Enhance Unobservable Solar Generation Estimation via Constructive Generative Adversarial Networks
Enhance Unobservable Solar Generation Estimation via Constructive Generative Adversarial Networks
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DOI：
10.1109/tpwrs.2023.3262773
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发表时间：
2024-01
影响因子：
6.6
通讯作者：
Jingyi Yuan;Yang Weng
中科院分区：
文献类型：
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作者：
Jingyi Yuan;Yang Weng
配电网在系统边缘经历了太阳能光伏（PV）的扩散。然而，其对应的稀疏电表部署对光伏电站的监控不足，潜在的违规行为对运营商的能源管理和稳定运行提出了挑战。以前的一些工作使用卫星图像来检测分布式pv，以便于获取数据。然而，它们的光伏定位方法依赖于具有单一背景/环境的标签丰富区域来实现；更困难的是，它们没有提供精确的计量光伏检测和量化来估计/了解不可观察区域的光伏发电输出，这对于防止边缘过度的双向功率流和其他违规行为至关重要。因此，我们将检测PV存在和量化PV量这两个步骤合并为一个分类任务。为了提高在不可观察边缘区域的分类性能，我们构建了一个生成式对抗网络，该网络同时增加了标记PV卫星图像的多样性，并嵌入了不同的PV特征/特征来训练分类器。此外，将光伏定位和量化结果与地理信息、历史天气条件和邻近发电模式相结合，估算系统边缘的功率输出。我们在美国西南部的光伏系统上验证了所提出的方法。实验结果表明，在没有足够先验信息的情况下，该方法预测分布式太阳能发电具有较高的准确性和鲁棒性。
Power distribution grids experiences 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 the easy access of data. However, their PV localization methods rely on label-rich area with 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 area, 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 quantify PV amount into one classification task. To boost the classification performance in unobservable edge area, we construct a generative adversarial network that simultaneous augments the diversity of labelled PV satellite images and embed 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.