Optimization of distributed generation of renewable energy sources by intelligent techniques Marcello Pucci – Institute for Studies on Intelligent Systems for Automation (I.S.S.I.A), National Research Council, Palermo (Italy) Intelligent Analysis of Environmental Data (S4 ENVISA Workshop 2009)

1. Optimization of Distributed Generation of Renewable
Energy Sources by Intelligent Techniques
Marcello Pucci
ISSIA (Istituto di Studi sui Sistemi Intelligenti per l’Automazione) – CNR
Via Dante, 12 – 90100 Palermo
pucci@pa.issia.cnr.it

2. Outline:
-Introduction
-Photovoltaic Plants
-Structure of the system
-Addressed issues
-Fuzzy control of PV voltage
-Neural MPPT of PV plants
-Neural emulation of PV plants
-K-means clustering of PV data
-Partial shading effect in PV plants
-Wind Plants
-Structure of the system
-Control techniques of IM wind generators
-Neural MPPT of IM generators
-Conclusions

3. Introduction:
Among the main research topics there are:
- Control techniques of power converters for exploiting the best
dynamic performance of the sources, according to their
characteristics and nonlinearities.
- Design of power converter topologies suited for a multi-source
input from several renewable sources.
- Experimental emulation of the renewable sources to reproduce all
the operating working conditions, avoiding any potential damage
or risk.
-Optimal exploit of the input energy source by MPPT-like
(Maximum Power Point Tracking) techniques.
- Connection techniques of the converter to the electrical grid.
- Effective estimation of the producible energy from a PV plant.
- Study of the partial shading effects in PV plants.

4. Photo-voltaic plant – Structure of the system
In multi-string technology,
several strings of PV modules
with separate maximum power
point tracking (MPPT) systems,
represented by DC/DC boost
converters, are connected to a
common grid connected
inverter.
Since every string can be controlled individually, the overall
efficiency of the PV plant is increased compared to former
centralized technology. Moreover the enlargement and upgrade
of the plant is possible simply by plugging new strings, with their
own DC/DC converters, into the existing platform.

5. Photo-voltaic plant – Addressed issues
The main issues which have been faced up to are:
-Intelligent voltage control of the DC-DC boost power converter to
guarantee the stability of the system in all working conditions and
correspondigly achieve the best dynamic performance.
-Intelligent emulation of the PV panel characteristics, including
their dependence on the solar irradiation and temperature.
-Optimal exploitation of the solar energy by neural mapping of
the PV chacteristic integrated with a perturbe&observe
technique.
-Use of statistical tools in order to obtain an effective estimation
of the energy produced by a photovoltaic array.
-Study of partial shading effects on I-V curves in large
photovoltaic fields by a reduced number of irradiance sensors.

6. PV plant – Fuzzy control of PV voltage
The transfer function of the averaged small-signal model of the
PV array can be written as:
K V − dRCV +L
r L P −r +R
H )= 2
(s 0
K0 = 0
ξ= ω= d L
s +2 ω ω
ξ s+ 2 n CV
L P 2dL P ω
r CV −r L PV
d C

7. Photo-voltaic plant – Results
Transition of the operating point from the constant voltage region
at G=950 W/m2 to the constant current region at G=550 W/m2

8. Photo-voltaic plant – K-means clustering
The whole experimental data set (about 12000
couples of values of current and voltage,
between 100 and 1100 W/m2 and 20 and 50°C) Useful experimental data subset (8242
points) for accurate energy forecast
5.5
5
4.5
4 1
3.5
Cluster
3
Current [A]
2.5
2
2
3
1.5
0 0.2 0.4 0.6 0.8 1
1 Silhouette Value
0.5 Silhouette plot for k=3
0
225 230 235 240 245 250
Grid Voltage [V]
E = ∑V i i I ∆t = 755kWh
E G1 = ∑V I ∆t
all sampled data
i 1 i1
i =1÷8242
( Cluster 1)
E = ∑ V I ∆t = 745kWh
Cluster 1
i i

9. PV plant–Kriging estimation of the shadow
G ri d
1
2 G ri d Spatial shadowing
3 G ri d distribution (3D and
4 G ri d 2D) on the PV field
5 G ri d
6 G ri d
6
UPPER STRING
Kriging map of the 4
Kriging based
shadowing with one
Current [A]
Measurement based
sensor per module.
2
0
0 50 100 150 200 250 300 350 400
Voltage [V]
6
LOWER STRING
Kriging map of the 4 Kriging based
Current [A]
shadowing with the 25% 2
of sensors. Measurement based
0
0 50 100 150 200 250 300 350 400
Voltage [V]
I-V characteristics of the upper and lower strings in array 4

10. Photo-voltaic plant – Neural MPPT of PV plants
The Growing Neural Gas (GNG) neural network has been used to
obtain the estimation of the maximum power point on the basis
of the instantaneous measurements of voltage and current
supplied by the PV source; Starting from this point a variable step
P&O algorithm searches for and locks the maximum power
working point

11. Photo-voltaic plant – Results
Irradiance transition from 850 W/m2 to 550 W/m2.

12. Photo-voltaic plant – Neural emulation of PV
Model based emulators have the following drawbacks: 1) model
parameters should be well known, 2) the array model is highly
non-linear and complex, 3) the modeling cannot be extended to
other kinds of sources (Fuel Cells for example). Another approach
consists in determining a numerical model of the PV array, based
on experimental data processed by AI.

13. Photo-voltaic plant – Results

14. Wind plants: Structure of the system
Induction machine wind generators with back-to-back inverter
topology and vector control on both the machine side and grid
side inverters seem to be a very good solution to achieve high
performance in controlling the electromechanical power
conversion with minimum impact on the grid.

15. Wind plants: Neural MPPT of IM generators
The Growing Neural Gas neural network implements the inverse
turbine model; it outputs the estimated wind speed from the
actual machine torque and speed. On this basis and the
knowledge of the optimal tip speed ratio, the computation of the
optimal power reference speed is computed.

16. Wind plants: Experimental set-up

17. Wind plants: Results
Step variation of the wind speed from 4 m/s to 6 m/s

18. Conclusions:
-Several applications of artificial intelligence to distributed
generators have been done.
-A fuzzified adaptive PI controller for the DC-DC boost converter
of the PV plant has been devised which guarantees the best
dynamic performance and the stability the system in all the
working conditions.
-A neural MPPT technique, based on the Growing Neural Gas, has
been devised which permits the PV plant work always in the MPP
point, according to any solar irradiance and environmental
temperature variations. This method has been further integrated
with a perturbe&observe technique.
-A neural PV emulator, based on the Growing Neural Gas and a
controlled DC-DC buck converter, has been devised permitting to
simulate experimentally the behaviour of the PV array in all
working conditions.

19. Conclusions:
-Two intelligent techniques have been devised, respectively for
clustering significant data for energy production and for energy
forecast in partial shading conditions.
-A neural MPPT technique, based on the Growing Neural Gas, has
been devised which permits the Induction Generator wind plant
to work always in the MPP point, according to any variations of
the wind speed.

20. Thank you for your attention!