## 4 The Proposed Use Case: Data Model and Scalability Analysis
### Application Example
The power output of the inverter, in fact, strictly depends on the power input provided by the PV strings, which is determined by the amount of solar irradiance collected by the PV modules forming the strings. By using these data, the efficiency of the PV inverter can be determined by computing the ratio between the power input and output, respectively. Conversely, potential malfunctioning of PV modules can be detected by computing the overall efficiency of the PV strings, i.e., by computing the ratio between the power input at the inverter and the total solar radiation on the PV strings (which is equal to the amount of measured solar irradiance multiplied by the total surface of the array of strings). Other relevant information on the health of the system and equipment can be also derived by the analysis of the Maximum Power Point (MPP) voltage inputs, compared with the amount of measured solar irradiance. Figure 5: Map of the engineering campus of the University of Brescia. PV: Photovoltaic, POD: Point of Delivery, MV: Medium Voltage, LV: Low Voltage. The supervisory monitoring of the PV inverters installed on 64 kW PV systems of the engineering campus of the University of Brescia is here proposed as example of the application of the proposed LoRaWAN architecture. The PV system is installed on the main building located on the north-east area of the campus, and connected to the LV public distribution grid. The PV array of the system is formed by 279 modules (model Trina Solar, TSM-230 PC05), subdivided in 18 PV strings. The PV strings of the system are installed on three different pitches of the four stories building, with 91 modules facing South, 68 modules facing West, and 120 modules facing East. Three different solar irradiance sensors (model Si-RS485 TC-T) are installed on each pitch. The PV strings are connected to six PV inverters (model ABB Power-One Aurora PVI-12.5-OUTD-FS-IT), which form the power conversion stage of the PV system. Each inverter is equipped with two separated Maximum Power Point Trackers (MPPTs), each connected to a single string and to a parallel of strings, respectively, to balance PV array input of the power conversion stage. While the solar irradiance sensors are installed on the roof of the building, the PV inverters are installed in the electrical equipment room located in the basement of the building. It is worth to note that in both the mentioned locations (i.e., the roof and the basement) the WiFi infrastructure implemented inside the building facilities is not available. To allow the communication between the installed equipment and the main infrastructure of the building by using the proposed LoRaWAN architecture, the following experimental setup was deployed. Each of the solar irradiance sensors installed on the roof of the building was connected to a Raspberry Pi 3 device by means of a RS485 bus, and the data acquired by implementing the Modbus RTU protocol. Similarly, each of the PV inverter, communicating by using the proprietary Aurora protocol, was connected to an intelligent device (ABB PVI-RS485-MODBUS Converter), responsible for the conversion between the proprietary protocol and the Modbus RTU protocol. The latter was then connected to a Raspberry Pi 3 by means of a RS485 bus, and the data acquired by implementing the Modbus RTU protocol. Finally, each of the installed Raspberry Pi 3 was interconnected with a RN2483 LoRaWAN module. Examples of data collected by using the described LoRaWAN architecture are reported in Figure 6.