Wind Speed Distributions Used in Wind Energy Assessment: A Review

With economic development and population growth, energy demand has shown an upward trend. Renewable energy is inexhaustible and causes little pollution, which has broad prospects for development. In recent years, wind energy has been developed as an essential renewable energy source. The use of wind power is very environmentally friendly and plays a critical role in economic growth. Assessing the characteristics and potential of wind energy is the first step in the effective development of wind energy. The wind speed distribution at a specific location determines the available wind energy. This paper reviews the wind speed distribution models used for wind energy assessment, and they are applicable to different wind regimes. All potential wind speed distribution models should be considered for modeling wind speed data at a particular site. Previous studies have selected several parameter estimation methods and evaluation criteria to estimate model parameters and evaluate the goodness-of-fit. This paper discusses their advantages and disadvantages. The characteristics of wind speed distribution are constantly varying geographically and temporally. Wind energy assessment should consider local geographical elements, such as local climate, topography, and thermal properties difference between the land and the sea, and focus on long-term variations in wind characteristics.

Doppler Lidar Investigations of Wind Turbine Near-Wakes and LES Modeling with New Porous Disc Approach

Many bottom-mounted offshore wind farms are currently planned for the coastal areas of Japan, in which wind speeds of 6.0–10.0 m/s are extremely common. The impact of such wind speeds is very relevant for the realization of bottom-mounted offshore wind farms. In evaluating the feasibility of these wind farms, therefore, strict evaluation at wind speeds of 6.0–10.0 m/s is important. In the present study, the airflow characteristics of 2 MW-class downwind wind turbine wake flows were first investigated using a vertically profiling remote sensing wind measurement device (lidar). The wind turbines used in this study are installed at the point where the sea is just in front of the wind turbines. A ground-based continuous-wave (CW) conically scanning wind lidar system (“ZephIR ZX300”) was used. Focusing on the wind turbine near-wakes, the detailed behaviors were considered. We found that the influence of the wind turbine wake, that is, the wake loss (wind velocity deficit), is extremely large in the wind speed range of 6.0–10.0 m/s, and that the wake loss was almost constant at such wind speeds (6.0–10.0 m/s). It was additionally shown that these results correspond to the distribution of the thrust coefficient of the wind turbine. We proposed a computational fluid dynamics (CFD) porous disk (PD) wake model as an intermediate method between engineering wake models and CFD wake models. Based on the above observations, the wind speed range for reproducing the behavior of the wind turbine wakes with the CFD PD wake model we developed was set to 6.0–10.0 m/s. Targeting the vertical wind speed distribution in the near-wake region acquired in the “ZephIR ZX300”, we tuned the parameters of the CFD PD wake model (CRC = 2.5). We found that in practice, when evaluating the mean wind velocity deficit due to wind turbine wakes, applying the CFD PD wake model in the wind turbine swept area was very effective. That is, the CFD PD wake model can reproduce the mean average wind speed distribution in the wind turbine swept area.

Application of differential evolution for wind speed distribution parameters estimation

Weibull distribution is an extensively used statistical distribution for analyzing wind speed and determining energy potential studies. Estimation of the wind speed distribution parameter is essential as it significantly affects the success of Weibull distribution application to wind energy. Various estimation methods viz. graphical method, moment method (MM), maximum likelihood method (ML), modified maximum likelihood method, and energy pattern factor method or power density method have been presented in various reported research studies for accurate estimation of distribution parameters. ML is the most preferred approach to study the parameter estimation. ML works on the principle of forming a likelihood function and maximizing the function for parameter estimation. ML generally uses the numerical based iterative method, such as Newton–Raphson. However, the iterative methods proposed in the literature are generally computationally intensive. In this paper, an efficient technique utilizing differential evolution (DE) algorithm to enhance the estimation accuracy of maximum likelihood estimation has been presented. The [Formula: see text] of GA-Weibull, SA-Weibull, and DE-Weibull is 0.958, 0.953, and 0.973 respectively, and value of RMSE of DE-Weibull 0.0083, GA-Weibull (0.0104), and SA-Weibull (0.0110), for the yearly wind speed data are obtained. The lowest root mean square error and larger regression value for both monthly and yearly wind speed data indicate that the DE-Weibull distribution has the best goodness of fit and advocate the DE algorithm for the parameter estimation.

Optimization of a Small Wind Power Plant for Annual Wind Speed Distribution

This article presents a method to adjust the elements of a small wind power plant to the wind speed characterized by the highest annual level of energy. Tests were carried out on the basis of annual wind distributions at three locations. The standard range of wind speeds was reduced to that resulting from the annual wind speed distributions in these locations. The construction of the generators and the method of their excitation were adapted to the characteristics of the turbines. The results obtained for the designed power plants were compared with those obtained for a power plant with a commercial turbine adapted to a wind speed of 10 mps. The generator structure and control method were optimized using a genetic algorithm in the MATLAB program (Mathworks, Natick, MA, USA); magnetostatic calculations were carried out using the FEMM program; the simulations were conducted using a proprietary simulation program. The simulation results were verified by measurement for a switched reluctance machine of the same voltage, power, and design. Finally, the yields of the designed generators in various locations were determined.

Numerical Evaluation of the Performances of the Ventilation System in a Blast Furnace Casthouse

The blast furnace casthouse is a typical heavy-polluting factory building of a steel enterprise. During the tapping process and the taphole opening, the dust concentration in the factory building's workroom can reach tens of thousands mg/m³. Over time, the air pollutants in the workplace can have unwanted consequences on employees' health. This paper selected a typical blast furnace tapping workshop and measured on-site the characteristics of the wind speed distribution, temperature distribution, and soot concentration distribution during the taping period. A numerical simulation model was established to analyze the taphole smoke exhaust system's performance based on computational fluid dynamics. The findings are: the velocity, temperature, and smoke density distributions in the workplace were very uneven, and the thermal comfort of the workers' area was relatively poor. Also, the concentration of PM2.5 was mostly below 80μg/m³. Besides, due to the plant's on-site measurement, real data was obtained for numerical simulation verification, making the evaluation of the entire plant's ventilation performance more reliable. Hence, this article's finding provides a scientific basis for optimizing the air distribution in the blast furnace cast house's workplace.