Abstract
Wet dust on the Photovoltaic (PV) surface is a persistent problem that is merely considered for rooftop based PV cleaning under a high humid climate like Malaysia. This paper proposes an Automated Water Recycle (AWR) method encompassing a water recycling unit for rooftop PV cleaning with the aim to enhance the electrical performance. This study makes a major contribution by developing a new model to correlate output power (
) and dust-fall factor. For model validation, we conducted an experiment of taking one set of Monocrystalline PV (mono) on a
of medium luminance day. One mono module was cleaned by AWR - pressurized water sprayed through 11 small holes over its front surface, while the other module was left with no-cleaning. The dust-contaminated water was filtered and collected back to the tank for recycling process. The water loss per cleaning cycle was achieved 0.32%, which was normalized to net loss of 28.8% at a frequency of 1 cycle/day for 90 days of operation. We observed that
of no-cleaning PV was decreased by 29.44% than that of AWR method. From this experimental data also, a unique and more accurate model is created for
prediction, which is much simpler compared to multivariables equation. Our investigation offers important insights into the accuracy of this regression model demonstrated by
or a strong negative quadratic relationship between
and dust-fall. The cleaning of PV modules is expected to save significant energy to reduce the payback period.
) and dust-fall factor. For model validation, we conducted an experiment of taking one set of Monocrystalline PV (mono) on a
of medium luminance day. One mono module was cleaned by AWR - pressurized water sprayed through 11 small holes over its front surface, while the other module was left with no-cleaning. The dust-contaminated water was filtered and collected back to the tank for recycling process. The water loss per cleaning cycle was achieved 0.32%, which was normalized to net loss of 28.8% at a frequency of 1 cycle/day for 90 days of operation. We observed that
of no-cleaning PV was decreased by 29.44% than that of AWR method. From this experimental data also, a unique and more accurate model is created for
prediction, which is much simpler compared to multivariables equation. Our investigation offers important insights into the accuracy of this regression model demonstrated by
or a strong negative quadratic relationship between
and dust-fall. The cleaning of PV modules is expected to save significant energy to reduce the payback period.
| Original language | English |
|---|---|
| Article number | 321 |
| Journal | SN Applied Sciences |
| Volume | 4 |
| DOIs | |
| Publication status | Published - 31 Oct 2022 |
Keywords
- Photovoltaic cleaning
- Dust-fall
- Energy yield
- Rooftop photovoltaic
