
wind speed simulation (Fig. 11), this source is generating
power throughout most of the simulation. Therefore, its
contribution is higher than the PV source (Fig. 13, plot 2).
Finally, plot 3 and 4 illustrate the switching of Buck and
Boost converters used on this system. It clearly shows how
both converters complement each other to achieve the
system’s load power demand.
VI. HARDWARE IMPLEMENTATION
AND HANDEL-C TESTING
Handel-C code (Fig. 14) was successfully co-simulated
and tested using DK5 Design Suite and Matlab/Simulink. The
control program running in parallel, enabled by the helpful
feature from higher level C-based languages for hardware
description, manages different energy sources simultaneously
in order to achieve a reliable, self-sufficient, continuous
power to variable sources and loads.
int 32 Err1, Err2, PIDout, Vfbk, Ifbk, Triwave, Iwind;
unsigned j=0;
while (1)
{
par
{
{
Vfbk= WindTurbine.input1_VLoad;
// Load Voltage
Ifbk= WindTrubine.input2_ILoad;
// Load Current
Iwind= WindTrubine.input3_I_WTurbine; // Wind Turbine Current
Iwind=Iwind*10000;
Err1=REFVOLTAGE-Vfbk;
// Voltage error
PIDout= PID (Err1, Iwind)
// Current reference
PIDout=PIDout/10000;
Err2=PIDout-Ifbk;
// Current error
PIDout= PID (Err2)
//PID output to generate
Output_pulses = PWM (PIDout, Triwave);// PWM pulses
}
{
fcount=fcount/10;
Triwave = triwave [j];
j++;
if (j>3)
{
j=0;
}
// Carrier wave running
// in parallel
}
}
Fig. 12. Load current result from the combination of PV and WT Current.
Fig. 14. DK5.0/Handel-C Code for energy management controller
This project, currently in its final stage of implementation,
is being tested using a RC100 evaluation board (Agility, Fig.
15). Handel-C code synthesized into hardware, using a Xilinx
Spartan 3 FPGA, generates the rapid prototype of a controller
for the renewable energy management system. The final code
for the controller, implemented onto the Spartan3 3S1500L-4
FPGA, has after compilation approximately 250K NAND
gates equivalent, 1k FFs and 128 memory bits (Fig. 16).
Fig. 13. Photovoltaic Array / Wind Turbine power control comparison
Fig. 15. RC100 Evaluation Board (Agility)
Fig. 16. DK5/Handel-C compilation report
Further testing in a lab environment is currently being
carried out on the test rig presented in Fig. 17. The testing
uses an oscilloscope for monitoring output pulses for the
power converters and a signal generator for Wind /
Photovoltaic power source emulation.
This work contributes to the large scale implementation of
Distributed Energy Resources (DER), energy storage
technologies and systems for grid connected applications and
the development of key enabling technologies for distributed
energy networks with high power quality and service security.
A high quality output power under variable loading
conditions for an autonomous system is another expected
advantage of the applications using such a controller.
REFERENCES
[1]
[2]
[3]
[4]
[5]
[6]
Figure 17. FPGA simulation and testing
VII. CONCLUSIONS
This research paper presents a wind-photovoltaic integrated
energy system controller, designed, modelled, simulated and
tested using the DK5 Design Suite (Agility) design and
modelling software environment based on Handel-C hardware
synthesis programming language.
The project achieves an integrated model of a distributed
renewable power generation system that allows rapid
prototyping of a global controller, targeted for FPGA
implementation using Handel-C and the real-time verification
RC100 (Agility) Spartan3 3S1500L-4 (Xilinx) FPGA
development board.
[7]
[8]
[9]
[10]
[11]
[12]
[13]
[14]
Blaabjerg, F, Teodorescu, R, Liserre, M and Timbus A V. 2006.
Overview of Control and Grid Synchronization for Distributed Power
Generation Systems. IEEE Transactions on Industrial Electronics. 53
(5), pp.1398-1409.
Carrasco, J M et al. 2006. Power-Electronic Systems for the Grid
Integration of Renewable Energy Sources: A Survey. IEEE
Transactions on Industrial Electronics. 53 (4), pp.1002-1016.
Edwards, S A. 2005. The Challenges of Hardware Synthesis from Clike Languages. In: Proceedings of Design Automation and Test in
Europe. Munich, Germany. March 2005. Vol. 1, pp. 66- 67.
Budiu, M and Goldstein, S C. 2002. Compiling Application-Specific
Hardware. In: Proceedings of the 12th International Conference on
Field Programmable Logic and Applications, FPL. September
2002.Montpellier, France, pp. 119-131.
Agility Design Solutions Inc: www.agilityds.com
Cirstea, M, Dinu, A, McCormick, M and Khor, J. 2002. Neural and
Fuzzy Logic Control of Drives and Power Systems. Oxford: Newnes.
Dettmer, R. Empowering Distributed Generation. The IEE Review,
January 2003.
Kennedy, M. Distributed Generation: Harder than it looks. The IEE:
Power Engineer, February 2003.
Fell, N. Distributing Power. The IEE: Power Engineer, June/July 2003.
Markvart, T. Solar Electricity. UNESCO energy engineering series,
Wiley, 2000.
Gipe, P. Wind Power, James & James, 2004.
Mohan, N. Undeland, T., Robbins, Power Electronics, Wiley, 2003.
Das, D, Esmaili, R, Xu, L, Nichols, D. An Optimal Design of a Grid
Connected Hybrid Wind / Photovoltaic / Fuel Cell System for
Distributed Energy Production. The 31st Annual Conference of the IEEE
Industrial Electronics Society, IECON, 2005, pp. 2499-2.
Petrinec, K, Cirstea, M N. 2006. Holistic Modelling of a Fuel Cell
Power System and FPGA Controller using Handel-C. In: Proceedings
of Annual Industrial Electronics Conference (IECON’06) of IEEE
Industrial Electronics Society, Paris, Nov. 2006, pp. 4951-4956.