| Cai Shibo (蔡世波),Tong Jianjun,Bao Guanjun,Pan Guobing,Zhang Libin,Xu Fang.[J].高技术通讯(英文),2016,22(3):305~312 |
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| Convex decomposition of concave clouds for the ultra-short-term power prediction of distributed photovoltaic system |
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| DOI：10.3772/j.issn.1006-6748.2016.03.010 |
| 中文关键词: |
| 英文关键词: distributed photovoltaic (PV) system， cloud features model， centroid point scattering model， convex decomposition |
| 基金项目: |
| Author Name | Affiliation | | Cai Shibo (蔡世波) | | | Tong Jianjun | | | Bao Guanjun | | | Pan Guobing | | | Zhang Libin | | | Xu Fang | |
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| 中文摘要: |
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| 英文摘要: |
| Concave clouds will cause miscalculation by the power prediction model based on cloud features for distributed photovoltaic (PV) plant. The algorithm for decomposing concave cloud into convex images is proposed. Adopting minimum polygonal approximation (MPP) to demonstrate the contour of concave cloud, cloud features are described and the subdivision lines of convex decomposition for the concave clouds are determined by the centroid point scattering model and centroid angle function, which realizes the convex decomposition of concave cloud. The result of MATLAB simulation indicates that the proposed algorithm can accurately detect cloud contour corners and recognize the concave points. The proposed decomposition algorithm has advantages of less time complexity and decomposition part numbers compared to traditional algorithms. So the established model can make the convex decomposition of complex concave clouds completely and quickly, which is available for the existing prediction algorithm for the ultra-short-term power output of distributed PV system based on the cloud features. |
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