Full Text Available
Note: Clicking the button above will open the full text document at the original institutional repository in a new window.
Effect of external factors on deterioration of photo voltaic panel's performance
Solar power is one of the most promising renewable energy resources. Thus, it has been under continuous research for improvement and enhancement. One type of solar power generators that has been appealing for the market is Photovoltaic solar cells (PVs). The performance of PV solar cells is effected...
Saved in:
| Main Author: | |
|---|---|
| Format: | Thesis |
| Published: |
AUC Knowledge Fountain
2018
|
| Subjects: | |
| Tags: |
No Tags, Be the first to tag this record!
|
| Summary: | Solar power is one of the most promising renewable energy resources. Thus, it has been under continuous research for improvement and enhancement. One type of solar power generators that has been appealing for the market is Photovoltaic solar cells (PVs). The performance of PV solar cells is effected by many factors. In a desert climate like Egypt, a major player in the performance is the sand or dust particles precipitation on the PV panels. This factor does not just affect the performance, but it also reflects on the utilizability of the power generation as a whole. In the present work field measurements of the performance of 4 different sets of identical PVs inclined at different tilt angles is presented. The measurements are conducted for many different days with a wide variety of clearness index and solar intensities, at the intervals of 15 seconds. Each set includes a PV module which was cleaned daily and two which were not cleaned, thus allowing dust accumulation to build up with time. The performance of the corresponding clean and soiled modules are compared. The study reveals that deterioration of performance of unclean (soiled) PVs is does not depend solely on the thickness of the dust layer alone, but rather by the interaction of the latter with other factors such as solar incidence angle, PV tilt angle and clearness Index. Finally the data is used to derive best fit regressive models for both daily energy performance and instantaneous performance. The models are based on true field measurements with all variable interactions present, rather than models which investigate the effect of a single variable under controlled lab conditions as common in previous investigations. |
|---|