Wind energy evaluation for electricity generation using WECS in seven selected locations in Nigeria

This paper statistically examine wind characteristics from seven meteorological stations within the North-West (NW) geo-political region of Nigeria using 36-year (1971–2007) wind speed data measured at 10 m height subjected to 2-parameter Weibull analysis. It is observed that the monthly mean wind speed in this region ranges from 2.64 m/s to 9.83 m/s. The minimum monthly mean wind speed was recorded in Yelwa in the month of November while the maximum value is observed in Katsina in the month of June. The annual wind speeds range from 3.61 m/s in Yelwa to 7.77 m/s in Kano. It is further shown that Sokoto, Katsina and Kano are suitable locations for wind turbine installations with annual mean wind speeds of 7.61, 7.45 and 7.77 m/s, respectively. The results also suggest that Gusau and Zaria should be applicable for wind energy development using taller wind turbine towers due to their respective annual mean speeds and mean power density while Kaduna is considered as marginal. In addition, higher wind speeds were recorded in the morning hours than afternoon periods for this region. A technical electricity generation assessment using four commercial wind turbines were carried out. The results indicate that, while the highest annual power is obtained with Nordex N80–2.5 MW as 14233.53 kW/year in Kano, the lowest is in Yelwa having 618.06 kW/year for Suzlon S52. It is further shown that the highest capacity factor is 64.95% for Suzlon S52–600 kW in Kano while the lowest is 3.82% for Vestas V80–2 MW in Yelwa.     

Modelling of solar energy potential in Nigeria using an artificial neural network model

In this study, an artificial neural network (ANN) based model for prediction of solar energy potential in Nigeria (lat. 4–14°N, log. 2–15°E) was developed. Standard multilayered, feed-forward, back-propagation neural networks with different architecture were designed using neural toolbox for MATLAB. Geographical and meteorological data of 195 cities in Nigeria for period of 10 years (1983–1993) from the NASA geo-satellite database were used for the training and testing the network. Meteorological and geographical data (latitude, longitude, altitude, month, mean sunshine duration, mean temperature, and relative humidity) were used as inputs to the network, while the solar radiation intensity was used as the output of the network. The results show that the correlation coefficients between the ANN predictions and actual mean monthly global solar radiation intensities for training and testing datasets were higher than 90%, thus suggesting a high reliability of the model for evaluation of solar radiation in locations where solar radiation data are not available. The predicted solar radiation values from the model were given in form of monthly maps. The monthly mean solar radiation potential in northern and southern regions ranged from 7.01–5.62 to 5.43–3.54 kW h/m² day, respectively. A graphical user interface (GUI) was developed for the application of the model. The model can be used easily for estimation of solar radiation for preliminary design of solar applications.     

Evaluation of wind energy potential and electricity generation at six locations in Turkey

In this study, wind characteristics were analyzed using the wind speed data collected of the six meteorological stations in Turkey during the period 2000–2006. The annual mean wind speed of the six stations (Erzurum, Elaz??, Bingöl, Kars, Manisa and Ni?de) is obtained as 8.7, 8.5, 5.9, 6.9, 7.4 and 8.0 m/s at 10 m height, respectively. The mean annual value of Weibull shape parameter k is between 1.71 and 1.96 while the annual value of scale parameter c is between 6.81 and 9.71 m/s. A technical assessment has been made of electricity generation from four wind turbines having capacity of (600 kW, 1000 kW, 1500 kW and 2000 kW). The yearly energy output and capacity factor for the four different turbines were calculated.     

Potential wind power generation in South Egypt

Egypt is one of the developing countries. The production of electricity in Egypt is basically on petroleum, natural gas, hydro-power and wind energy. The objective of this work to prove the availability of sufficient wind potential in the wide area of deep south Egypt for the operation of wind turbines there. Nevertheless, it gives in general an approximate profile which is useful to the wind parks design for this area. The data used in the calculation are published and analyzed for the first time. The diagrams of the measured wind data for three meteorological stations over a period of two years (wind speed, frequency, direction), wind shear coefficient, the mean monthly and annual wind speed profile for every location are presented. Monthly Weibull parameters, standard deviation and coefficient of variation have been statistically discussed. A comparison of the rose diagrams shows that the wind speed is more persistent and blow over this region of Egypt in two main sectors N and NNW with long duration of frequencies from 67% to 87% over the year with an average wind speed in the range 6.8-7.9 m/s at the three stations. Evaluation of monthly wind energy density at 10 m height by two different methods was carried out. And the final diagram for every site shows no significant difference between them. The annual natural wind energies at 70 m A.G.L. lie between 333 and 377 W/m² for Dakhla South and Kharga stations, respectively, which is similar to the inland wind potential of Vindeby (Denmark) and some European countries. These results indicate that Kharga and Dakhla South locations are new explored sites for future wind power generation projects.     

Application of artificial neural networks for the wind speed prediction of target station using reference stations data

In this study, artificial neural networks (ANNs) were applied to predict the mean monthly wind speed of any target station using the mean monthly wind speeds of neighboring stations which are indicated as reference stations. Hourly wind speed data, collected by the Turkish State Meteorological Service (TSMS) at 8 measuring stations located in the eastern Mediterranean region of Turkey were used. The long-term wind data, containing hourly wind speeds, directions and related information, cover the period between 1992 and 2001. These data were divided into two sections. According to the correlation coefficients, reference and target stations were defined. The mean monthly wind speeds of reference stations were used and also corresponding months were specified in the input layer of the network. On the other hand, the mean monthly wind speed of the target station was utilized in the output layer of the network. Resilient propagation (RP) learning algorithm was applied in the present simulation. The hidden layers and output layer of the network consist of logistic sigmoid transfer function (logsig) and linear transfer function (purelin) as an activation function. Finally, the values determined by ANN model were compared with the actual data. The maximum mean absolute percentage error was found to be 14.13% for Antakya meteorological station and the best result was found to be 4.49% for Mersin meteorological station.     

Wind energy potential assessment at four typical locations in Ethiopia

The wind energy potential at four different sites in Ethiopia – Addis Ababa (09:02N, 38:42E), Mekele (13:33N, 39:30E), Nazret (08:32N, 39:22E), and Debrezeit (8:44N, 39:02E) – has been investigated by compiling data from different sources and analyzing it using a software tool. The results relating to wind energy potential are given in terms of the monthly average wind speed, wind speed probability density function (PDF), wind speed cumulative density function (CDF), and wind speed duration curve (DC) for all four selected sites. In brief, for measurements taken at a height of 10 m, the results show that for three of the four locations the wind energy potential is reasonable, with average wind speeds of approximately 4 m/s. For the fourth site, the mean wind speed is less than 3 m/s. This study is the first stage in a longer project and will be followed by an analysis of solar energy potential and finally the design of a hybrid standalone electric energy supply system that includes a wind turbine, PV, diesel generator and battery.     

Assessment of wind energy potential of two sites in North-East,Nigeria

The study is used to assess the wind energy potential of Maiduguri and Potiskum, two sites in North-East, Nigeria. 21 years (1987–2007) monthly mean wind data at 10 m height were assessed from the Nigeria Meteorological department and subjected to 2-parameter Weibull and other statistical analyzes. The result showed that average monthly mean wind speed variation for Potiskum ranged from 3.90 to 5.85 m/s, while for Maiduguri, it ranged from 4.35 to 6.33 m/s. Seasonally, data variation between the dry and wet seasons revealed that, the mean wind speed variation for Potiskum ranged from 4.46 (for dry) to 5.16 m/s (for wet), while for Maiduguri it ranged from 5.10 (dry) to 5.59 m/s (wet). The wind power density variation based on the Weibull analysis ranged from 102.54 to 300.15 W/m² for Potiskum and it ranged from 114.77 to 360.04 W/m² for Maiduguri respectively. Moreover, Maiduguri was found to be the better of the sites in terms of monthly and seasonal variation of mean wind speed, but they both can be suitable for stand alone and medium scale wind power generation.     

A novel application of artificial neural network for wind speed estimation

Providing accurate multi-steps wind speed estimation models has increasing significance, because of the important technical and economic impacts of wind speed on power grid security and environment benefits. In this study, the combined strategies for wind speed forecasting are proposed based on an intelligent data processing system using artificial neural network (ANN). Generalized regression neural network and Elman neural network are employed to form two hybrid models. The approach employs one of ANN to model the samples achieving data denoising and assimilation and apply the other to predict wind speed using the pre-processed samples. The proposed method is demonstrated in terms of the predicting improvements of the hybrid models compared with single ANN and the typical forecasting method. To give sufficient cases for the study, four observation sites with monthly average wind speed of four given years in Western China were used to test the models. Multiple evaluation methods demonstrated that the proposed method provides a promising alternative technique in monthly average wind speed estimation.     

Wind power analysis and site matching of wind turbine generators in Kingdom of Bahrain

In this paper, the hourly measured wind speed data for years 2003–2005 at 10 m, 30 m and 60 m height for Kingdom of Bahrain have been statically analyzed to determine the potential of wind power generation. Extrapolation of the 10 m data, using the Power Law, has been used to determine the wind data at heights of 30 m and 60 m. Weibull distribution parameters have been estimated and compared annually and on monthly bases using two methods; the graphical method and the another method, designated in this paper as approximated method, which depends on the standard deviation and average wind speed. The maximum power density for 10 m, 30 m and 60 m heights were found to be 164.33 W/m², 624.17 W/m² and 1171.18 W/m² in February, respectively while the minimum power density were 65.33 W/m², 244.33 W/m² and 454.53 W/m² in October, respectively. The average annual wind power density was found to be 114.54 W/m² for 10 m height, 433.29 W/m² for 30 m height and 816.70 W/m² for 60 m height. Weibull probability function, using Weibull parameters estimated from the approximated method, has shown to provide more accurate prediction of average wind speed and average power density than the graphical method. In addition, the site matching of wind turbine generators at 30 m and 60 m heights has been investigated by estimating the capacity factors of various commercially available wind turbines generators. The monthly and annual variation of capacity factors have been studied to ensure optimum selection of wind turbine generators.     

The potential of electricity generation on the east coast of Red Sea in Egypt

Wind characteristics have been analyzed based on long-term measured data of monthly mean wind speed of seven meteorological stations along the east coast of Red Sea in Egypt. It was found that the windiest stations (Region A) namely (Zafarana, Abu Darag, Hurghada and Ras Benas) have annual mean wind speeds (7.3, 7.2, 6.4 and 5.5 m/s) at 10 m height, respectively.Numerical estimations using measured wind speeds and frequencies to calculate the two Weibull parameters were carried out and two methods were applied.The methodical analysis for the corrected monthly wind power density at a height of 10 m above ground level, over roughness class 0 (water), for each station was done. The recommended correlation equation was also stated for Red Sea zone in Egypt. Also the corrected annual wind power density at the heights (50–70) m was obtained for all stations. Moreover, calculations show that the four stations in (Region A) have a huge energy potential available (430–1000 W/m²) at 70 m height, while Quseir and Suez stations (Region B) have good wind power density (170–190 W/m²) at 50 m height.A technical and economic assessment has been made of electricity generation from two turbines machines having capacity of (1000 and 600 kW) considered in Regions A & B, respectively, using WASP program. The yearly energy output, capacity factor and the electrical energy cost of kWh produced by the two different turbines in each region were estimated. The production costs of four stations in Region A was found to be less than 2€ cent/kWh and compared with retail tariff.     

Wind energy development policy and prospects in Lithuania

According to the EU Directive 2001/77/EC 7% of all electricity production is to be generated from renewable energy sources (RES) in Lithuania in 2010. Electricity production from RES is determined by hydro, biomass and wind energy resources in Lithuania. Further development of hydro power plants is limited by environmental restrictions, therefore priority is given to wind energy development. The aim of this paper is to show estimation of the maximum wind power penetration in the Lithuanian electricity system using such criteria as wind potential, possibilities of the existing electricity network, possible environmental impact, and social and economical aspects. Generalization of data from the meteorological stations and special measurements shows that the highest average wind speed in Lithuanian territory is in the coastal region and at 50 m above ground level reaches 6.4 m/s. In regard to wind resource distribution in this region, arrangement of electricity grid and environment protection requirements, six zones have been determined for wind power plant construction. Calculations have shown that the largest total installed capacity of wind farms, which could cause no significant increase in power transmission expenses, is 170 MW. The threshold, which cannot be passed without capital reconstruction of electricity network, is 500 MW of total capacity of wind farms.     

Investigation of wind characteristics and assessment of wind-generation potentiality in Uludağ-Bursa,Turkey

In this study, wind characteristic and wind energy potential of the Uluda? skinning which is located in the south Marmara region of Turkey were analyzed using the wind speed data collected during the period 2000–2006. The wind speed distribution curves of Uluda?-Bursa were obtained by using the Weibull and Rayleigh probability density functions. The average Weibull shape parameter k and scale parameter c were found as 1.78 and 7.97 m/s for the period 2000–2006. The yearly mean wind speed in Uluda?-Bursa was obtained as 7.08 m/s for period of 7 years. A technical and economic assessment has been made of electricity generation from four wind turbines having capacity of (600, 1000, 1500 and 2000 kW). The yearly energy output, capacity factor and the electrical energy cost of kW h produced by the three different turbines were calculated. The cost of each kW h produced using the chosen wind turbines in Uluda?-Bursa were found to between 0.255 and 0.306 $/kW h.     

Comparison between ANNs and linear MCP algorithms in the long-term estimation of the cost per kW h produced by a wind turbine at a candidate site: A case study in the Canary Islands

In the work presented in this paper Artificial Neural Networks (ANNs) were used to estimate the long-term wind speeds at a candidate site. The specific costs of the wind energy were subsequently determined on the basis of the knowledge of these wind speeds. The results were compared with those obtained with a linear Measure–Correlate–Predict (MCP) method. The mean hourly wind speeds and directions recorded over a 10 year period at six weather stations located on different islands in the Canary Archipelago (Spain) were used as a case study. The power-wind speed curves for five wind turbines of different rated power were also used. The mean absolute percentage error (MAPE), Pearson’s correlation coefficient and the Index of Agreement (IoA) between measured and estimated data were used to evaluate the errors made with the different metrics analysed.     

On wind speed pattern and energy potential in Nigeria

The aim of this paper is to review wind speed distribution and wind energy availability in Nigeria and discuss the potential of using this resource for generation of wind power in the country. The power output from a wind turbine is strongly dependent on the wind speed and accurate information about the wind data in a targeted location is essential. The annual mean wind speeds in Nigeria range from about 2 to 9.5 m/s and the annual power density range between 3.40 and 520 kW/m² based on recent reported data. The trend shows that wind speeds are low in the south and gradually increases to relatively high speeds in the north. The areas that are suitable for exploitation of wind energy for electricity generation as well as for water pumping were identified. Also some of the challenges facing the development of wind energy and suggested solutions were presented.     

Feasibility study of harnessing wind energy for turbine installation in province of Yazd in Iran

This paper analyses the wind speed of some major cities in province of Yazd which is located in central part of Iran. Also, the feasibility study of implementing wind turbines to take advantage of wind power is reviewed and then the subject of wind speed and wind potential at different stations is considered. This paper utilized wind speed data over a period of almost 13 years between 1992 and 2005 from 11 stations, to assess the wind power potential at these sites. In this paper, the hourly measured wind speed data at 10 m, 20 m and 40 m height for Yazd province have been statically analyzed to determine the potential of wind power generation. Extrapolation of the 10 m data, using the Power Law, has been used to determine the wind data at heights of 20 m and 40 m. The results showed that most of the stations have annual average wind speed of less than 4.5 m/s which is considered as unacceptable for installation of the wind turbines. City of Herat has higher wind energy potential with annual wind speed average of 5.05 m/s and 6.86 m/s, respectively, at height of 10 m and 40 m above ground level (AGL). This site is a good candidate for remote area wind energy applications. But some more information is required, because the collected data for Herat is only for 2004. Cities of Aghda with 3.96 m/s, Gariz with 3.95 m/s, and Maybod with 3.83 m/s annual wind speed average at height of 10 m above ground level are also able to harness wind by installing small wind turbines. The Tabas and Bafgh sites wind speed data indicated that the two sites have lower annual wind speed averages between 1.56 m/s and 2.22 m/s at 10 m height. The monthly and annual wind speeds at different heights have been studied to ensure optimum selection of wind turbine installation for different stations in Yazd.     

Evaluation of wind energy potential and electricity generation on the coast of Mediterranean Sea in Egypt

Wind data from 10 coastal meteorological stations along the Mediterranean Sea in Egypt have been used for statistical analysis to determine the wind characteristics. It was found that three stations show annual mean wind speed greater than 5.0 m/s. In order to identify the Weibull parameters for all stations two different methods were applied.The methodical analysis for all stations was done for the corrected monthly and annual mean wind power at a height of 10 m, over roughness class 0 (water). The recommended correlation equation was also stated for Mediterranean Sea zone in Egypt. Also the wind power densities for heights of 30–50 m were calculated for all stations. Three of them are the best locations, namely: Sidi Barrani, Mersa Matruh, and El Dabaa, where these contiguous stations have great abundantly wind energy density.A technical assessment has been made of the electricity generation using WASP program for two commercial turbines (300 kW and 1 MW) considering at the three promising sites. The wind turbine of capacity 1 MW was found to produce an energy output per year of 2718 MW h at El Dabaa station, and the production costs was found 2€ cent/kW h.     

Wind speed prediction in the mountainous region of India using an artificial neural network model

Measured wind speed data are not available for most sites in the mountainous regions of India. The objective of present study is to predict wind speeds for 11 locations in the Western Himalayan Indian state of Himachal Pradesh to identify possible wind energy applications. An artificial neural network (ANN) model is used to predict wind speeds using measured wind data of Hamirpur location for training and testing. Temperature, air pressure, solar radiation and altitude are taken as inputs for the ANN model to predict daily mean wind speeds. Mean absolute percentage error (MAPE) and correlation coefficient between the predicted and measured wind speeds are found to be 4.55% and 0.98 respectively. Predicted wind speeds are found to range from 1.27 to 3.78 m/s for Bilaspur, Chamba, Kangra, Kinnaur, Kullu, Keylong, Mandi, Shimla, Sirmaur, Solan and Una locations. A micro-wind turbine is used to assess the wind power generated at these locations which is found to vary from 773.61 W to 5329.76 W which is suitable for small lighting applications. Model is validated by predicting wind speeds for Gurgaon city for which measured data are available with MAPE 6.489% and correlation coefficient 0.99 showing high prediction accuracy of the developed ANN Model.     

Techno-economics of small wind electric generator projects for decentralized power supply in India

A techno-economic evaluation of small wind electric generator (SWEG) projects for providing decentralized power supply in remote locations in India is presented. SWEG projects that have either been implemented or are under implementation have been considered. The capital costs of the SWEG projects and sub-systems have been analysed. Levelised unit cost of electricity (LUCE) has been estimated for 19 select places located in different geographical regions of the country. The LUCE is found to vary in the range of Rs. 4.67–83.02/kWh (US$¹ 0.10–1.86/kWh) for wind electric generator projects in the capacity range 3.2–50 kW with annual mean wind speed variation in the range 5–10 m/s. Issues relating to their environmental impact(s), barriers to diffusion and institutional mechanism(s) to implement such projects have also been discussed.     

Prediction of monthly mean daily diffuse solar radiation using artificial neural networks and comparison with other empirical models

An artificial neural network (ANN) model for estimating monthly mean daily diffuse solar radiation is presented in this paper. Solar radiation data from 9 stations having different climatic conditions all over China during 1995–2004 are used for training and testing the ANN. Solar radiation data from eight typical cities are used for training the neural networks and data from the remaining one location are used for testing the estimated values. Estimated values are compared with measured values in terms of mean percentage error (MPE), mean bias error (MBE) and root mean square error (RMSE). The results of the ANN model have been compared with other empirical regression models. The solar radiation estimations by ANN are in good agreement with the actual values and are superior to those of other available models. In addition, ANN model is tested to predict the same components for Zhengzhou station over the same period. Results indicate that ANN model predicts the actual values for Zhengzhou with a good accuracy of 94.81%. Data for Zhengzhou are not included as a part of ANN training set. Hence, these results demonstrate the generalization capability of this approach and its ability to produce accurate estimates in China.     

Spatial estimation of wind speed

This study utilizes Abductory Induction Mechanism to estimate the mean monthly wind speed at some locations in Saudi Arabia based on wind data at other available recording stations in addition to some historical wind speed data at the target site. Wind speed data from 20 meteorological stations over a period of 16 years between 1990 and 2005 was used to accomplish the set objective. To validate the model, data from 19 stations was used to estimate the wind speed at the 20th location. Evaluation was performed for every one of the 20 available locations. Results show good agreement between estimated and measured monthly mean wind speed values. Copyright © 2010 John Wiley & Sons, Ltd.