Preprint Article Version 1 This version is not peer-reviewed

Evaluation of the Technical Wind Energy Potential of Kisii Region Based on the Weibull and Rayleigh Distribution Models

Version 1 : Received: 10 October 2018 / Approved: 12 October 2018 / Online: 12 October 2018 (05:13:33 CEST)

How to cite: Ongaki, L.N.; Maghanga, C.M...; Kerongo, J. Evaluation of the Technical Wind Energy Potential of Kisii Region Based on the Weibull and Rayleigh Distribution Models. Preprints 2018, 2018100256 (doi: 10.20944/preprints201810.0256.v1). Ongaki, L.N.; Maghanga, C.M...; Kerongo, J. Evaluation of the Technical Wind Energy Potential of Kisii Region Based on the Weibull and Rayleigh Distribution Models. Preprints 2018, 2018100256 (doi: 10.20944/preprints201810.0256.v1).

Abstract

The research sought to investigate the long term characteristics of wind in the Kisii region (elevation 1710m above sea level, 0.68oS, 34.79o E). Wind speeds were analyzed and characterized on short term (per month for a year) and then simulated for long term (ten years) measured hourly series data of daily wind speeds at a height of 10m. The analysis included daily wind data which was grouped into discrete data and then calculated to represent; the mean wind speed, diurnal variations, daily variations as well as the monthly variations. The wind speed frequency distribution at the height 10 m was found to be 2.9ms-1 with a standard deviation of 1.5. Based on the two month’s data that was extracted from the AcuRite 01024 Wireless Weather Stations with 5-in-1 Weather Sensor experiments set at three sites in the region, averages of wind speeds at hub heights of 10m and 13m were calculated and found to be 1.7m/s, 2.0m/s for Ikobe station, 2.4m/s, 2.8m/s for Kisii University stations, and 1.3m/s, 1.6m/s for Nyamecheo station respectively. Then extrapolation was done to determine average wind speeds at heights (20m, 30m, 50m, and 70m) which were found to be 85.55W/m2, 181.75W/m2, 470.4W/m2 and 879.9W/m2 respectively. The wind speed data was used statistically to model a Weibull probability density function and used to determine the power density for Kisii region.

Subject Areas

wind speed; wind power; scale factor and shape factor

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