Wind Resource Assessment and Availability Analysis Using Meteorological Data for Gombe Station, Nigeria
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325-335
Digital Object Identifier:
10.58578/ajstea.v4i3.9211
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Authors:Muhammad Basheer Hassan1*, Muhammad Nasir Umar2, Felix Wilfred Burari3, Usama Yusuf4, Abduqadir Ahmad5
Copyright :Authors retain copyright and grant the journal right of first publication.
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Abstract
This study investigates wind energy potential and availability in Gombe, Nigeria, using ten years of wind speed data from 2015 to 2024 obtained from the Nigerian Meteorological Station (NiMET). The objective was to analyze wind speed distribution, estimate wind power density, and determine wind availability at the station. The data were statistically examined using Weibull, Rayleigh, Normal, and Gamma probability distribution models to identify the model that best represents the observed wind characteristics. The Kolmogorov–Smirnov (KS) and Anderson–Darling (AD) goodness-of-fit tests were applied to validate model performance. The results indicate that the Gamma distribution provided the best fit, with a KS p-value of 0.44 and an AD statistic of 0.25, outperforming the other models. The Gamma distribution parameters were estimated at approximately α = 168.1 and θ = 0.0192, yielding a mean wind speed of 3.23 m/s and a standard deviation of 0.25 m/s. Based on the Gamma model, the mean wind power density (WPD) was estimated at 22.8 W/m², classifying Gombe as a low-to-moderate wind potential area suitable for small-scale or distributed wind energy applications. Wind availability analysis showed that wind speeds could support turbines operating at or above 50% of their rated capacity approximately 81.9% of the time when v₅₀ ≥ 3.0 m/s. However, turbines with v₅₀ ≥ 4.0 m/s exhibited negligible availability, indicating that only low-rated-speed and low-cut-in turbines are technically viable for the site. The study concludes that Gombe has stable and consistent moderate-speed wind conditions suitable for decentralized rural electrification and low-power applications such as water pumping and small hybrid systems. These findings contribute to wind resource assessment by demonstrating the importance of accurate statistical modeling, particularly the Gamma distribution, for characterizing low-to-moderate wind regimes and informing site-specific renewable energy planning.
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