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Recently, there has been a growing interest in automatically collecting distributed solar photovoltaic (PV) installation information in smart grid systems, including the quantity and locations of solar PV deployments, as well as their profiling information across a given geospatial region. Most recent approaches are still suffering low detection accuracy due to insufficient sample and principal feature learning when building their models and also separation of rooftop object segmentation and identification during their detection processes. In addition, they cannot report accurate multi-deployment results. To address these problems, we design a new system-SolarDetector+, which can automatically and accurately detect and profile distributed solar PV arrays without any extra cost. In essence, SolarDetector+first leverages multiple data augmentation techniques, including Cycle-Consistent Adversarial Networks, Latent Diffusion Models, and Generative Adversarial networks, to build a large rooftop satellite imagery dataset (RSID). Then, SolarDetector+employs Mask R-convolutional neural networks algorithm to accurately identify rooftop solar PV arrays and learn the detailed installation information for each solar PV array simultaneously. We find that pre-trained SolarDetector+yields an average Matthews correlation coefficient of 0.862 to detect solar PV arrays over RSID, which is ∼50% better than the most recent open source detection system—SolarFinder.
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Investigation of Wind-Induced Dynamic Effects on Rooftop Solar Arrays
The use of photovoltaic (PV) arrays as a source of renewable energy has become increasingly popular in the USA. Despite their wide usage, rooftop PV arrays are vulnerable to damage under strong winds. This can be attributed to the underestimation of peak wind loads on these systems where dynamic effects are unaccounted for. This study consists of investigating the wind-induced dynamic effects on rooftop PV arrays based on an experimental-numerical program and field calibration. Field measurements were conducted on a rooftop PV array at Central Washington University. Finite Element Modeling was performed to design the PV array model for experimental testing such that its dynamic properties are comparable to the in-situ array. Impact hammer and wind loading tests were carried out at the NHERI Wall of Wind Experimental Facility at Florida International University. The experimental tests were calibrated and validated based on the field measurements. Significant wind-induced vibrations were observed and their effect on the structure’s response was shown to increase with increasing wind speed.
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- Award ID(s):
- 1825908
- PAR ID:
- 10336024
- Date Published:
- Journal Name:
- 14th Americas Conference on Wind Engineering
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
- Conference Paper:
- The DOI is not currently available.
