Abstract
Solar photovoltaic (PV) systems are widely used to mitigate greenhouse gases (GHG), due to their green renewable nature. However, environmental factors such as bird drops, shade, pollution, etc., accommodation on PV panels surface reduce photons transmission to PV cells, which results in lower energy yield and GHG mitigation potential of PV system. In this study, the PV system’s energy and GHG mitigation potential loss is investigated under environmental stresses. Defects/hotspots caused by the environment on PV panel surface have unknown occurrence frequency, time duration, and intensity and are highly variable from location to location. Therefore, different concentrations of defects are induced in a healthy 12 kWp PV system. Healthy PV system has the potential to avoid the burning of 3427.65 L of gasoline by 16,157.9 kWh green energy production per annum. However, in 1% and 20% defective systems, green energy potential reduces to 15,974.3 and 12,485.6 kWh per annum, respectively. It is equivalent to lesser evasion burning of 3388.70, and 2648.64 L of gasoline, respectively. A timely solution to defective panels can prevent losses in the PV system to ensure optimal performance.
Highlights
PV sysdefects/hotspots frequency, severity, and time are purely unpredictable, PV system output tems’ energy production potential and greenhouse gases (GHG) mitigation potential under different concenenergy with different percentages of defects is calculated for a year
Firstly a 12 kWp PV system output without any operational defects caused by any environmental stress or internal issues is calculated as a reference
1% loss is considered in the PV system for a year
Summary
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. The PV panel suffering due to environmental issues brought on by shadow, bird the PV systems in drops, the short term andinlong term, which performance, efficiency, dust, etc., results localized heating of PVincludes panels. PV panel issues such as bird drops, string, block, etc., have different concentrations onofPV defects and possible identification through visuals of IR images is provided in [12,20]. Birds’ drop image filter was binarized by calculating locally adaptiv was utilized for binarized defects investigation on solar PV panels surface. It is image threshold using local first-order image statistics around each pixel. These technique need careful selection, and results may greatly vary as a result
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