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A complex nonlinear model for a single-mesh helical gear train is developed by including a time-varying mesh stiffness, axial vibrations, torsional vibrations, shaft and bearing damping, generator back EMF (Electromotive Force) and gear backlashes. With the help of a time series and Fast Fourier Transform (FFT) frequency spectrum, the effects of these nonlinear terms on the wind turbine and generator rotational speeds are studied under different excitation conditions by numerically integrating the associated equations using the RK4 algorithm. Results show that for lightly damped oscillations, an extra harmonic is induced in the generator dynamics due to contributions from internal excitations. However, this extra vibration can be suppressed at higher damping. Big helical angles will generally induce heavy nonlinear vibrations in the turbine and generator; a smaller mesh frequency will induce extra noise in the generator; and the external excitation due to wind gust has a greater influence on the nonlinearity of the wind turbine dynamics as compared to the internal excitations due to static transmission errors, time-varying mesh stiffness and the generator back EMF.

For the purpose of providing cheap, affordable and reliable electrical energy to communities in need and power-up new industries and enterprises, a small onshore wind farm with an estimated capacity of 500 kW is designed and studied. With the help of a wind rose, wind resource map, and results from Weibull statistics, a potential site is selected and the wind farm is positioned along Cameroon’s coastline for maximum energy capture. Using the PARK model for wind turbine layout optimization, two different layout patterns (Checkers pattern and column pattern) are studied for the purpose of minimizing the wake effect and thereby, maximizing the output power from the farm. The Checkers model was found suitable as compared to the column model to be used on Grand Batanga, a small locality South of the city of Kribi, Cameroon. Keywords: Wind farm, Layout, Optimization

For the future installation of a wind farm in Cameroon, the wind energy potentials of three of Cameroon’s coastal cities (Kribi, Douala and Limbe) are assessed using NASA average monthly wind data for 31 years (1983–2013) and compared through Weibull statistics. The Weibull parameters are estimated by the method of maximum likelihood, the mean power densities, the maximum energy carrying wind speeds and the most probable wind speeds are also calculated and compared over these three cities. Finally, the cumulative wind speed distributions over the wet and dry seasons are also analyzed. The results show that the shape and scale parameters for Kribi, Douala and Limbe are 2.9 and 2.8, 3.9 and 1.8 and 3.08 and 2.58, respectively. The mean power densities through Weibull analysis for Kribi, Douala and Limbe are 33.7 W/m2, 8.0 W/m2 and 25.42 W/m2, respectively. Kribi’s most probable wind speed and maximum energy carrying wind speed was found to be 2.42 m/s and 3.35 m/s, 2.27 m/s and 3.03 m/s for Limbe and 1.67 m/s and 2.0 m/s for Douala, respectively. Analysis of the wind speed and hence power distribution over the wet and dry seasons shows that in the wet season, August is the windiest month for Douala and Limbe while September is the windiest month for Kribi while in the dry season, March is the windiest month for Douala and Limbe while February is the windiest month for Kribi. In terms of mean power density, most probable wind speed and wind speed carrying maximum energy, Kribi shows to be the best site for the installation of a wind farm. Generally, the wind speeds at all three locations seem quite low, average wind speeds of all the three studied locations fall below 4.0m/s which is far below the cut-in wind speed of many modern wind turbines. However we recommend the use of low cut-in speed wind turbines like the Savonius for stand alone low energy needs