**3.1. Development of geographic information system tools for advanced photovoltaic plant supervision and management**

The collection and compilation of a dataset of a number of PV power generation variables into a GIS tool for the easy visualization, location, and prediction of current problems in a PV power plant is of great worth. This involves the creation of a database and base map, and adequate procedures for systemizing data introduction and analysis, which in turn should lead to the simplification of the study of existing electrical and thermal defects.

Furthermore, the application of a GIS tool allows for the novel correlation of cell defects with not only power losses on the affected PV module but also with overall performance of the power plant.

The complete PV power plant should first be introduced into the GIS software (ESRI ArcGIS) through aerial orto-photography before geometrical parameters are projected. The geographical position of the PV serial strings and limitation of plots can thus be set.

**2.10. Defective bypass diode**

100 Solar Panels and Photovoltaic Materials

are adopted.

**2.11. Blue cells**

a 17% yield loss under certain conditions [27].

also include non-spatial data.

**plant supervision and management**

power plant.

**3. Energy geographic information system applications**

Bypass diodes reduce the effects of intermittent cloud cover and partial shading on power generation by limiting reverse voltage potentials [5]. Power output is decreased significantly without bypass diodes and partial shading may cause local overheating, hot spots, and damage [25]. A new bypass system has been designed [26] allowing significant hot spot temperature reduction in both partial and full shading conditions. It relies on a series-connected power metal oxide semiconductor field effect transistor (MOSFET) that subtracts part of the reverse voltage from the shaded solar cell, thus acting as a voltage divider. The authors claim that it would be possible to cool up to 24°C with respect to the case in which standard bypass diodes

This consists of the lightening of the dark blue tone of certain cells in the PV module. Some authors classify this as an esthetic defect, but others have noted that its appearance can cause

A geographic information system or GIS consists of a set of applications and programs that manage spatially referenced databases, which can be visualized through the use of maps [28]. It is a powerful and dynamic tool for the analysis of geographical and spatial data, which can

A correctly implemented GIS tool provides comprehensive analysis of an area for any activity that entails a spatial component, meaning that GIS technology has wide application in resource management and can be an important tool in any decision-making task with a spatial element. GIS can thus be found applied to the development of solar atlas, resource location tools, and so on. Some authors have used GIS together with global positioning systems (GPS) and unmanned

aerial vehicles (UAV) to propose efficient inspection and maintenance of PV plants [29].

**3.1. Development of geographic information system tools for advanced photovoltaic** 

lead to the simplification of the study of existing electrical and thermal defects.

The collection and compilation of a dataset of a number of PV power generation variables into a GIS tool for the easy visualization, location, and prediction of current problems in a PV power plant is of great worth. This involves the creation of a database and base map, and adequate procedures for systemizing data introduction and analysis, which in turn should

Furthermore, the application of a GIS tool allows for the novel correlation of cell defects with not only power losses on the affected PV module but also with overall performance of the Furthermore, it is necessary to identify the exact position within the panel of any possible faults that may appear. For this reason, split installation of photovoltaic panels and the thirds of cells within each panel are taken into account. The maps have been geo-referenced by assigning them projected coordinates ETRS89 UTM 30 N.

PV modules are identified by their rack number and by a code that includes the serial string and the relative position of the string within the series.

A systematic procedure needs to be set up to identify the geographical position of a defect within a panel. A photovoltaic module is made up of 180 thirds of cells so the following nomenclature is suggested. Each cell in the panel can be split into 6 columns (A–F) and 10 rows (0–9). Each cell is, in turn, split into three thirds (X, Y, or Z). In this way, the position of a defect can be indicated in the third of the cell where it is located by an alphanumerical code (e.g., 4EY). **Figure 5** shows such an identifying code applied to a panel.

With the graphical part of the GIS tool delimited and the identification procedure for each PV cell established, a geo-referenced database or geo-database can be implemented. The geodatabase consists of a set of various kinds of geographical datasets in a common file system folder. From this a comprehensive information model can be created to represent and manage all the geographical information related to the power plant. This information model is realized as a series of tables storing entity classes, raster datasets, and attributes.

The model can be divided into three sorts of information: measurements of electrical variables, graphic information (pictures and thermographs), and defects and their description. **Figure 6** shows the schematic diagram of the relational database that has been created. According to

**Figure 5.** Adopted nomenclature for PV defect identification.

Finally, graphic information can also be included in the form of front and rear photographs and thermographs of each analyzed module. Furthermore, diagrams and figures can also be stored such as the I-V curve obtained by a PV curve tracer. This information is recorded in the FIGURES table, which includes hyperlinks to files (FILE), the type of graphic information (TYPE OF FIGURE), and its quality (FIGURE QUALITY). The type of figure is also related to the TYPE OF FIGURE auxiliary table. Figure quality is especially pertinent in the case of thermographs and consists of a one-digit code with the same description as is found in the

Degradation Monitoring of Photovoltaic Plants: Advanced GIS Applications

http://dx.doi.org/10.5772/intechopen.75650

As shown in **Figure 7**, pictures, thermographs, and I-V curves are spatially referenced and associated with each module. This is extremely useful as it means graphic information can be spatially related to the measured attributes in each panel. Furthermore, all graphic informa-

Inspection entails the characterization of the performance of a PV module by measurement of its I-V curve within normal working conditions and its extrapolation to standardized standard test conditions (STC) conditions (cell temperature of 25°C, incident global irradiance of

There must be absolutely no shadow on the PV modules during this process, as it can cause irregular thermal areas leading to a misinterpretation of the results. Furthermore, windy conditions should be avoided as thermal exchange by convection may also cause a diffuse thermal image. The most favorable conditions for taking quality, representative thermal images is when the panel is working at maximum power, which generally occurs at noon with clear sky conditions [30]. This means that thermographs should be taken only when there is a minimum of 700 W/m<sup>2</sup>

of

103

tion is available both from the geo-database and through the GIS map.

previously described QUALITY auxiliary table.

, and air mass of 1.5).

**Figure 7.** Instantaneous access to a PV module frontal thermography.

1000 W/m<sup>2</sup>

**Figure 6.** Schematic diagram of the database organization and relations.

the three sorts of information, the model has been organized into four main tables and four additional tables. The PV MODULES main table includes information related to a given PV module, such as its identifier code (ID\_MOD) which is the primary key and uniquely identifies each record, the string series (SERIES), the inverter associated with the module (INVERTER), its relative position in the series (POSITION IN SERIES), its rack number (RACK NUMBER), and its manufacturing reference code (PV MODULE REFERENCE NUMBER).

The MEASUREMENTS main table includes the measured electrical variables that allow for analysis of the performance. Each measurement is assigned a unique identifier (ID\_MEASURE) that relates each measurement record to the PV module using the ID\_MOD. The date of the measurement (DATE) and the inspector who took the measurement (INSPECTOR) are also included. The installation date and factory settings of the module (FACTORY) can be included under the INSPECTOR setting. In order to assure an adequate analysis, the measurement quality (MEASURE QUALITY) is also included in the form of metadata, an eight-digit code described in the QUALITY auxiliary table. The quality code is a number between 0 and 9, that describes if the record is a calibration value (0), if the record has no validation (1), if the record has been checked for being in a suitable range (2), and so on. There is one digit for each measured electrical value.

The DEFECTS table records PV defects and faults. Each record shows the module affected (ID\_MOD), the location code of the cell (CELL), which third of the cell is affected (POSITION IN CELL) along with the date (DATE), inspector recording the defect (INSPECTOR), type of defect (TYPE), and defect evolution within the module (EVOLUTION). The DEFECT TYPE auxiliary table records identifiers for the type of defect found in the form of a digit—0: snail track, 1: cell crack, 2: hot spot, 3: busbar discoloration, 4: cell discoloration, 5: EVA discoloration, 6: blue cell, and 7: other. The EVOLUTION field in the DEFECTS table is associated with the EVOLUTION auxiliary table. The following descriptors show the defect evolution in the PV module in this table—0: new defect or not detected before, 1: already detected but remains the same as the previous inspection, 2: has increased from the previous inspection, and 3: has decreased from the previous inspection.

Finally, graphic information can also be included in the form of front and rear photographs and thermographs of each analyzed module. Furthermore, diagrams and figures can also be stored such as the I-V curve obtained by a PV curve tracer. This information is recorded in the FIGURES table, which includes hyperlinks to files (FILE), the type of graphic information (TYPE OF FIGURE), and its quality (FIGURE QUALITY). The type of figure is also related to the TYPE OF FIGURE auxiliary table. Figure quality is especially pertinent in the case of thermographs and consists of a one-digit code with the same description as is found in the previously described QUALITY auxiliary table.

As shown in **Figure 7**, pictures, thermographs, and I-V curves are spatially referenced and associated with each module. This is extremely useful as it means graphic information can be spatially related to the measured attributes in each panel. Furthermore, all graphic information is available both from the geo-database and through the GIS map.

Inspection entails the characterization of the performance of a PV module by measurement of its I-V curve within normal working conditions and its extrapolation to standardized standard test conditions (STC) conditions (cell temperature of 25°C, incident global irradiance of 1000 W/m<sup>2</sup> , and air mass of 1.5).

There must be absolutely no shadow on the PV modules during this process, as it can cause irregular thermal areas leading to a misinterpretation of the results. Furthermore, windy conditions should be avoided as thermal exchange by convection may also cause a diffuse thermal image.

The most favorable conditions for taking quality, representative thermal images is when the panel is working at maximum power, which generally occurs at noon with clear sky conditions [30]. This means that thermographs should be taken only when there is a minimum of 700 W/m<sup>2</sup> of

**Figure 7.** Instantaneous access to a PV module frontal thermography.

the three sorts of information, the model has been organized into four main tables and four additional tables. The PV MODULES main table includes information related to a given PV module, such as its identifier code (ID\_MOD) which is the primary key and uniquely identifies each record, the string series (SERIES), the inverter associated with the module (INVERTER), its relative position in the series (POSITION IN SERIES), its rack number (RACK NUMBER),

The MEASUREMENTS main table includes the measured electrical variables that allow for analysis of the performance. Each measurement is assigned a unique identifier (ID\_MEASURE) that relates each measurement record to the PV module using the ID\_MOD. The date of the measurement (DATE) and the inspector who took the measurement (INSPECTOR) are also included. The installation date and factory settings of the module (FACTORY) can be included under the INSPECTOR setting. In order to assure an adequate analysis, the measurement quality (MEASURE QUALITY) is also included in the form of metadata, an eight-digit code described in the QUALITY auxiliary table. The quality code is a number between 0 and 9, that describes if the record is a calibration value (0), if the record has no validation (1), if the record has been checked for being in a suitable range (2), and so on. There is one digit for each measured electrical value. The DEFECTS table records PV defects and faults. Each record shows the module affected (ID\_MOD), the location code of the cell (CELL), which third of the cell is affected (POSITION IN CELL) along with the date (DATE), inspector recording the defect (INSPECTOR), type of defect (TYPE), and defect evolution within the module (EVOLUTION). The DEFECT TYPE auxiliary table records identifiers for the type of defect found in the form of a digit—0: snail track, 1: cell crack, 2: hot spot, 3: busbar discoloration, 4: cell discoloration, 5: EVA discoloration, 6: blue cell, and 7: other. The EVOLUTION field in the DEFECTS table is associated with the EVOLUTION auxiliary table. The following descriptors show the defect evolution in the PV module in this table—0: new defect or not detected before, 1: already detected but remains the same as the previous inspection, 2: has increased from the previous inspection,

and its manufacturing reference code (PV MODULE REFERENCE NUMBER).

**Figure 6.** Schematic diagram of the database organization and relations.

102 Solar Panels and Photovoltaic Materials

and 3: has decreased from the previous inspection.

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 to the front part [31].

Once the electrical measurements, fault detection, and graphic information have been obtained, all data can be compiled into a complete project constituting a GIS.
