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CYPRUS UNIVERSITY OF TECHNOLOGY DEPARTMENT OF ENVIRONMENTAL SCIENCE AND TECHNOLOGY Rogiros D. Tapakis and Alexandros G. Charalambides COMPUTATION OF CLOUD MOTION FOR SOLAR IRRADIANCE PREDICTION October 2014 Slide 2 Solar Irradiance Fluctuations 2/16 Slide 3 AIM 3/16 Short Term Forecast of Electricity Generation of PV Parks Equipment Date, time Clear Sky models Presence of Clouds obscuring the sun Cloud types Correlation to irradiance attenuation Cloud motion Cloud development in time Possible enhancement of Solar Irradiance due to clouds Overall Aim Optimisation of energy production balance including PV assets Higher Penetration of PVs into wholesale electricity markets Development of mature and reliable green electricity grids Slide 4 Laboratory Setup - Equipment 4/16 Meteorological Station Direct, Diffuse, Global Radiation Wind, temperature, humidity UV radiation Delta BF-5 Irradiance sensor Direct, Diffuse Radiation PV installations Large scale (150kW, 100kW) Small scale (3-7kW) Laboratory installations All sky cameras Orion StarShoot camera Custom Made (in Process) Wide angle cameras GoPro Hero 4 GoPro Hero 3 Nikon D3100 (18-55mm VR) Custom made mounting Integration on Sun tracker PV mounting Occulators Slide 5 Progress so far 5/16 Irradiance modelling Clear Sky model Novel proposed methodology for correlating Diffuse Fraction to Clearness index Slide 6 Progress so far 6/16 Correlation of Irradiance/temperature to the power output of 150kW PV Park R 2 = 0.982 RMSE= 14.11% MBE= -2.23% Slide 7 Progress so far 7/16 Cloud detection, Computation of Cloud trajectories Slide 8 Progress so far 8/16 Over-irradiance due to clouds around the sun Slide 9 Cloud Detection Near the sun Equipment 9/16 GoPro Hero 3 Mounted at the back row of the Photovoltaics Focused on the area near the sun 7 kWp Photovoltaic system Measurements of I,V,P at the DC and AC side Slide 10 Cloud Detection Near the sun HSB space 10/16 HueSaturationBrightness Slide 11 Cloud Detection Near the sun Cloud Mask 11/16 Slide 12 Trajectories Computation Optical Flow 12/16 Processing steps Mask of every sequential image Computation of optical flow vectors for each pixel using two sequential images Image segmentation in square blocks Motion vector smoothing using median filter Computation of a single motion vector per block Slide 13 Trajectories Computation Optical Flow 13/16 Slide 14 Trajectories Computation Optical Flow 14/16 Slide 15 Outcomes of the study The developed cloud mask detect clouds close to the sun The optical flow technique can compute the cloud motion and the development of the clouds in time The algorithm can detect multiple clouds in the same image Conclusions 15/16 Future Work Correlate the image of the sky pattern to the measurements of the PV power output Integrate the algorithm to images from all sky camera for computation of cloud motion vectors Develop a comprehensive algorithm for the short-term forecast of the solar electricity generation of the PV Slide 16 End of Presentation THANK YOU FOR YOUR ATTENTION Questions? 16/16