Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Mean Wind Speed Comparison and Wind Farm Energy Prediction at Chang-Hua Offshore (Taiwan)

Version 1 : Received: 13 December 2016 / Approved: 13 December 2016 / Online: 13 December 2016 (10:01:29 CET)

How to cite: Barapati, M.; Miau, J.; Chang, P. Mean Wind Speed Comparison and Wind Farm Energy Prediction at Chang-Hua Offshore (Taiwan). Preprints 2016, 2016120069. https://doi.org/10.20944/preprints201612.0069.v1 Barapati, M.; Miau, J.; Chang, P. Mean Wind Speed Comparison and Wind Farm Energy Prediction at Chang-Hua Offshore (Taiwan). Preprints 2016, 2016120069. https://doi.org/10.20944/preprints201612.0069.v1

Abstract

Taiwan developing offshore wind power to promote green energy and self-electricity production. In this study, a Light Detection and Ranging (Lidar) was set up at Chang-Hua development zone one on the sea and 10km away from the seashore. At Lidar location, WRF (3.33km & 2km grid lengths) model and WAsP were used to simulate the wind speed at various elevations. Three days mean wind speed of simulated results were compared with Lidar data. From the four wind data sets, developed five different comparisons to find an error% and R-Squared values. Comparison between WAsP and Floating Lidar was shown good consistency. Lukang meteorological station 10 years wind observations at 5m height were used for wind farm energy predictions. The yearly variation of energy predictions of traditional and TGC wind farm layouts are compared under purely neutral and stable condition. The one-year cycle average surface heat flux over the Taiwan Strait is negative (-72.5 (W/m2) and 157.13 STD), which represents stable condition. At stable condition TGC (92.39%) and 600(92.44%), wind farms were shown higher efficiency. The Fuhai met mast wind data was used to estimate roughness length and power law exponent. The average roughness lengths are very small and unstable atmosphere.

Keywords

wind; wind farm; offshore; energy; lidar

Subject

Engineering, Energy and Fuel Technology

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