The PV system is located on the rooftop of the UTAR Sg Long Campus. The orientation of the PV module is facing south with a tilting angle of 10°. The module temperature is measured by the temperature sensor placed at the centre of the PV module's rear surface. The data logger collected the module temperature at an interval of 4 seconds.
The weather station is also located on the rooftop of UTAR Sg Long Campus. The weather station measured meteorological data such as global tilted irradiance (GTI), wind speed and ambient temperature. The GTI is measured by a TBQ-2-B pyranometer. This meteorological data measured by the weather station are almost the same as the one experienced by the PV module as the weather station is located near the PV module. The weather station measures and collects these meteorological data at an interval of 1-minute.
Since the weather station collects the meteorological data at a 1-minute interval, only the first PV module temperature data of every minute was retrieved. This was done by using the slicing technique in Python. Figure 3.2 shows the portion of the module temperature dataset before and after the filtering process.
Figure 3.2: (A) Portion of the Original Data (B) Portion of the Filtered Data Moreover, there is time in advance or delay in the data of the module temperature due to the real-time clock (RTC) of the data logger cannot synchronise with the GPS time. As in-plane solar irradiance is proportional to the module temperature, the time in advance or delay was determined by plotting a double axis graph of global titled irradiance (GTI) and module temperature against time. The difference between the peak (either positive or negative) of the in-plane solar irradiance and module temperature is the amount of time delay. Figure 3.3 shows that the module temperature data on 28/01/2020 are advanced by 47 minutes, while Figure 3.4 shows the module temperature data after the shifting of data in advance by 47 minutes.
Figure 3.3: The Module Temperature Data On 28th January 2020 Is Advanced by 47 Minutes.
Figure 3.4: The Module Temperature Data After the Shifting of Data Advance by 47 Minutes.
Since there is a year of meteorological, module temperature, sun altitude angle and cosine sun incident angle data, an arbitrary 14 days' data were selected to be used in this project (13 days and one day's data were used to determine and analyse the coefficients for all models and case study to estimate the module temperature, respectively). The data from 21st January 2020 to 2nd February 2020 were used for scatter plots, while the data on 13th February 2020 were used for the case study to estimate the module temperature. These interested data were retrieved from the numerous data (whole year data) by using the "Pandas iloc" function in Python. Figure 3.5 shows the coding used to retrieve the meteorological data on 21st January 2020 from the year of data.
Figure 3.5: The Coding to Retrieve Meteorological Data from Numerous Data.
As discussed in Section 1.3, it is of great interest to include the sun incident angle while estimating the module operating temperature because solar irradiance is more precisely linked to the sun incident angle rather than time. It is crucial to determine the position of the sun before calculating the sun incident angle. The sun's position can be determined through the sun altitude and azimuth angle. Both of the angles were calculated by using Eqn. 2.8 and Eqn. 2.10 respectively.
When the sun altitude angle is less than zero, it indicates there is no sunlight (before sunrise and after sunset). A "NaN" value was assigned to the sun altitude and azimuth angle using Python when there is no sunlight (before sunrise and after sunset). Since only the data between the sunrise and sunset are interested as the PV module only harvests energy during this interval, therefore, the purposes of assigning a "NaN" value to the sun altitude and azimuth angle during nighttime and before dawn are to ease the calculation of the sun incident angle and give a tidy or better visualisation of the sun altitude and azimuth dataset. The sun incident angle was calculated by using Eqn. 2.14. The tilted angle of the PV module, in this project is equal to 10° as the tilting angle of the PV module for most of the installation in Malaysia is 5° to 10°. This sun incident angle was applied in the angle-specific Ross coefficient model and the details will be discussed in Section 3.3.
Since the meteorological and PV module temperature data for the year 2020 were used, therefore, these three parameters were calculated for every minute of the year 2020. After the calculation, the dataset of these three parameters was generated respectively. Figure 3.6 to Figure 3.8 show the portion of the sun altitude dataset, sun azimuth dataset and cosine sun incident angle dataset. Appendix A shows the python coding for the calculation of the parameters and generation of the datasets.
Figure 3.6: The Portion of the Sun Altitude Angle Dataset.
Figure 3.7: The Portion of the Sun Azimuth Angle Dataset.
Figure 3.8: The Portion of the Cosine Sun Incident Angle Dataset.
After that, all the interested meteorological, PV module temperature, cosine sun incident angle and sun altitude angle data were compiled together.
Figure 3.9 shows the portion of compiled data. The compiled data consists of module temperature, ambient temperature, GTI, wind speed, cosine sun incident angle, and sun altitude angle for a particular date and time.
Figure 3.9: The Portion of the Compiled Data.