Mean Power Density Research Articles

Investigation of Wind Energy Potential at Selected Locations in the Volta Region of Ghana

The cost of extending electrical power systems to remote areas is expensive and the option is to decentralise small scale energy power systems that could meet the demand in those areas. Wind potential at selected locations in Ghana has been studied for wind energy purpose since there is an increase in socio-economic development in the country. Four (4) of the three-cup anemometers and data-loggers were installed on a 20-m and 30m masts at Amedzofe, Anloga, Areeba-Nkwanta and Kue-Nkwanta (all in the Volta region). The recorded data were analyzed using STATISTICAL software and calculations were made to obtain the Weibull distribution two-parameters and prediction for hourly, monthly mean wind speeds and mean wind power densities were predicted at 50 m above ground level. Among the selected sites, the mean wind power density of Amedzofe and Anloga fell into Classes 3 and 6 of the commercially international system of wind classification respectively, while Areeba-Nkwanta and Kue-Nkwanta fell into Class 2. Anloga is therefore a suitable site for wind power generation to serve the community.
 Keywords: Wind power potential; Wind atlas analysis and application program (WAsP); Weibull distribution; Wind speed; Wind power density.

Improvement of SAR Accuracy by Combining Two SAR Quantification Methods for Small Animals in Reverberation Chamber Above 10 GHz

Two measurement-based methods are presented to quantify the whole-body-averaged specific absorption rate (WBA-SAR) and local SAR for small animals in a medium-size reverberation chamber (RC) for a bio-effect experiment at frequencies above 10 GHz. S-parameter-based method is a totally experimental approach to quantify the WBA-SAR in small animals based on measuring the mean power density and loss power expressed by S-parameters. Two-step method is a measurement-simulation hybrid approach, which links the mean power density to the finite difference time domain (FDTD) calculated WBA-SAR and local SAR in specified organs. However, at a frequency as high as 10 GHz, the RC is usually not ideal because loss on the RC walls is not negligible. In addition, a numerical-only approach is also unrealistic to quantify the SAR because of the heavy calculation burden and difficulty in obtaining converged results. To address this issue, we proposed an improved two-step hybrid method. The method uses the S-parameter-based method to derive the mean power density by matching its WBA-SAR to that of the two-step hybrid method, and then calculate the local SAR in anatomical numerical animal models using the two-step hybrid method under this mean power density. In such a way, in addition to the advantage of obtaining local SAR, the two-step hybrid method has significantly improved its accuracy of SAR quantification.

Wind characteristic analysis based on Weibull distribution of Al-Salman site, Iraq

This paper aims to analyze the characteristic of wind speed data in Al-Salman site — Iraq using Weibull distribution. Maximum likelihood method (MLM) was used to find out two essential Weibull parameters. The best wind distribution was described by using probability density function and cumulative distribution function. Based on Weibull parameters, an analysis is carried out for various wind turbine hub heights. The characteristics of wind wave for this site are regular, uniform, and close to Rayleigh function. Furthermore, the shape factor and scale factor have fluctuated from 1.8 to 3.2 and from 5.93 m/s to 8.3 m/s for different period respectively. It was found that, at 50 m height, the mean wind speed is 5.93m/s, mean power density for this site is 219 W/m and that almost 50% of the wind speed was above 5 m/s within the total hours per annum. As consequence, this site has a potential wind power to erect small wind turbines for electricity production. Wind turbine model was proposed relying on capacity factor.

Comparison of six different methods of Weibull distribution for wind power assessment: A case study for a site in the Northern region of Pakistan

Pakistan is one of those countries that heavily dependent on hydrocarbon fuel for energy production which results in severe climate change. Although renewable energy resources, like wind, are available in abundance, they are not completely utilized due to which energy crises of the country increases day by day. This study comprehensively elaborates the potential of wind power of a site located in the Northern part of Pakistan. The potential of wind power for the site was accessed and analyzed by using two years (2016–18) wind data at four heights (80, 60, 40 and 20 m from sea level). Six numerical methods are used for the estimation of Weibull parameters (shape ‘k’ and scale ‘c’). Using these parameters, Weibull mean velocity, power density, and energy density are computed at all mast heights for each year. The seasonal wind characteristics parameters and power density are also calculated. Among these methods, the modified maximum likelihood method (MMLM) is found to be the most effective method to represent the wind data, while the graphical method (GM) showed the weakest fit. Root mean square error (RMSE), determination of coefficient (R2), and mean bias error (MBE) are computed to validate the computed results. Wind power potential assessment of the calculated data shows that the proposed location is appropriate for small scale wind energy generation.

Evaluation of probabilistic and deterministic methods for estimating energy potential of tidal currents in the Khuran Strait, the Persian Gulf

Tidal current can be used as a source of renewable and clean energy. Deterministic modeling of tidal current speed in power generation systems is commonly used for evaluating the tidal current energy. Due to lack of long-term tidal current measurement, regeneration of a long-period time series of tidal current speeds is of high importance. In this study, both deterministic and statistical models were applied to estimate energy potential at tidal current observation stations in the Khuran Strait in the Persian Gulf. The long-term current speed data were generated by harmonic analysis and probabilistic modeling. To evaluate the efficiencies of the statistical models and predict the tidal current speeds, a set of measurements were performed in the 12 stations. The conducted analysis showed that the Wakeby distribution was the best probability distribution function capable of successfully passing the statistical tests. Kolmogorov–Smirnov (K–S) and root-mean-square error were used to perform a priori and a posteriori statistical tests in two steps. Harmonic analysis and prediction methodology were applied to generate the long-term data to be used in a posteriori test and energy potential energy estimations. In addition, the predicted tidal current speeds derived from the mentioned distribution function were used for energy estimations of tidal current at the Khuran Strait in the Persian Gulf. The mean power density over a year was calculated using the cubed velocity formula with the Betz limit coefficient and current speed distribution frequencies in the tidal current stations. The results indicated that the density power of tidal currents at Laft station was stronger compared to other locations. Moreover, the estimated values for potential energy of tidal currents in both methods were close together (r2 = 0.998) with high precision.

Wind Resource Assessment on Puná Island

Puná Island, located in the Pacific Ocean off the southern coast of Ecuador, has a population of approximately 3344 inhabitants. However, not all inhabitants have access to electricity, which is largely supplied by diesel generators. Therefore, to identify a renewable, sustainable, environmentally friendly and low-cost alternative, a 40-m-high anemometer tower was installed for wind resource assessment and to determine the possibility of generating electricity from wind energy. Based on mathematical models for electricity generation from wind energy, data were analyzed using the software Windographer and WAsP, to determine a long-term wind speed of 4.8 m/s and a mean wind power density of 272 W/m2. By simulating the use of a 3.3-MW wind turbine, we demonstrated that as much as 800 kWh could be generated during the hours when the wind reaches its highest speed. In addition to demonstrating the technical feasibility of meeting the electricity demands of Puná Island through wind power, this study exemplifies a method that can be used for wind resource assessment in any location.

Assessment of tidal stream energy resources in a hypertidal estuary with highly irregular bathymetry using 3D numerical modelling

Owing to large currents generated by the strong tides coming from Ungava Bay, the hydrokinetic energy potential of the Koksoak River Estuary (KRE) Nunavik, Canada, is substantial. With a tidal range of more than 12 m, the KRE is classified as “hypertidal”, and with water depths ranging from 10 to 130 m and widths ranging from 700 to 4500 m, it could also be said that the KRE is “hyperbathymetric”. Even aside from the intense cold associated with its Arctic climate, this combination of hypertidal/hyperbathymetric makes the KRE possibly the most extreme estuary in the world. Using a 3D hydrodynamic finite-difference model to simulate the KRE’s velocities, the in-stream power densities were calculated over the entire domain. The resulting map pinpoints the energy sites available for the 3000 residents of Kuujjuaq, a town located 50 km from the estuary mouth. At optimal KRE sites, based on vertically averaged velocities, the available mean power density is 7.2 kW/m2. Near the surface, the available mean power density increases to 10.5 kW/m2. These values exceed that calculated (6.0 kW/m2) for the famous Bay of Fundy’s Minas Passage. This paper describes the nature of the estuary, introduces the modelling efforts, and quantifies the spatial–temporal variability of the available hydrokinetic power density.

Comparative study of offshore winds and wind energy production derived from multiple scatterometers and met buoys

Wind resource assessment is a challenging task where the availability of offshore Met buoys is limited. Satellite remote sensing provides the best alternative inexpensive data and fills the data gaps by providing a huge volume of data for extended periods. However, due to the availability of multiple satellite scatterometers, the assessment of such resources from any single scatterometer would lead to inconsistency. The present article provides an extensive comparison of four scatterometers namely QuikSCAT, OSCAT, ASCAT-A and ASCAT-B with long-term in-situ wind datasets over the North Indian Ocean. Results show that the QuikSCAT and the OSCAT wind data have a lesser bias with the range of 0.15 m/s (2.4%) to 0.83 m/s (15.1%) before adjustments. For adjusting the satellite wind speeds, linear regression was used. Further, the synergetic approach of linear regression adjustments and the combination of scatterometer data have resulted in smaller differences. The absolute deviation in mean wind speed between co-located buoy data and combined scatterometer data is less of the order 2.1%, and the mean power density showed a difference of 2.5%. The study revealed that the combined scatterometer datasets would help in the accurate assessments of wind resource rather than a single scatterometer.

Investigations into Some Simple Expressions of the Gamma Function in Wind Power Theoretical Estimate by the Weibull Distribution

The Weibull distribution is a probability density function (PDF) which is widely used in the study of meteorological data. The statistical analysis of the wind speed v by using the Weibull distribution leads to the estimate of the mean wind speed , the variance of v around and the mean power density in the wind. The gamma function Γ is involved in those calculations, particularly Γ (1+1/k), Γ (1+2/k) and Γ (1+3/k). The paper reports the use of the Weibull PDF f(v) to estimate the gamma function. The study was performed by looking for the wind speeds related to the maximum values of f(v), v2 f(v) and v3 f(v). As a result, some approximate relationships were obtained for Γ (1+1/k), Γ (1+2/k) and Γ (1+3/k), that use some fitting polynomial functions. Very good agreements were found between the exact and the estimated values of Γ (1+n/k) that can be used for the estimation of the mean wind speed , the variance σ2 of the wind speed v; around the mean speed and the average wind power density.

Evaluation of Wind Energy Potential for Power Generation in some Coastal Cities in Akwa Ibom State, Nigeria

The wind potential of three coastal sites, Ibeno (Latitude 4° 33' 54.2"N & Longitude 8° 4' 21.3"E), Oron (Latitude 4°48'26.53"N & Longitude 8°14'15.74"E) and Okobo (Latitude 4°41'48.48"N & Longitude 8°15'23.23"E) in Akwa Ibom State, Nigeria was investigated. Using data obtained from the Nigeria Meteorological Agency (NIMET) and the Maritime Academy of Nigeria, Oron for Ibeno, Okobo and Oron respectively for a period of five years (2012-2016), a statistical analysis was performed. Using empirical method, the overall average Weibull distribution parameters in terms of k and c, mean power density and mean wind speed were determined for the three locations to be 3.29, 4.74 & 4.11, 7.72, 6.54 & 6.40 m/s, 344.68, 236.10 & 185.18 w/m2 and 8.24, 7.26 & 6.70 m/s respectively for Ibeno, Oron and Okobo. The results indicate that all the locations had a wind speed range higher than the 5-6 m/s recommended for electricity generation and a wind density value greater than 100 W/m2. Resulting from the analysis, the overall annual energy production was projected to be 15.23, 10.45 & 8.19 GW for Ibeno, Oron and Okobo respectively, leading to the conclusion that the three cities located along the coastal lines of Akwa Ibom, would be viable for commercial wind power production.

Wind Energy Potential Assessment of Coastal States in South-South Nigeria Based on the Weibull Distribution Model

Energy has since become the global index for assessment of standard of living for socio-economic and industrial development. Worldwide, energy demand is rising with increasing population. Conventional energy sources such as fossil fuels are unsustainable and environmentally-unfriendly. Alternative sources of energy such as the sun, the wind etc. that are sustainable and less harmful to the environment need be exploited to meet the ever rising demand for energy, to avoid energy deficit. This paper investigated the wind energy potential of three locations in south-south Nigeria. Wind speed data measured at 10-metres height over the period 2013-2017 obtained from the Nigerian Meteorological Agency (NiMet) for Yenagoa, Calabar and Port Harcourt were evaluated using the Weibull two-parameter probability distribution model to ascertain the wind energy potential of the respective locations. The study outcome shows that during the study period, the monthly mean wind speed varied between 1.2 m/s in November and 2.3 m/s in February for Yenagoa. The same varied between 2.2 m/s in July and 3.7 m/s in February for Calabar but ranged between 1.0 m/s in July to 1.6 m/s in February for Port Harcourt. The annual mean wind speeds for Yenagoa, Calabar and Port Harcourt were 1.74 m/s, 2.85 m/s and 1.38 m/s respectively. The annual mean power densities for Yenagoa, Calabar and Port Harcourt were found to be 4.64 W/m2, 7.06 W/m2 and 3.08 W/m2 respectively while the corresponding values of the annual mean energy densities were 3.24 KW/m2, 4.93 KW/m2 and 2.14 KW/m2 respectively. The study reveal that though wind energy in the study areas is sufficient only for standalone power generating systems, water pumping and applications requiring less power, higher value of wind energy is possible if wind speed data were collected at heights above the 10m implemented in the study. Â

On wind speed pattern and energy potential in China

To achieve sustainable socioeconomic development as part of the global community, China has set ambitious goals for developing wind power within its national energy security framework. Existing wind power density maps in China present some knowledge gaps (e.g., lack of spatiotemporal resolution/characteristics and estimates at the height of a typical wind turbine). To address them, we combine the advantages of large network of wind speed observations (2430 meteorological stations; 2006–2015) and the grid-based method in order to create the spatiotemporal distributions of wind resource at the hub height. We found that the spatiotemporal variability of wind resources in China is apparent. Nationally, the annual mean wind speed and wind power density are 4.09 m s−1 and 164.1 W m−2, respectively. Spatially, Northeast China has the highest wind potential with annual mean wind speed and power density 4.64 m s−1 and 204 W m−2, while South China has the lowest wind potential annual mean wind speed and power density 3.55 m s−1 and 97.4 W m−2. Temporally, the wind resources over the entire China are higher in the cold season (spring and winter with peaks in April) than those in the warm season (summer and autumn with bottoms in August). Specifically, the wind power output in “Three North” region (including Northeast China, North China and Northwest China) in the cold season is 1.4 times higher than that in the warm season. Some coastal regions in East China have relatively high wind power density (>500 W m−2) and small seasonal variations. These findings should help decision-makers to identify the areas suitable for utilizing wind energy, and optimizing power generation and transmission across the grid for better system performance.

Wind and Solar Assessment in the Sahelian Zone of Chad

Renewable energy sources have long been renowned as potential sources of free, clean and inexhaustible energy. However, energy in Chad is still exploited from non-renewable sources due to lack of comprehensive wind and solar maps, skills and public awareness pertaining to its significance in the country. This study is therefore set out to provide assessments of both wind and solar energies in the Sahelian zone in Chad, using the statistical two-parameter Weibull distribution function and Angstrom-Prescott model, respectively for a ten-year period. The evaluation of wind potential shows that the annual mean of the shape and scale parameters for all stations under study varied in the range of 1.26 – 1.56 and 2.49 m/s – 3.91 m/s, respectively. Stronger wind speed was observed during the dry season and a relatively slower wind speed was perceived during the wet season. Among the four stations, N’Djamena showed the highest power density compared to the other three stations with a mean power density that ranges between 100 W/m 2 – 220 W/m 2 . The prediction of solar potential in this region indicates that the global solar radiation for all stations is promising with station Abeche however, to show the highest solar energy density on average. A good agreement between Angstrom model and NASA data was also observed.

Application of the Cuckoo Search in the Adjustment of Weibull Curves for Wind Energy Using Wind Data of Petrolina City

In this work a Cuckoo Search (CS) applicability study was carried out using the methodology described by Yang and Deb (2009) in the optimization of Weibull curves, being implemented from an algorithm written in programming language R. The real velocity data were obtained from the National System for the Organization of Environmental Data (SONDA) platform for the city of Petrolina - PE, because it is located in a state with great potential for wind energy generation. The Cuckoo Search heuristic method was compared with six deterministic methods widely used in the literature from the results of the statistical tests Root Mean Square Error (RMSE), R 2 , Mean Absolute Error (MAE) and Power Density Error (EDP). With the objective function used, Cuckoo Search was satisfactory in the wind representation of the studied region, presenting RMSE of 0.006052, R 2 of 0.99348 and MAE of 0.003979, more desirable in relation to the other methods. Also, an EDP of approximately 1.12% above the actual power density was obtained, which makes it difficult to use for problems in which energy oversizing is not allowed.

Observation-based solar and wind power capacity factors and power densities

Power density is the rate of energy generation per unit of land surface area occupied by an energy system. The power density of low-carbon energy sources will play an important role in mediating the environmental consequences of energy system decarbonization as the world transitions away from high power-density fossil fuels. All else equal, lower power densities mean larger land and environmental footprints. The power density of solar and wind power remain surprisingly uncertain: estimates of realizable generation rates per unit area for wind and solar power span 0.3–47 We m−2 and 10–120 We m−2 respectively. We refine this range using US data from 1990–2016. We estimate wind power density from primary data, and solar power density from primary plant-level data and prior datasets on capacity density. The mean power density of 411 onshore wind power plants in 2016 was 0.50 We m−2. Wind plants with the largest areas have the lowest power densities. Wind power capacity factors are increasing, but that increase is associated with a decrease in capacity densities, so power densities are stable or declining. If wind power expands away from the best locations and the areas of wind power plants keep increasing, it seems likely that wind’s power density will decrease as total wind generation increases. The mean 2016 power density of 1150 solar power plants was 5.4 We m−2. Solar capacity factors and (likely) power densities are increasing with time driven, in part, by improved panel efficiencies. Wind power has a 10-fold lower power density than solar, but wind power installations directly occupy much less of the land within their boundaries. The environmental and social consequences of these divergent land occupancy patterns need further study.

The effect of 2.45 GHz non-ionizing radiation on the structure and ultrastructure of the testis in juvenile rats.

Nowadays, mobile devices that emit non-ionizing electromagnetic radiation (EMR) are predominantly used by juveniles and pubescents. The aim of the present study was to evaluate the effect of whole body pulsed EMR on the juvenile Wistar albino rat testis at a frequency of 2.45 GHz and mean power density of 2.8 mW/cm². The investigated animals (n=24) were divided into two control and two EMR groups (5 and 6 week old rats; 6 rats per group). Both EMR groups were irradiated continually for 3 weeks (2h/day) from postnatal days 14 and 21, respectively. EMR caused an irregular shape of seminiferous tubules with desquamated immature germ cells in the lumen, a large number of empty spaces along the seminiferous epithelium and dilated and congested blood vessels in the interstitial tissue of the testis. The cytoplasm of Sertoli cells showed strong vacuolization and damaged organelles, with the cytoplasm full of different heterophagic and lipid vacuoles or the cytoplasm of spermatocytes with swollen mitochondria in both irradiated groups. A significant increase in the total tubular area of seminiferous tubules was observed in both EMR groups compared with controls (P<0.001). A significant increase in the TUNEL-positive apoptotic nuclei (P<0.01) was accompanied by a significant rise in both Cu-Zn-SOD (P<0.01) and Mn-SOD (P<0.001) positive cells in the 6 week old experimental rats compared to control animals. Our results confirmed a harmful effect of non-ionizing radiation on the structure and ultrastructure of the juvenile rat testis.

Map of Jordan governorates wind distribution and mean power density

The Renewable energy potential in Jordan is enormous as it lies within the top of the world countries renewable resources. Electricity demand in Jordan plays a major role in the high amount of energy requring to cover the needs of heating, cooling, lighting, etc. For that, the availability of the wind distribution information becomes essential to help in the design and building of the wind energy application. In this study, a wind distribution map is provided of all Jordan governorates: Irbid, Mafraq, Ajloun, Jarash, Amman, Zarqa, Madaba, Balqa, Karak, Tafilah, Mann and Aqaba.

Statistical Analysis Of Wind Speed Distribution Of Turkey As Regional

In this study, the statistical analysis of wind power density and wind speed distribution parameters of the selected cities from seven region of Turkey was investigated using the hourly wind speed data measured by the Turkish State Meteorological Service between 2005 and 2014. The Weibull and Rayleigh distributions were used for modeling and the success of this modeling process was evaluated according to the parameters of R2, RMSE and c2. The Weibull parameters and the Rayleigh parameters were estimated analytically, and the mean wind speed and energy potential were determined based on these parameters. At the Weibull distribution, the lowest mean wind speed and power density was obtained as 1.73 m/s and 5.78 W/m2 in Adıyaman, respectively. The highest mean speed and power density was determined as 2.95 m/s and 33.32 W/m2 in Sinop. At the Rayleigh distribution, the lowest and the highest mean speed and the power density was obtained as 1.72 m/s and 5.63 W/m2 in Adıyaman, 3.06 m /s and 33.44 W/m2 in Sinop, respectively. Generally, the highest mean wind speed and power density values were determined in Sinop, and the lowest mean wind speed and power density values in Adıyaman. According to statistical criteria in wind data analysis of Turkey, the Weibull distribution was better than the Rayleigh distribution.

Application of the hybrid ANFIS models for long term wind power density prediction with extrapolation capability.

In this paper, the suitability and performance of ANFIS (adaptive neuro-fuzzy inference system), ANFIS-PSO (particle swarm optimization), ANFIS-GA (genetic algorithm) and ANFIS-DE (differential evolution) has been investigated for the prediction of monthly and weekly wind power density (WPD) of four different locations named Mersing, Kuala Terengganu, Pulau Langkawi and Bayan Lepas all in Malaysia. For this aim, standalone ANFIS, ANFIS-PSO, ANFIS-GA and ANFIS-DE prediction algorithm are developed in MATLAB platform. The performance of the proposed hybrid ANFIS models is determined by computing different statistical parameters such as mean absolute bias error (MABE), mean absolute percentage error (MAPE), root mean square error (RMSE) and coefficient of determination (R2). The results obtained from ANFIS-PSO and ANFIS-GA enjoy higher performance and accuracy than other models, and they can be suggested for practical application to predict monthly and weekly mean wind power density. Besides, the capability of the proposed hybrid ANFIS models is examined to predict the wind data for the locations where measured wind data are not available, and the results are compared with the measured wind data from nearby stations.

An investigation of wind power density distribution at location with low and high wind speeds using statistical model

The study represents wind characteristics and power density in the locations with different wind speed conditions. The wind speed data measured at meteorological stations were used for statistical analysis from two locations with high and low wind speeds in Lithuania. It was found that many of the probability density functions of calculation methods allows you to get a fairly reliable results. However, depending on the geographical situation of the area, the height and other factors affect the wind power density indicates that some of the methods are not acceptable. The Weibull shape k and scale c parameters were calculated by using four methods and after the wind characteristics and power density were estimated. Also, the wind parameters extrapolation with the height was carried out. The mean square error, coefficient of determination, chi-square test and relative error were calculated for validation of goodness fit of Weibull parameters. The empirical model was developed for evaluation of monthly mean wind power density based on the measured wind speeds. The proposed simulation model for assessing the wind energy density could be successfully used for the finding the suitable sites for the development of wind energy in the selected locations.