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Laboratoire
conventionné avec
l’Université de Toulouse
A new method for fault detection and identification
of shadows based on electrical signature of defectsM. BRESSAN1, Y. EL-BASRI1, C. ALONSO1,2
1 CNRS; LAAS; 7 Avenue du Colonel Roche, F-31077, TOULOUSE, FRANCE.2 UNIVERSITE DE TOULOUSE; UPS, INSA; LAAS; F-31400, TOULOUSE, FRANCE.
E-mail: [email protected], [email protected]
AbstractThe good diagnosis and monitoring of PV systems are key points to maximize PV production. The main objective of this work is todevelop a new tool capable of detecting faults of PV systems in real-time, with a particular care for the detection andidentification of shadows. The proposed detection method is based only on DC power measurements.
Introduction
The PV arrays may be completely or partially shaded, decreasing performances. The duration of the DC power drop is related tothe type of shadow on PV modules such as soiling, building environment, leaves, etc.
The aim of this work is to create a real-time fault detection by analyzing the DC power output and more particularly the impactof shadows. This detection method is designed to minimize the use of sensors due to economic constraints. The measurement ofoutput current and voltage of the PV array is carried out by a monitoring system. With these measurements, the method is ableto detect DC power losses and identify the nature of faults on PV arrays.
PV system description
Field Power PV modules
Inclination
Facade 38kWp Bi-glass 529Wp
65° South
Experimental roof + walls
“R+1”
35kWp TE2200 250Wp
0° to 90° South
Roof “R+2” 24kWp TE2200 250Wp
10° South
08:24 09:36 10:48 12:00 13:12 14:24 15:36 16:48 18:00 19:12 20:240
20
40
60
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DC
pow
er
(W)
(1) Reference
(2) Variable shadows (tree)
(3) Intermittent shadows (clouds)
(4) Fixed shadows (soiling)(1)
(2)
(3)
(4)
Time
DC power measuring box and DC power analysis
DC Voltage Input max 1500V
DC Current Input max 30A
Necessity to develop a DC power faultdetection in function of types of shadows onPV modules
1
DS2e-0190
Shadow source Table 1 : List of PV equipment of ADREAM platform
Figure 1 : ADREAM platform, LAAS-CNRS, Toulouse, France
Figure 2 : Schematic bloc of test bench for DC power measurement with measuring box Figure 3 : Analysis of the power behavior for each case of shadows
Table 2 : Maximum input specifications of the DC measuring box
Laboratoire
conventionné avec
l’Université de Toulouse
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0
1
Time (h)F
ixed
shadow
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10
20
14:00
30
DC
pow
er
sim
ula
tion
(W)
08:00 09:00 10:00 11:00 12:00 13:00 14:00 15:00 16:00 17:00 18:00 19:00 20:000
0.5
1
Fault
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0.5
1
Inte
rmitte
nt
shadow
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0.5
1
Variable
shadow
1 CNRS; LAAS; 7 Avenue du Colonel Roche, F-31077, TOULOUSE, FRANCE.2 UNIVERSITE DE TOULOUSE; UPS, INSA; LAAS; F-31400, TOULOUSE, FRANCE.
E-mail: [email protected], [email protected]
Fault detection algorithm
Experimental results
Module TE2200 Mono crystalline Pmpp VOC Isc Vmpp Impp
Value 250Wp 37.5V 8.8A 30.05V 8.4A
2
0P Pd P
P
Variable initialization
Fault == 0 or P > P0 x 0.9
P > 0
P0 = P P0 = P01
t = 0
yes
yes
yes
yes
yes
yes
yes
yes
no
no
no
no
no
no
no
no
0 1P Pd P
P
Start
P > P0 x 0.9
P=PMPP
P01 = P
dP< -0.1 and P > 5
Fault == 1
Fault = 1Fault = 0
t = 0 t = t + 1
10 min < t > 60 min
Variable = 1Variable = 0
0 < t > 10 min
c > 60 min
Intermittent = 0 Intermittent = 1
Fixed= 1 Fixed= 0
Step (2)
Step (3)
Step (1)
Simulation results
(a) DC power simulation on one day with two kinds of fault
(b) Fault detection without identifying its nature
(c) Identification by default that the shadow is intermittentand supposed to be present during a short time
(d) Identification of a variable shadow if the duration of thefailure is longer than 10 minutes
(e) Identification of a fixed shadow if the duration of thefailure is longer than 60 minutes
08:00 09:00 10:00 11:00 12:00 13:00 14:00 15:00 16:00 17:00 18:00 19:00 20:000
4080
120160200
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0.5
1
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0.5
1
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0.5
1
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0
1
Fix
ed
shadow
DC
pow
er
(W)
Fault
Inte
rmitte
nt
shadow
Variable
shadow
08:00 09:00 10:00 11:00 12:00 13:00 14:00 15:00 16:00 17:00 18:00 19:00 20:000
4080
120160200
08:00 09:00 10:00 11:00 12:00 13:00 14:00 15:00 16:00 17:00 18:00 19:00 20:000
0.5
1
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0.5
1
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0.5
1
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0.5
1
Fix
ed
shadow
DC
pow
er
(W)
Fault
Inte
rmitte
nt
shadow
Variable
shadow
(a)
(b)
(c)
(d)
(e)
Figure 4 : Flowchart for fault detection and identification of shadows
Figure 5 : Drop DC power analysis in simulation and fault detection
Figure 7 : Drop DC power output measurements and fault detection analysis for very cloudy weather (October 2014)
Figure 6 : DC power output measurements and fault detection analysis for a clean sky (October 2014)
Table 3 : TE2200 PV module specifications (TENESOL)
ConclusionPhotovoltaic facilities are most affected by shadows such as antennas, chimneys, trees, soiling, etc, especially in urban areas. Amethod of detecting, locating and identifying several types of shadows is given by DC power output analysis which allowsaccurately and timely maintenance of solar field. This method could inform PV users or PV manufacturers of the robustness of PVmodules against presence of shadows despite the protection of diodes included in the PV modules.
A new method for fault detection and identification
of shadows based on electrical signature of defectsM. BRESSAN1, Y. EL-BASRI1, C. ALONSO1,2