Wind energy potential assessment of Cameroon’s coastal regions for the installation of an onshore wind farm

AbstractFor 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 need

INTELLIGENT PREDICTION OF LOAD-DEPENDENT POWER LOSS IN A WIND TURBINE GEARBOX: APPLICATION TO 3. 0 MW WIND TURBINES

This paper presents the load-dependent power loss in a wind turbine gearbox under real-time operating wind speed for three different oil formulations. The gear power loss was determined using mathematical models from the values of gear loss factors and specific film thickness experimentally determined by other researchers. The bearing power loss was determined using the new SKF calibrated model. Wind data from Bafoussam, a town in Cameroon was used to validate the model. A back propagation neural network with different numbers of hidden neurons was designed for power loss modeling and prediction. The achieved results reveal that the load-dependent power loss in a wind turbine gearbox is greatly influenced by wind speed and oil type. Finally, it is shown that the predictive performance of the neural network is also influenced by the number of neurons in the hidden layer.</jats:p

Atmospheric implications of aminomethylphosphonic acid promoted binary nucleation of water molecules

Aminomethylphosphonic acid (AMPA) is the main metabolite of glyphosate and phosphonate, the major constituents of herbicides and insecticides used nowadays in modern agriculture and treatment of environmental refuge and sewage. Through these activities, AMPA is released into the atmosphere which can result in water molecule adsorption around AMPA. The DFT method through the APF-D/6–31++G(d,p) was used throughout the work to cluster one to ten water molecules around AMPA and to find their concentrations in atmosphere along with climate forcing. It comes to light that, the binding energies of the complexes AMPA(H2O)n = 1–10 increase upon addition of H2O. The binding energy (ΔE) per H2O is approximatively -55.7 kJ/mol for n = 1 – 5 and -52.7 kJ/mol for n = 6 – 10. Likewise, the Gibbs free energy per H2O averages within the same ranges at -19.0 and -13.5 kJ/mol, respectively. Thus, AMPA easily forms clusters with water molecules in an exothermic reaction. This happens with high cluster concentrations and high evaporation rate constant. The concentrations, [AMPA(H2O)n], show that AMPA forms more complexes with water at higher relative humidity (saturated air) than lower relative humidity (moderate or dry air). However, the significant drop in the concentrations at n > 5, shows that the stability of the complexes reduces with cluster size. The evaporation rates of a single water evaporation pathway of AMPA(H2O)n are large enough thereby showing that binary clusters AMPA – water easily evaporate in the atmosphere. The presence of clusters, AMPA(H2O)n, in the atmosphere can contribute greatly to the atmospheric puzzles of global warming and climate change. This is supported by the estimates of radiative forcing efficiencies of AMPA(H2O)n

Modelling of Solar Radiation for Photovoltaic Applications

This chapter explores the different ways in which solar radiation (SR) can be quantified for use in photovoltaic applications. Some solar radiation models that incorporate different combinations of parameters are presented. The parameters mostly used include the clearness index (Kt), the sunshine fraction (SF), cloud cover (CC) and air mass (m). Some of the models are linear while others are nonlinear. These models will be developed for the estimation of the direct (Hb) and diffuse (Hd) components of global solar radiation (H) on both the horizontal and tilted surfaces. Models to determine the optimal tilt and azimuthal angles for solar photovoltaic (PV) collectors in terms of geographical parameters are equally presented. The applicable, statistical evaluation models that ascertain the validity of the SR mathematical models are also highlighted