Analysis of Wind Data, Calculation of Energy Yield Potential, and Micrositing Application with WAsP

Topaloğlu, Fatih; Pehlivan, Hüseyin

doi:10.1155/2018/2716868

Cited by 10 publications

(6 citation statements)

References 8 publications

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“…The Logarithmic Function. If we assume the neutral wind profile at height Z y , then the difference in the surface roughness can be determined as follows [52]:…”

Section: Methods Of Extrapolating Weibull Parameters Thementioning

confidence: 99%

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

2021

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Background. Global warming is a growing threat in the world today mainly due to the emission of CO2 caused by the burning of fossil fuel. Consequently, countries are being forced to seek potential alternative sources of energy such as wind, solar, and photovoltaic among many others. However, the realization of their benefits is faced with challenges. Though wind stands a chance to solve this problem, the lack of adequate site profiles, long-term behavioural information, and specific data information that enables informed choice on site selection, turbine selection, and expected power output has remained a challenge to its exploitation. In this research, Weibull and Rayleigh models are adopted. Wind speeds were analyzed and characterized in the short term and then simulated for a long-term measured hourly series data of daily wind speeds at a height of 10 m. 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, and monthly variations. To verify the models, statistical tools of Chi square, RMSE, MBE, and correlational coefficient were applied. Also, the method of measure, correlate, and predict was adopted to check for the reliability of the data used. The wind speed frequency distribution at the height of 10 m was found to be 2.9 ms-1 with a standard deviation of 1.5. From the six months’ experiments, averages of wind speeds at hub heights of 10 m were calculated and found to be 1.7 m/s, 2.4 m/s, and 1.3 m/s, for Ikobe, Kisii University, and Nyamecheo stations, respectively. The wind power density of the region was found to be 29 W/m2. By a narrow margin, Rayleigh proves to be a better method over Weibull in predicting wind power density in the region. Wind speeds at the site are noted to be decreasing over the years. The region is shown as marginal on extrapolation to 30 m for wind energy generation hence adequate for nongrid connected electrical and mechanical applications. The strong correlation between the site wind profiles proves data reliability. The gradual decrease of wind power over the years calls for attention.

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“…The Logarithmic Function. If we assume the neutral wind profile at height Z y , then the difference in the surface roughness can be determined as follows [52]:…”

Section: Methods Of Extrapolating Weibull Parameters Thementioning

confidence: 99%

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

2021

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“…Based on (Gul et al, 2019;Mathew & Philip, 2011;Topaloǧlu & Pehlivan, 2018), these are determined by the following expressions:…”

Section: Weibull Distributionmentioning

confidence: 99%

“…A period of time T is considered and computed using Eq. ( 22), according to (Letcher, 2017;Ministerio de Electricidad y Energía Renovable, 2013;Topaloǧlu & Pehlivan, 2018).…”

Section: Energy Generatedmentioning

confidence: 99%

Parametric study of a horizontal axis wind turbine with similar characteristics to those of the Villonaco wind power plant

Sánchez

Hidalgo

Velasco

et al. 2021

J APPL RES TECH ENG

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<p class="JAREAbstract">The present paper focuses on the selection of parameters that maximize electrical energy production of a horizontal axis wind turbine using Python programming language. The study takes as reference turbines of Villonaco wind field in Ecuador. For this aim, the Blade Element Momentum (BEM) theory was implemented, to define rotor geometry and power curve. Furthermore, wind speeds were analyzed using the Weibull probability distribution and the most probable speed was 10.50 m/s. The results were compared with mean annual energy production of a Villonaco’s wind turbine to validate the model. Turbine height, rated wind speed and rotor radius were the selected parameters to determine the influence in generated energy. Individual increment in rotor radius and rated wind speed cause a significant increase in energy produced. While the increment in turbine’s height reduces energy generated by 0.88%.</p>

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“…Wind is a very powerful natural phenomenon carrying an enormous amount of energy. This energy can be converted into useful electrical energy using a wind turbine [12][13][14][15][16] . The amount of energy available in wind on a selected site can be calculated using:…”

Section: Wind Statistics and Economicsmentioning

confidence: 99%

Technical and economic prospect of wind energy at Lapaha, Tonga

Kumar

Tausinga

Kishore

et al. 2020

Resour Environ Econ

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The wind at 50 m above ground level (a.g.l) was measured for 22 months. The mean wind speed predicted by Wind Atlas Analysis and Application Program (WAsP) at Lapaha was 6.39 m/s and the power density were 279 W/m 2 . The prevailing wind direction at Lapaha site was East and Southeast direction with a low turbulence. The WAsP wind map indicated that Lapaha has a good wind potential for power production. A wind farm consisting of four Vergnet 275 kW wind turbines at Lapaha site is expected to pay itself back in 9 years with a Benefit to Cost Ratio (BCR) of 1.74, a Levelized Cost of Energy (LCoE) in Tongan Pa'anga (TOP) of 0.10/kWh compared to TOP 0.86/kWh presently charged to domestic consumers. The expected internal rate of return (IRR) would be 50% and with a life time of 25 years. The expected cost saved from the wind farm is TOP 1.3 million per year, which is equivalent to 1.3 million liters of diesel saved, resulting in 535 tons less CO 2 emitted annually. The proposed wind farm is expected to decrease diesel consumption by 20% annually.

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Analysis of Wind Data, Calculation of Energy Yield Potential, and Micrositing Application with WAsP

Cited by 10 publications

References 8 publications

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

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

Parametric study of a horizontal axis wind turbine with similar characteristics to those of the Villonaco wind power plant

Technical and economic prospect of wind energy at Lapaha, Tonga

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