Bayes Estimation of Wind Speed Extreme Values

ABSTRACTThe present article utilizes wind measurements from three buoys data collection stations in Ionian Sea to study the wind speed and power characteristics using the Weibull shape and scale parameters. Specifically, the site dependent, annual, and monthly mean patterns of mean wind speed, Weibull parameters, frequency distribution, most probable wind speed, maximum energy carrying wind speed, wind power density and wind energy density characteristics have been analyzed. The Weibull distribution was found to represent the wind speed distribution with more than 90% accuracy, in most of the cases. Moreover, the correlation between the percentages of times the wind speed was above cut-in-speed and the measured mean wind speed for the three selected sites, as the correlation between the aforementioned percentages and the scale parameter c were examined and were found linear. At all these sites, no definite increasing or decreasing trends in annual mean wind speed values could be detective over the data reporting period. The mean values of wind speed, scale parameter, most probable wind speed, maximum energy carrying wind speed, wind power and wind energy density values showed higher values during winter time and lower in summer time in Pylos and Zakynthos. Moreover, Pylos and Zakynthos were found to be the best sites from wind power harnessing point of view.

The probability distributions of wind speeds and the availability of wind turbines were investigated by considering the vertical wind shear. Based on the wind speed data at the standard height observed at a wind farm, the power-law process was used to simulate the wind speeds at a hub height of 60 m. The Weibull and Rayleigh distributions were chosen to express the wind speeds at two different heights. The parameters in the model were estimated via the least square (LS) method and the maximum likelihood estimation (MLE) method, respectively. An adjusted MLE approach was also presented for parameter estimation. The main indices of wind energy characteristics were calculated based on observational wind speed data. A case study based on the data of Hexi area, Gansu Province of China was given. The results show that MLE method generally outperforms LS method for parameter estimation, and Weibull distribution is more appropriate to describe the wind speed at the hub height.

This study focuses on assessing wind energy potential in the windiest region of Hungary, near Mosonmagyarvr.Over a span of 6 years (2010)(2011)(2012)(2013)(2014)(2015), data on wind speed, direction, and energy production were collected from a wind farm featuring E40 Energon wind turbines.The objective is to enhance weather forecasting by utilizing historical wind data for specifying wind distribution forms.The E40 turbine, positioned at a height of 65 m, exhibits a mean wind speed of 5.26 2.8 m/s with 10-minute time resolution measurements and an average power output of 103 kW (17% of the maximum).The study employs the Weibull distribution to model wind speeds and employs the Power Density method for scale and shape parameter estimation, favouring a computationally efficient approach.Parameters such as scale () and shape () are derived as functions of mean wind speed, wind sectors, and energy pattern factor.Overall, this research contributes to understanding wind energy viability in the region, considering wind patterns, energy production, and statistical modelling techniques.

Due to their advantages over onshore wind farms, such as higher average wind speeds and less land occupation, the development of large-scale offshore wind farms (OWFs) has generated a lot of attention. In order to take into consideration the effects of collecting system component failure as well as seasonal variations in wind speed, a model based on the Markov chain Monte Carlo (MCMC) method for OWF dependability evaluation is created in this study. Then, K-means clustering is used to separate the historical wind speed data into the several states. By taking seasonality into consideration and taking into account the probability distribution of wind speed specific to each wind speed level, the transition probability between various wind speed levels is computed. By taking into account the probability distribution of wind speed inside each wind speed state, MCMC is used to establish a wind speed simulation model with seasonal characteristics. The MCMC-based OWF component state simulation method is applied to generate OWF components states time series. The graph theory method is used to examine the topological connectivity of offshore wind farm systems for each state produced by the proposed model. Then the reliability evaluation procedure of OWF based on MCMC model is used to obtain the reliability index. Taking an OWF as an example, the proposed model's accuracy and efficacy are confirmed by comparison with other models, and the impact of collecting system component failure on the dependability of OWFs is examined.

Analysis and application of forecasting models in wind power integration: A review of multi-step-ahead wind speed forecasting models

Appropriate wind speed modeling for generating synthetic wind speed data is crucial in microgrid reliability evaluation studies. This paper proposes a 2-D wind speed statistical model based on historical wind speed data. The first dimension of the proposed wind model focuses on the probability distribution on the time duration of different wind speed scales, while the second dimension focuses on the probability distribution of wind speed in each wind speed scale. Unlike traditional wind speed models, the proposed model can simultaneously deal with the probabilistic characteristics of wind speed and wind time duration. This paper also presents a 2-D wind model-based data sampling method, and incorporates it into the microgrid reliability assessment algorithm. Using historical wind speed data in Tianjin, China, it shows that the proposed 2-D wind speed statistical model helps to fully simulate the volatility of wind energy. Furthermore, the proposed wind speed model is applied on the microgrid reliability evaluation study, which shows that the proposed model can be effectively utilized by planners to conduct reliability evaluation for microgrid.

A comparison between wind speed distributions derived from the maximum entropy principle and Weibull distribution. Case of study; six regions of Algeria

The requirement for energy has expanded day by day due to excessive consumption, developing technology, and population growth, especially after the Industrial Revolution. Since the investments for promoting renewable energy sources, The Turkish energy sector has been progressing in the way of renewable energy. This inevitable change in the energy sector and variable power generation values in wind energy directly affect the energy markets. Accompanying with the structural transformation of the energy sector, Turkey had launched the Day-Ahead electricity marketing system in December 2011. The price per megawatt-hour is determined on the open market by the supply and demand. Hatay province was selected in this study because of its highest wind potential. Temperature and wind speed values, observed in Hatay Samandağı for October 2019, were determined. The data was configured with The Weather Research and Forecasting Model. The results indicate that the wind speed at high altitudes could reach above 7 meters per second on Turkey’s eastern Mediterranean coast, Hatay. The results were compared with the real-time data, and the error rates were determined. In the selected region, the electrical energy values generated using the model results were calculated the day before.

In a power system analysis, it is useful to find the relation between power losses and generated power on a power system. It can be performed using loss coefficients. The main drawback of traditional loss coefficients is that they must be recalculated in case that change in conditions takes place in the power system. In this paper, a comparison is performed by using different estimation methods of loss coefficients. The first method is the traditional Kron method (KM); the second one is a proposed modification of the traditional incremental method (IM); finally, a heuristic estimation method, robust under different changes in conditions of power systems, is proposed. Additionally, the paper presents an application of power losses estimation in power systems with solar and wind energy. The stochastic behaviour of solar and wind energy is modelled using the probability distribution of solar irradiance and wind speed, respectively.

With the increasing consumption of fossil fuel, reducing greenhouse gas (GHG) emissions has become a serious issue that has attracted worldwide attention. Therefore, Lebanon is currently interested in utilizing renewable energy technologies to reduce energy dependence on oil reserves and GHG emissions. The present study is focused on solar and wind power potential and the economic viability of wind/solar systems for the Rayak region in Lebanon for the first time. The input data sources for the study including the Meteorological data for wind speed and NASA database for solar energy. In the assessment of wind energy, a two-parameter Weibull distribution function was used to analyze the characteristics of wind speed. Yearly and seasonal Weibull parameters were calculated for 10 m height using the Maximum likelihood method. In addition, yearly and seasonal wind power density values were calculated. The results showed that the mean annual wind speed and wind power density values were 5.884 m/s and 124.534 W/m2, respectively, during the investigation period. It can be concluded that the value of wind power density at the region was classified as marginal wind power potential and high-scale wind turbines can be used to gather wind energy potential in the region. Furthermore, predicting wind speed depends on various atmospheric factors and random variables. Therefore, 63 ANN models are developed by varying the meteorological parameters to predict the daily wind speed in the selected region. All the models with various combinations are validated and the performances of the models are analyzed using root mean squared error. The results demonstrated that the most relevant input variables for predicting the daily wind speed were found to be temperature, pressure, and relative humidity. In the assessment of wind energy, average monthly global solar radiation data were evaluated. It is found that the annual global solar radiation was 1877.41 kWh/m2, which indicates the selected region has high solar resources and is categorized as an excellent potential class. Moreover, this study provides a comprehensive and integrated feasibility analysis of 100 MW grid-connected wind and solar projects economic projects that can be developed in the country to reduce the electricity crisis and GHG emissions. Several different economic and financial indicators were calculated. The results indicate that the wind farm is a more economical option than the solar plant because of the higher values of NPV, BCR, ALCS, and IRR as well as the lower values of EB, SB, and LCOE.

Anemometer is a device used to measure wind speed at weather measurement stations. An Anemometer, which is used to measure the wind speed of an area, is widely used in weather stations. With the anemometer, wind speed and weather forecasts of a certain region are made. In this study, the wind speed values of a sample anemometer in a certain region and the voltage produced by the anemometer for wind energy were remotely monitored via the Internet of Things. In the system created, Arduino Uno microcontroller development board for the control of the system, a sample anemometer for measuring wind speed and generating voltage, LCD shield for displaying wind speed and voltage values on the screen, esp8266 wifi module for sending instant data from the anemometer to the thingspeak IoT platform and for feeding the system. solar panel and battery charge regulator are used. In this study, the wind speed in a certain region and the monitoring of voltage produced depending on the wind speed was monitored on an internet basis. The system was operated in a certain region and the wind speed values and the generated voltage were monitored both instantaneously on the LCD screen and graphically on the thingspek an IoT platform as instantaneous, hourly, daily and weekly. With this system created, the thingview application was used for instant tracking on android devices.

There is a growing need for an environmentally friendly source of energy that can replace the conventional fossil fuel energy. This is because the effects of global warming is becoming very obvious, as evidenced by the severe flooding that occurred in the U.S. in 20171. Two notable solutions to this dilemma are wind and solar energy. Solar powered devices derive their energy from the sun, hence, the amount of energy is severely limited during the cold months of the year when solar intensity is typically low. Wind energy, on the other hand is most prevalent during this cold months when the wind speed is typically higher. The aim of this research is therefore to conduct a comprehensive assessment of wind energy potential in Michigan City, Indiana, United States. This information will allow homeowners and investors with interest in alternative energy to make critical decisions in this regard. The study was conducted using wind speed data collected over a five-year period from 2012 to 2016. In this work, we have also determined the best method for evaluating the Weibull parameters (shape and scale factors) for wind data analysis. The site average wind speed ranged from 4m/s to 9m/s with a peak in the winter months and minimum in the summer months. The wind speed with the maximum energy at the hub height varied between 5.84 m/s in August 2016 to 12.79 m/s in October 2012 with annual average speeds between 8.85 and 9.35 m/s and a five-year average of 9.13 m/s. The prevailing wind speed was within the range of 4–8m/s and strongest on the Southern part of the site especially in the South Southeastern direction. Consequently, siting a wind turbine on the Southern part of the City would generate more energy than on any other direction. Among the turbines analyzed, ITALTECH 250 will yield the maximize energy production with a capacity factor of 0.385 and average annual energy production of 840 MWh/yr. The results presented in this work proves the great potential for investments in wind energy in Michigan City.

The purpose of the study is to determine the features of the spatial distribution of the wind speed in Marrakesh - Safi region in 2021-2050, as well as the distribution of the specific power of the wind flow at various altitudes above the earth’s surface to determine the wind class of the area in the coming decades. Currently, the region has two large wind farms: Essaouira-Amogdoul and Tarfayer. To assess the future state of climate in Marrakesh − Safi region, the results of calculations of regional climate models (RCM) of the CORDEX-Africa project for the period 2021-2050 were used. The RCM modeling was carried out for the region of Africa, in a rectangular coordinate system with a spatial resolution of ~ 44 km. Model calculation was performed taking into account the greenhouse gas concentration trajectory of RCP 4.5. As a result of simulation for the period 2021-2050, mean monthly values of wind speed "sfcWind" (m·s-1) and the daily maximum near-surface wind speed "sfcwindmax" (m·s-1) at 10 m height were obtained. Then, based on the wind speed rows, the values of the wind power density at a height of 50 m and 100 m were calculated. The results of model calculations of wind speed showed that the ensemble mean of wind speed for the period 2021-2050 will be from 3.8 m∙s-1 in Kelaat Sraghna Province to 7.2 m∙s-1 on the stretch of the Atlantic coast between Cap Sim and Cap Tafelny.The distribution over the territory will be influenced by proximity to the ocean, models predict the highest wind speeds on the coast, and when moving deep into the region, the wind speed will decrease.The analysis of simulation results showed that in the coastal areas of the region favorable conditions in terms of wind energy development will remain, and the highest wind speeds of the model are predicted on the Atlantic coast between Cap Sim and Cap Tafelny. By the size of the specific power of the wind flow, significant wind resources will have the territory lying along the coast from Cap Sim to the southern border of the region, and in the area of the power plants Essaouira-Amogdoul and Tarfayer models predict the conditions corresponding to the outstanding wind power class.

Satellite observations of the ocean surface provide a powerful method for acquiring global data on wind speed and wave height. Radar altimeters have now been in operation for more than 25 years, providing a reasonably long term data set with global coverage. This paper presents data from a fully calibrated and validated altimeter dataset. The dataset provides the basis for obtaining a global perspective of a number of parameters critical to ocean engineering design, ship operations and global climate change. Analysis of the data provides ocean climatology of mean monthly values of wind speed and wave height useful for ship operations. The data set is also sufficiently long to provide extreme value (i.e. 100-year return period) estimates of wind speed and wave height. The paper presents such values and describes the approaches most appropriate to obtain statistically significant extreme value estimates from such satellite data. With a data set of this length, it is possible to investigate whether there have been statistically significant changes in the wind and wave climates over the period. Careful trend analysis of the extensive data set shows that there has been a statistically significant increasing trend in mean wind speed over the period. The corresponding increase in wave height is less clear. There is also evidence to suggest that extreme wind speeds and wave heights are increasing and the data set is analysed to investigate these trends. The paper clearly shows the value of this dataset and its application to a range of engineering problems.

The probability distribution of wind speed and availability of wind farms are investigated considering the vertical wind shear. Wind speed at extended height (60 m) was generated from that of reference height (10 m) through the Power-law Process. The least square (LS) method, maximum likelihood estimation (MLE) method and modified method are used to estimate the parameters of Weibull distribution. Then the goodness-of-fit and wind energy characteristic indices are deduced. It shows that MLE and modified MLE method are superior to LS method in parameter-estimating, and wind speed distribution at different height changes with wind shear. The rationality and effectiveness of the modified method are verified from the case study.