Pattern Classification and PSO Optimal Weights Based Sky Images Cloud Motion Speed Calculation Method for Solar PV Power Forecasting

• Type: Article in International Scientific Journal
• Date: 2019

Authors

Zhao Zhen (Author) Other; Shuaijie Pang (Author) Other; Fei Wang (Author) Other; Kangping Li (Author) Other; Zhigang Li (Author) Other; Hui Ren (Author) Other; Miadreza Shafie-khah (Author) Other; João P. S. Catalão (Author) FEUP

Journal

Title: IEEE Transactions on Industry Applications

Vol. 55 No. 4

Pages: 3331-3342

ISSN: 0093-9994

Publisher: IEEE

Indexing

ISI Web of Knowledge - 84 Citations

Scopus - 102 Citations

INSPEC

Other information

Authenticus ID: P-00Q-TTR

DOI: 10.1109/tia.2019.2904927

Abstract (EN): The motion of cloud over a photovoltaic (PV) power station will directly cause the change of solar irradiance, which indirectly affects the prediction of minute-level PV power. Therefore, the calculation of cloud motion speed is very crucial for PV power forecasting. However, due to the influence of complex cloud motion process, it is very difficult to achieve accurate result using a single traditional algorithm. In order to improve the computation accuracy, a pattern classification and particle swarm optimization optimal weights based sky images cloud motion speed calculation method for solar PV power forecasting (PCPOW) is proposed. The method consists of two parts. First, we use a k-means clustering method and texture features based on a gray-level co-occurrence matrix to classify the clouds. Second, for different cloud classes, we build the corresponding combined calculation model to obtain cloud motion speed. Real data recorded at Yunnan Electric Power Research Institute are used for simulation; the results show that the cloud classification and optimal combination model are effective, and the PCPOW can improve the accuracy of displacement calculation.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 12