Solar Panel Identification Under Limited Labels

Luo, Shuman; Weng, Yang; Cook, Elizabeth M.; Trask, Robert; Blasch, Erik

doi:10.1109/pesgm48719.2022.9917215

Cited by 1 publication

(1 citation statement)

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“…Unlike the conventional energy source with scheduled power output, PV generation depends on various temporal and spatial factors, e.g., weather, atmospheric conditions, and installation position and quantity [2]. They naturally bring variability and uncertainty, leading to bidirectional power flow and frequent voltage fluctuations of voltages and currents in distribution grids [3], [4]. To maintain the safety and reliability of the grid, distribution system operators (DSOs) require accurate information on the solar panel locations and PV generation forecasts for system remodeling and predictive energy management [5]- [7].…”

Section: Introductionmentioning

confidence: 99%

Enhance Unobservable Solar Generation Estimation via Constructive Generative Adversarial Networks

Yuan

Weng

2024

IEEE Trans. Power Syst.

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Power distribution grids experience the proliferation of solar photovoltaics (PV) at the system edge. However, its counterpart of sparse meter deployment provides insufficient monitoring of PVs, for which the potential violations challenge the operators for energy management and stable operation. Some previous works use satellite imagery to detect distributed PVs for easy access to data. However, their PV localization methods rely on the label-rich area with a unitary background/environment to implement well; even further/harder, they do not provide precise metered-PV detection and quantification to estimate/know PV generation outputs in unobservable areas, which is essential to prevent the edge from excessive two-way power flow and other violations. Thus, we combine the two steps of detecting PV existence and quantifying PV amount into one classification task. To boost the classification performance in the unobservable edge area, we construct a generative adversarial network that simultaneously augments the diversity of labeled PV satellite images and embeds distinct PV characteristics/features for training the classifier. Furthermore, the PV localization and quantification result is combined with geographic information, historical weather conditions, and neighboring generation patterns to estimate power output at the system edge. We validate the proposed approaches on PV systems in the southwest of the U.S. Experiment results show high accuracy and robustness in predicting distributed solar power without sufficient prior information.

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