**4.2. Geographic information system applied to a 9 kWp PV power plant**

This is a home PV power plant operated by Himalaya Sol, a Spanish company. It consists of a fixed 9 kW peak power plant on the roof of a family home. It began operating in February 2017 and is made up of 36 GFM 220-250 monocrystalline silicon modules manufactured by Wuxi Guofei Green Energy Source Co. Ltd. The modules are fixed with a tilt of 32 degrees oriented to 6 degrees east and have a peak power of 250 W per unit. A P300 optimizer from Solar Edge is used to optimize power output given that the installation is affected by shadows due to the architectural configuration. The PV modules are arranged in 3 serial strings of 13, 13, and 10 modules, respectively.

Once again, it is considerably easier for staff to supervise and monitor the PV plant state. However, distribution of electrical parameters may be less significant as only 36 PV panels

**Figure 8.** Electrical parameter distributions: (a) STC peak power, (b) STC fill factor (c) max. power voltage, (d) max. power current, (e) open circuit voltage, (f) short circuit current, (g) serial resistance, (h) parallel resistance (108 kWp PV plant).

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Results for the GIS implementation in the 9 kWp home power plant do not have much relevance as of the moment. Due to the fact that the installation only recently started working,

are included in this installation.

**Figure 10** shows all electrical parameters for each PV module, while **Figure 11** shows the PV faults log.

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global irradiance on the horizontal surface. Furthermore, frontal and rear thermographs should be taken in order to minimize interference on the measurements due to reflections from the front of the PV module. However, special care needs to be taken when using rear thermographs for corrections, as temperatures may be higher due to a lack of thermal dissipation when compared

Once the electrical measurements, fault detection, and graphic information have been

The GIS tool was applied to two case studies in Spain. The first was a fixed 108 kW peak power PV plant connected to the grid, which has been operating since mid-2008. The second was a fixed domestic 9 kW peak power PV plant installed on the rooftop of a family home.

The first case study is a commercial plant with 100 kW nominal power on the inverters and 108 kW peak power on the PV modules, operated by the Spanish company Sobarriba Leon 0. The PV modules are installed on fixed structures, pointing south, and inclined at 28 degrees to the horizontal. The PV field consists of 432 GFM 220-250 monocrystalline 250 Wp silicon modules manufactured by Wuxi Guofei Green Energy Source Co. Ltd. They are organized in 24 strings with 18 modules on each string. There are six electrical protection boxes for the strings in total. **Figure 8** shows all electrical parameters for each PV module, while **Figure 9**

The effective application of the GIS tool allows the observation of all electrical parameters in a holistic way. As shown in **Figure 8(a)**, critical peak power performances can be easily detected among more than 400 PV modules. Other electrical parameters, such as fill factor, open circuit voltage, or short circuit current dispersion, can also be observed in the context of the facilities.

This is a home PV power plant operated by Himalaya Sol, a Spanish company. It consists of a fixed 9 kW peak power plant on the roof of a family home. It began operating in February 2017 and is made up of 36 GFM 220-250 monocrystalline silicon modules manufactured by Wuxi Guofei Green Energy Source Co. Ltd. The modules are fixed with a tilt of 32 degrees oriented to 6 degrees east and have a peak power of 250 W per unit. A P300 optimizer from Solar Edge is used to optimize power output given that the installation is affected by shadows due to the architectural configuration. The PV modules are arranged in 3 serial strings of 13,

**Figure 10** shows all electrical parameters for each PV module, while **Figure 11** shows the PV

obtained, all data can be compiled into a complete project constituting a GIS.

This installation has been in operation since the beginning of 2017.

**4.1. Geographic information system applied to a 108 kWp PV power plant**

**4.2. Geographic information system applied to a 9 kWp PV power plant**

to the front part [31].

104 Solar Panels and Photovoltaic Materials

**4. Examples**

shows the PV faults log.

13, and 10 modules, respectively.

faults log.

**Figure 8.** Electrical parameter distributions: (a) STC peak power, (b) STC fill factor (c) max. power voltage, (d) max. power current, (e) open circuit voltage, (f) short circuit current, (g) serial resistance, (h) parallel resistance (108 kWp PV plant).

Once again, it is considerably easier for staff to supervise and monitor the PV plant state. However, distribution of electrical parameters may be less significant as only 36 PV panels are included in this installation.

Results for the GIS implementation in the 9 kWp home power plant do not have much relevance as of the moment. Due to the fact that the installation only recently started working,

almost no degradation has yet been observed. However, **Figure 11(a)** and **(b)** show some snail tracks and cell discoloration that might lead to the modules needing future maintenance. The distribution of these defects does not appear homogeneous and seems to be concentrated at

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The GIS tool presented here has shown itself to be of great use when analyzing degradation effects on a PV field, the location of the most common PV defects, and their overall correlation with the plant. Although useful information was found in both case studies, the application

The GIS tool is extremely useful for supervising the degradation of electrical parameters in a power plant and the evolution and distribution of defects in a PV field. Researchers and maintainers are encouraged to use it on their installations and compare results. We will continue to add periodical measurements and inspections to the geo-database and the real degradation effects of the PV field will then be completely analyzed. Such analysis will lead to more eco-

A systematic organization and analysis of measurements thanks to the implementation of GIS applications not only allows preliminary preventive maintenance actions to be carried out, such as replacing damaged PV modules, redistributing PV modules according to their performance, and developing specific supervision, cleaning, and maintenance procedures for modules affected by PV faults, but also makes feasible the supervision of the degradation of electrical parameters in the power plant and the evolution and distribution of defects in the

This chapter has been published in open access thanks to the support and funding of the Laboratorio de Inspección Técnica de la Escuela de Minas (LITEM) and the University of León (Spain). The authors wish to thank everybody who contributed to the project, especially

of GIS to large plants seems to be more viable than for small installations.

**Figure 11.** PV faults and defects in the 9 kWp PV plant: (a) snail tracks (b) cell discolorations.

nomical and effective maintenance and replacement strategies.

string series extremes.

**5. Conclusions**

PV field.

**Acknowledgements**

**Figure 9.** PV faults and defects in the 108 kWp PV plant: (a) snail tracks (b) hot spots and burn marks.

**Figure 10.** Electrical parameter distributions: (a) STC peak power, (b) STC fill factor (c) max. power voltage, (d) max. power current, (e) open circuit voltage, (f) short circuit current, (g) serial resistance, (h) parallel resistance (9 kWp PV plant).

**Figure 11.** PV faults and defects in the 9 kWp PV plant: (a) snail tracks (b) cell discolorations.

almost no degradation has yet been observed. However, **Figure 11(a)** and **(b)** show some snail tracks and cell discoloration that might lead to the modules needing future maintenance. The distribution of these defects does not appear homogeneous and seems to be concentrated at string series extremes.
