Naslov
Image phase shift invariance based multi- transform-fusion method for cloud motion displacement calculation using sky images

Autori
Zhen ; Zhao, Xuan ; Zhiming, Wang ; Fei, Sun ; Rongfu ; Duić, Neven ; Jin ; Tao

Izvornik
Energy conversion and management (0196-8904) 197 (2019); 111853, 14

Vrsta, podvrsta i kategorija rada
Radovi u časopisima, članak, znanstveni

Ključne riječi
Solar PV power forecasting ; Sky image ; Image phase shift invariance ; Cloud motion displacement ; Fourier phase correlation theory

Sažetak
For minute time scale solar photovoltaic (PV) power forecasting, the motion of clouds over PV power plant mainly contribute to the fluctuant and intermittent nature of solar PV power output. Therefore, research on cloud motion displacement (CMD) calculation to realize cloud motion prediction is a key sub-process for minute time scale solar PV power forecasting approaches. Fourier phase correlation theory (FPCT) is widely applied in CMD calculation for its superiority of simplicity and less computation, then an improved algorithm based on image-phase-shift-invariance (IPSI) is proposed to reduce the outlier probability of CMD results. However, at present, the current IPSI algorithm still has limitations and cannot avoid the occurrence of outliers altogether. In this paper, we presented a novel method, termed IPSI based multi-transform-fusion (MTF) method, to further improve the effectiveness compared with traditional FPCT and affine transform based IPSI method. First, three image transform methods satisfying IPSI condition, respectively wavelet transform (WT), affine transform (AT), and convolution transform (CT), are explored. Then the information increment of the transformed sky images using the above three methods is analyzed, respectively. Second, we determine the suitable image transform method for IPSI algorithm under specific cloud condition according to the corresponding information increment. Third, an IPSI based MTF method for CMD calculation in sky images is proposed. The original sky images are transformed through WT, AT, and CT to generate multiple images that maintain the same object motion information, then calculate the CMDs in each generated image. Finally, we apply Gaussian distribution to fit the multiple CMD values and taking its mathematical expectation as final CMD result. Various experimental results in 4 different scenarios show that the performance of the proposed approach is better than FPCT, AT based IPSI, and OF method, by reducing plenty of CMD outliers, thus delivering greater accuracy and robustness.

Izvorni jezik
Engleski

Znanstvena područja
Strojarstvo

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