Defects Impact on PV System GHG Mitigation Potential and Climate Change

Ahmed, Waqas; Sheikh, Jamil Ahmed; Farjana, Shahjadi Hisan; Mahmud, Arafat

doi:10.3390/su13147793

Cited by 13 publications

(7 citation statements)

References 19 publications

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“…If such issues remain undetected and persist on the PV surface, these issues result in permanent failure/faults of PV panels [20]. Moreover, defects/hotspots formation on PV panels' occurrence frequency, duration of stay, and intensity are unpredictable and are purely natural parameters [21]. In addition, only a few defects are identifiable through naked eye/visual inspection, such as corrosion, burned and broken cells, bubbles, etc.…”

Section: Losses In Pv Systemmentioning

confidence: 99%

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Photovoltaic Panels Classification Using Isolated and Transfer Learned Deep Neural Models Using Infrared Thermographic Images

Ahmed

Hanif

Kallu

et al. 2021

Sensors

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Defective PV panels reduce the efficiency of the whole PV string, causing loss of investment by decreasing its efficiency and lifetime. In this study, firstly, an isolated convolution neural model (ICNM) was prepared from scratch to classify the infrared images of PV panels based on their health, i.e., healthy, hotspot, and faulty. The ICNM occupies the least memory, and it also has the simplest architecture, lowest execution time, and an accuracy of 96% compared to transfer learned pre-trained ShuffleNet, GoogleNet, and SqueezeNet models. Afterward, ICNM, based on its advantages, is reused through transfer learning to classify the defects of PV panels into five classes, i.e., bird drop, single, patchwork, horizontally aligned string, and block with 97.62% testing accuracy. This proposed approach can identify and classify the PV panels based on their health and defects faster with high accuracy and occupies the least amount of the system’s memory, resulting in savings in the PV investment.

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Section: Losses In Pv Systemmentioning

confidence: 99%

“…The defects-based dataset is imbalanced since defects occurrence and frequency is a natural parameter [21]. A new balanced augmented dataset of defects based on 275 images (with 20% of each class) is created using scaling and shifting (0-20 • ) with noise [39].…”

Section: Transfer Learning On Augmented Datasetmentioning

confidence: 99%

Photovoltaic Panels Classification Using Isolated and Transfer Learned Deep Neural Models Using Infrared Thermographic Images

Ahmed

Hanif

Kallu

et al. 2021

Sensors

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“…Li [88] investigated the techno-economic feasibility of a 1 MW grid-connected PV system in Jiangsu Province, China using RETScreen software. Ahmed et al [89] utilized RETScreen software to investigate the environmental issue's impact on the energy of PV system and GHG mitigation potential.…”

Section: Simulation Toolmentioning

confidence: 99%

Techno-Economic Feasibility of Grid-Connected Solar PV System at Near East University Hospital, Northern Cyprus

2021

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The growth of populations and economy in Northern Cyprus has led to continuing utilization of fossil fuels as the primary source of electricity, which will raise environmental pollution. Thus, utilizing renewable energy, particularly solar energy, might be a solution to minimize this issue. This paper presents the potential of grid-connected solar PV power generation at Near East University Hospital (NEU Hospital), one of the largest and leading medical facilities in Northern Cyprus, to meet the energy demand during the daytime to reduce energy bills. For this purpose, the first objective of the study is to evaluate the solar energy potential as a power source for the NEU Hospital based on four datasets (actual measurement, Satellite Application Facility on Climate Monitoring (CMSAF), Surface Radiation Data Set-Heliosat (SARAH), and ERA-5, produced by the European Centre for Medium-range Weather Forecast). The results showed that the solar resource of the selected location is categorized as excellent (class 5), that is, the global solar radiation is within the range of 1843.8–2035.9 kWH/m2. The second objective is to investigate the impact of orientation angles on PV output, capacity factor, economic feasibility indicators, and CO2 emissions by using different PV modules. The results are compared with optimum orientation angles found by Photovoltaic Geographical Information System (PVGIS) simulation software. This objective was achieved by using RETScreen Expert software. The results demonstrated that the highest performance of the proposed system was achieved for orientation angles of 180° (azimuth angle) and −35° (tilt angle). Consequently, it is recommended that orientation angles, PV modules, and market prices are considered to maximize energy production and reduce electricity production costs.

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“…However, extracting energy from solar radiation through PV panels is a challenging task since it is limited to multiple factors, which include PV material, geographical constraints, temperature, pollution, humidity, wind speed, cleanness, hotspots, and orientation angle of PV systems, etc. [3,6,[8][9][10][11][12]. For instance, pollution, i.e., dust accommodation on PV panels, causes losses in PV system output by limiting the PV panel's ability to absorb radiation [12][13][14].…”

Section: Introductionmentioning

confidence: 99%

“…[3,6,[8][9][10][11][12]. For instance, pollution, i.e., dust accommodation on PV panels, causes losses in PV system output by limiting the PV panel's ability to absorb radiation [12][13][14]. However, in the literature, there are different methods to increase the PV output, such as selection of appropriate absorbing material, Maximum power point tracking (MPPT), tracking, etc.…”

Section: Introductionmentioning

confidence: 99%

Impact of PV System Tracking on Energy Production and Climate Change

Ahmed¹,

Sheikh

Mahmud

2021

Energies

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Green energy by PV systems reduces the dependence on fossil fuel-based power plants. Maximizing green energy to meet the demand reduces the burden on conventional power plants, hence lesser burning and greenhouse gases (GHG) emissions. For this purpose, this study draws a relationship between tracking schemes of the PV systems to GHG mitigation potential. The best fit location for detailed analyses is selected among the 15 most populous cities of Australia. The solar radiation potential is increased to 7.78 kWh/m2/d through dual axes tracking compared to 7.54, 6.82, 5.94, 5.73 kWh/m2/d through the one axis, azimuth based, fixed-tilted, and fixed-horizontal surface schemes, respectively. Through the dual axes tracking scheme, a 1 MW PV system per annum energy output avoids the burning of 796,065.3 L of gasoline, 4308.7 barrels of crude oil which is equal to the mitigation of 1852.7 tCo2 equivalent GHGs. Concisely, the PV system, through its green energy output, can avoid the release of greenhouse gases from fossil-fuel plants to tackle climate change more effectively.

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Defects Impact on PV System GHG Mitigation Potential and Climate Change

Cited by 13 publications

References 19 publications

Photovoltaic Panels Classification Using Isolated and Transfer Learned Deep Neural Models Using Infrared Thermographic Images

Photovoltaic Panels Classification Using Isolated and Transfer Learned Deep Neural Models Using Infrared Thermographic Images

Techno-Economic Feasibility of Grid-Connected Solar PV System at Near East University Hospital, Northern Cyprus

Impact of PV System Tracking on Energy Production and Climate Change

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