Daniel Gfeller Jlcoweg 1 | 3400 Burgdorf | Switzerland www.pvtest.ch | iem.bfh.ch/photovoltaik | [email protected] ▶ Engineering and Information Technology ▶ Photovoltaic Laboratory Performance maintenance of large PV installations 14. Nationale Photovoltaik-Tagung, 22. - 23. Februar 2016, Bern Manuel Lanz, Prof. Urs Muntwyler Partners: Monitoring of large PV installations can be very labour intensive. A thermal imaging drone system can significantly increase the monitoring efficiency. A new system and unmanned aerial drone vehicle, developed and built at the Photovoltaic Laboratory (PV LAB) of Bern University of Applied Sciences BFH, provides evidence for efficiency advantages in the search for soiling effects on large PV installations. Unmanned aerial vehicle UAV The PV LAB drone system carries both (i) an infrared camera that records a thermal imaging video and (ii) a digital camera for comparison shots. Using this combination, PV module surfaces can quickly be monitored and defects easily determined and located. Fig. 1 shows the UAV ready to take-off. Quality control of PV installations Screening of PV installations in the monitoring network operated by the PV LAB at BFH shows that the UAV is able to detect power losses of about 5 Wp. Fig. 2 displays a IR video capture and a close-up thermal image of a defective Siemens M55 module. Another example is the “Stade de Suisse”, a PV installation with a total capacity of 1347 kWp. Cleaning effects were determined in summer 2015. The soiling effects were examined before and after cleaning the installation with the PV LAB thermal imaging drone. Again, the extra energy yield gained from cleaning the PV modules was determined by the difference in K G - values (Table 1). The degradation of the “Stade the Suisse” PV installation is displayed in Fig. 5. Cleaning the PV modules in 2015 (i.e., after 5 years of operation) increased the energy yield up to 4-5% (module inclination of 7 degrees). Cleaning the PV modules after 8 years of operation (also in 2015), increased the energy yield up to 6-9% (module inclination of 20.5). In conclusion, regular inspection with thermal imaging drones offers a real efficiency advantage when monitoring the improved energy yield production from PV due to cleaning. This PV LAB research is sponsored by: Fig. 3 illustrates the power loss (of about 20%) of the module in Fig. 2. Soiling effects on PV installations As evidenced by PV LAB studies (e.g., examination of the PV plant on the “Stade de Suisse”), regular cleaning of PV modules increases the electric energy production. As an example, the Siemens M55 PV module surface, a 50 kWp installation on the roof of the PV LAB building at BFH in Burgdorf, is cleaned every four years. This results in a yield increase of 5-8% (Fig. 4). The values in Fig. 4 are determined with the "generator correction factor" k G , which is formed by dividing the "array yield" Y A with the "temperature-corrected radiation yield" Y T (Formula 1) and thus represents the ratio of the actual yield to the theoretical yield. Fig. 1: PV LAB IR-multicopter drone; the take-off weight is about 7.5 kg. Fig 2: IR video capture, magnified (left) and close- up of marked module (right). Table 1: Increase of energy yield at “Stade de Suisse” after cleaning the PV modules. 65 70 75 80 85 90 95 100 05.06.2010 05.09.2010 05.12.2010 05.03.2011 05.06.2011 05.09.2011 05.12.2011 05.03.2012 05.06.2012 05.09.2012 05.12.2012 05.03.2013 05.06.2013 05.09.2013 05.12.2013 05.03.2014 05.06.2014 kG value [%] Date [DD.MM.YYYY] Degradation of k G values over time DI1 DI2 BI1 BI2 CI1 CI2 CI3 Fig. 5: Degradation of k G values (2010-14) of the “Stade de Suisse” PV installation. Subsystems Increase in % Notes/year BI1 4.0 ~4 2nd cleaning 10/15 BI2 6.4 ~6 2nd cleaning 10/15 CI1 5.7 ~6 2nd cleaning 10/15 CI2 4.3 ~4 2nd cleaning 10/15 CI3 4.3 ~4 2nd cleaning 10/15 DI1 5.6 ~6 2nd cleaning 10/15 DI2 6.4 ~6 2nd cleaning 10/15 E1 (AA1) 6.4 ~6 1st cleaning 07/15 E2 (AA2) 8.5 ~9 1st cleaning 07/15 F1 (DA1) 7.8 ~8 1st cleaning 07/15 F2 (DA2) 7.5 ~8 1st cleaning 07/15 Total 6.2 ~6 Fig. 3: Characteristics of the defective module (marked Siemens M55 module, Fig. 2) 0 5 10 15 20 25 30 0 0.5 1 1.5 2 2.5 3 0 5 10 15 20 25 Power [W] Current [A] Voltage [V] Siemens M55 Referenzmodul defektes M55 Modul Siemens M55 Referenzmodul (Leistung) defektes M55 Modul (Leistung) Fig. 4: Energy yield increase (1994-2007) of the PV installation at BFH Burgdorf due to cleaning. 0.70 0.75 0.80 0.85 0.90 0.95 1.00 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 Generator-correction factor k G = Y a / Y T Year PV installation Tiergarten West, BFH-TI, Burgdorf: History of generator-correction factor k G (April-September) Periode 1 Periode 2 Periode 3 Periode 4 Longer snow coverings (> 7 days) 1st cleaning 2nd cleaning Irradiation measured with Pyranometer 3rd cleaning Formula 1: Calculation of formula k G
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Daniel Gfeller
Jlcoweg 1 | 3400 Burgdorf | Switzerland
www.pvtest.ch | iem.bfh.ch/photovoltaik | [email protected]
▶ Engineering and Information Technology
▶ Photovoltaic Laboratory
Performance maintenance of
large PV installations
14. Nationale Photovoltaik-Tagung, 22. - 23. Februar 2016, Bern
Manuel Lanz, Prof. Urs Muntwyler
Partners:
Monitoring of large PV installations
can be very labour intensive. A
thermal imaging drone system can
significantly increase the monitoring
efficiency.
A new system and unmanned aerial
drone vehicle, developed and built at
the Photovoltaic Laboratory (PV LAB)
of Bern University of Applied Sciences
BFH, provides evidence for efficiency
advantages in the search for soiling
effects on large PV installations.
Unmanned aerial vehicle UAV
The PV LAB drone system carries both (i)
an infrared camera that records a
thermal imaging video and (ii) a digital
camera for comparison shots. Using this
combination, PV module surfaces can
quickly be monitored and defects easily
determined and located. Fig. 1 shows
the UAV ready to take-off.
Quality control of PV installations
Screening of PV installations in the
monitoring network operated by the PV
LAB at BFH shows that the UAV is able to
detect power losses of about 5 Wp.
Fig. 2 displays a IR video capture and a
close-up thermal image of a defective
Siemens M55 module.
Another example is the “Stade de
Suisse”, a PV installation with a total
capacity of 1347 kWp. Cleaning effects
were determined in summer 2015. The
soiling effects were examined before
and after cleaning the installation with
the PV LAB thermal imaging drone.
Again, the extra energy yield gained
from cleaning the PV modules was
determined by the difference in KG-
values (Table 1).
The degradation of the “Stade the
Suisse” PV installation is displayed in Fig.
5. Cleaning the PV modules in 2015 (i.e.,
after 5 years of operation) increased the
energy yield up to 4-5% (module
inclination of 7 degrees). Cleaning the
PV modules after 8 years of operation
(also in 2015), increased the energy
yield up to 6-9% (module inclination of
20.5).
In conclusion, regular inspection with
thermal imaging drones offers a real
efficiency advantage when monitoring
the improved energy yield production
from PV due to cleaning.
This PV LAB research is sponsored by:
Fig. 3 illustrates the power loss (of
about 20%) of the module in Fig. 2.
Soiling effects on PV installations
As evidenced by PV LAB studies (e.g.,
examination of the PV plant on the
“Stade de Suisse”), regular cleaning of PV
modules increases the electric energy
production. As an example, the Siemens
M55 PV module surface, a 50 kWp
installation on the roof of the PV LAB
building at BFH in Burgdorf, is cleaned
every four years. This results in a yield
increase of 5-8% (Fig. 4).
The values in Fig. 4 are determined with
the "generator correction factor" kG,
which is formed by dividing the "array
yield" YA with the "temperature-corrected
radiation yield" YT (Formula 1) and thus
represents the ratio of the actual yield to
the theoretical yield.
Fig. 1: PV LAB IR-multicopter drone; the take-off
weight is about 7.5 kg.
Fig 2: IR video capture, magnified (left) and close-
up of marked module (right).
Table 1: Increase of energy yield at “Stade de
Suisse” after cleaning the PV modules.
65
70
75
80
85
90
95
100
05.0
6.20
10
05.0
9.20
10
05.1
2.20
10
05.0
3.20
11
05.0
6.20
11
05.0
9.20
11
05.1
2.20
11
05.0
3.20
12
05.0
6.20
12
05.0
9.20
12
05.1
2.20
12
05.0
3.20
13
05.0
6.20
13
05.0
9.20
13
05.1
2.20
13
05.0
3.20
14
05.0
6.20
14
kG
valu
e [%
]
Date [DD.MM.YYYY]
Degradation of kG values over time
DI1
DI2
BI1
BI2
CI1
CI2
CI3
Fig. 5: Degradation of kG values (2010-14) of the
“Stade de Suisse” PV installation.
Subsystems Increase in % Notes/year
BI1 4.0 ~4 2nd cleaning 10/15
BI2 6.4 ~6 2nd cleaning 10/15
CI1 5.7 ~6 2nd cleaning 10/15
CI2 4.3 ~4 2nd cleaning 10/15
CI3 4.3 ~4 2nd cleaning 10/15
DI1 5.6 ~6 2nd cleaning 10/15
DI2 6.4 ~6 2nd cleaning 10/15
E1 (AA1) 6.4 ~6 1st cleaning 07/15
E2 (AA2) 8.5 ~9 1st cleaning 07/15
F1 (DA1) 7.8 ~8 1st cleaning 07/15
F2 (DA2) 7.5 ~8 1st cleaning 07/15
Total 6.2 ~6
Fig. 3: Characteristics of the defective module
(marked Siemens M55 module, Fig. 2)
0
5
10
15
20
25
30
0
0.5
1
1.5
2
2.5
3
0 5 10 15 20 25
Po
wer [W
]
Cu
rren
t [A
]
Voltage [V]
Siemens M55 Referenzmodul defektes M55 Modul
Siemens M55 Referenzmodul (Leistung) defektes M55 Modul (Leistung)
Fig. 4: Energy yield increase (1994-2007) of the PV
installation at BFH Burgdorf due to cleaning.
0.70
0.75
0.80
0.85
0.90
0.95
1.00
1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007
Ge
ne
rato
r-c
orr
ec
tio
n f
ac
tor
kG =
Ya / Y
T
Year
PV installation Tiergarten West, BFH-TI, Burgdorf: History of generator-correction factor kG (April-September)
Periode 1
Periode 2
Periode 3
Periode 4
Longer snow coverings (> 7 days)
1st cleaning
2nd cleaning
Irradiation measured with Pyranometer
3rd cleaning
Formula 1: Calculation of formula kG
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