Wind Speed Data Research Articles

Control and simulation of a stand-alone wind-hydrogen generation system

The potential to produce hydrogen from renewable resources is of considerable interest due to fears over man-made climate change and resource depletion. Hydrogen produced from renewable resources can be used to generate electricity through a fuel cell, or for other uses in a hydrogen economy. There are a number of potential system configurations for hydrogen production from electrolysis including grid connected and stand alone. In remote locations, stand-alone configurations are of interest, and may prove more economically viable than grid connected systems. In this paper a standalone wind - hydrogen generation system is designed and proposed to take advantage of an electrolyser capable of operating at very low power levels. A dynamic model of the system is presented, along with a maximum power point (MPPT) control algorithm of the system. The potential yield of such a wind-hydrogen stand-alone system located at the University of Glamorgan’s Hydrogen Centre is investigated using wind speed data collected at the site and the performance of the system under variable wind conditions determined.

Feasibility of Residential Wind Energy Generation in Puerto Rico

In Puerto Rico the rising cost of electrical energy from fossil fuels has created the necessity to use alternative energy sources for electricity generation. Wind energy, an accessible and clean source of energy, can help Puerto Rico achieve energy independence. Due to high population density, urban spread, lack of land use planning and the ever-growing concerns for the safety of flora and fauna, large-scale commercial wind generation has been met with increasing public opposition. Residential wind energy is most commonly used by homes and private land owners, making it less controversial. In this study we analyze recently obtained wind speed data from CariCOOS to help select a small residential wind turbine. An economical analysis of a residential wind turbine project is presented using the estimated energy produced.

Wind speed distributions in the Italian coasts

For offshore wind energy assessment it is necessary to appropriately model and describe wind climate. In this connection, the Rayleigh and Weibull distributions are widely suggested for offshore wind speed modelling. Although the use of these distributions is theoretically consistent, in practice, they are often proved to be inadequate. In two recently published papers some, less known, multi-parameter distributions (Johnson SB, Kappa and Wakeby) were introduced and proved to describe more accurately the stochastic behaviour of wind speed measurements obtained from buoys located in entirely different sea areas of the world. In order to evaluate their fitting performance with reference to coastal wind speed data, in this paper we assessed long-term time series obtained from ten meteorological land-based stations across the Italian coasts. The obtained results confirmed that the Johnson SB, Kappa and Wakeby distributions are of general validity for any wind data set analysed, since they performed fairly well for the modelling of coastal wind speeds as well. These distributions adapted better than the Weibull and are suggested as reliable and prominent candidates for the modelling of offshore and coastal wind speed in any sea area.

Evaluation of simple wind power forecasting methods applied to a long-term wind record from Scotland

We present an analysis of the ability to predict the power output from a nominal wind turbine or wind farm a few hours ahead using only locally available data – either the current and recent wind speed or power output. A third method combines the current state with knowledge of the long-term climatology. The wind speed data were taken from a 46-year long record of hourly readings at a Scottish coastal site and converted to power output and electricity production using a generic wind turbine power curve. The wind speed data or the calculated power output at a given time were used to predict the output a few hours ahead, either using persistence, a linear model, or a model based on the mean daily cycle extracted from the long-term record. Since many wind farm operators base their forecast on current wind speed or output measures alone, this analysis will provide some quantification of the quality of this approach, either to help them plan their operation or be able to put these simple methods in quantitative context of more complex methods.

A short-term wind speed prediction method based on the BLS–RVM hybrid model

Abstract The study introduces a novel approach to short-term wind speed forecasting, which amalgamates statistical and machine learning techniques through the utilization of a hybrid model consisting of the broad learning system (BLS) and the relevance vector machine (RVM), to account for the nonstationary characteristics of wind speed data. Firstly, the initial wind speed time series is preprocessed using singular spectrum analysis to achieve noise reduction. Subsequently, the denoised wind speed time series is predicted using generalized learning system (BLS), and the prediction error is obtained. Finally, the prediction error of BLS is further predicted using RVM, and the final prediction result is obtained by combining it with the prediction results of BLS. By integrating multiple algorithms, this novel wind speed forecasting hybrid model improves prediction accuracy and can adapt to different wind speed characteristics and complex wind speed fluctuations. Through a case study, we find that this model outperforms other comparative models in terms of prediction, fully demonstrating its superiority.

Short-term wind speed forecasting based on a hybrid model that integrates PSO-LSSVM and XGBoost

Abstract A groundbreaking method is proposed to mitigate the impact of unpredictable fluctuations in wind velocity on wind power generation. This innovative approach integrates the particle swarm optimization (PSO)-least squares support vector machine (LSSVM) and XGBoost models in a harmonious manner. Initially, the raw wind speed data is subjected to wavelet threshold denoising to reduce noise and volatility. For short-term wind speed prediction, a PSO-LSSVM-XGBoost model is introduced. After the initial wind speed sequence undergoes wavelet threshold denoising, the enhanced sequence is forecasted using the LSSVM model, with its hyperparameters optimized through the PSO algorithm. The errors, obtained by subtracting the predicted values from the original data, are compensated using XGBoost. The final forecast results combine the rectified error data with the initial projected results. Experimental findings demonstrate the model’s remarkable capability to enhance prediction performance and accuracy.

Synthesis Weibull Stochastic Differential Equation: Properties and application

The Weibull distribution is suitable for modeling stochastic phenomena. However, most of the existing stochastic differential equations (SDEs) of Weibull type found in the literature do not follow the Weibull distribution, making them unsuitable for modeling phenomena that follow a Weibull random variable. To address this issue, this paper proposes a new SDE known as the Synthesis Weibull Stochastic Differential Equation (SWSDE) using the synthesis method. Some properties and numerical studies were carried out. The usefulness of the model is also demonstrated using Malawian wind speed data. The results show that the proposed SDE is suitable for predicting the data. The model also demonstrates a mean-reverting property, whereby the equilibrium is the solution of a differential equation in simulation studies. Additionally, the solutions of the model are found to always follow the Weibull distribution.

Assessment of wind energy potential in Zambia

An assessment of potential for wind energy in Zambia was carried out to help address the shortage of energy due to increasing energy needs arising from energy requirements for newly opened mining industries and unreliable hydropower generation due to climate change. The assessment was carried out by collecting wind speed data of 25 sites owned by Zambia Meteorological Department. The objective of the study was to analyse wind patterns and determine areas in Zambia where production of electricity from wind can be invested. The analysis was based on the use of Weibull shape factor and yielded 3.32 m/s wind speed and 13.2 shape factor from a site with the most suitable wind speeds (Kasama Meteorological Station). A correlation study was carried out using these results and Goldwin 1.5 MW wind turbine of model GW82/1500. The study results showed that 44.46 kW power output could be obtained at 3.83 m/s wind speed by a correlation equation of this turbine power output with wind speeds. A series of similar power output results were obtained for wind speeds recorded in the years from 2013 to 2021 and compared with those obtained in the same period by using model equation due to Al Buhariri, and found that the power outputs compared very closely at low wind speeds ranging 3 to 3.2 m/s but rather widely at higher wind speeds.This study is significant in that it provides information on decision making and helps determine policy direction for wind utilization in Zambia and it can also help influence investment decision by Zambian mining companies who have financial ability to undertake large scale investments.

Large scale integration of wind power – influence of geographical allocation

This paper investigates the influence of geographical allocation of wind power generation in Northern Europe, assuming large scale integration of wind power. The work applies a linear cost optimization model of the heat and power sector with a 1-hour time resolution. The model minimizes the sum of running costs to meet the heat and power demand and the wind power and transmission investment costs. Wind data are taken from modelled wind speed data from the Swedish Meteorological and Hydrological Institute. The Nordic countries and Germany were divided into regions and the 200 sites with the highest yearly output were chosen to represent the region. The model gives the most favourable distribution of wind power between the regions. In addition, the paper provides an assessment of the effect of geographical distribution of wind power with respect to influence on the aggregated wind power production (only considering the wind power generation itself). The modelling results show that the largest investments in wind power are made in the windy region of Southern Norway. However, depending on the cost of transmission allocating wind power near large load centers in Germany may also be favourable. As for the assessment of distribution of wind power, the wind data gives that if the 400 best sites in Europe were used, this would result in a capacity factor of 38.5% and a lowest output of 2.5 % of rated power (applying 2009 wind data).

Optimal placement of fixed hub height wind turbines in a wind farm using twin archive guided decomposition based multi-objective evolutionary algorithm

Harnessing maximum wind energy's power output and efficiency is vital to combat environmental challenges tied to conventional fossil fuels. Wind power's cost-effectiveness and emission reduction potential underscore its significance. Efficient wind farm layout plays a pivotal role, both technically and commercially. Evolutionary algorithms show their potential while solving multi-objective wind farm layout optimization problems. However, due to the large-scale nature of the problems, existing algorithms are getting trapped into local optima and fail to explore the search space. To address this, the TAG-DMOEA algorithm is upgraded with an adaptive offspring strategy (AOG) for better exploration. The proposed algorithm is employed on a wind farm layout problem with real-time data of wind speed and direction from two different locations. Unlike mixed hub heights, fixed hub heights such as 60, 67, and 78 m are adopted to conduct the case studies at two potential locations with real-time statistical data for the investigation of improved results. The results obtained by TAG-DMOEA-AOG on six cases are compared with 10 state-of-the-art algorithms. Statistical tests such as Friedman test and Wilcoxon signed rank test along with post hoc analysis (Nemenyi test) confirmed the superiority of the TAG-DMOEA-AOG on all cases of the considered multi-objective wind farm layout optimization problem.

Economic Analysis of Wind Turbines in Oil and Gas Sector

Diesel generators or gas turbines located in the platforms usually do electricity production in oil and gas platforms. Using the above equipment, taking into account safety issues to prevent explosions and fires, as well as fueling them offshore, will be very expensive. In addition, the mentioned devices emit a significant amount of CO2 and NOx. Therefore, the use of renewable energy instead of fossil fuels can be much more economical and environmentally friendly. This study attempts to investigate the potential of using wind energy in the Persian Gulf and the feasibility and detailed economic analysis of using wind turbines on oil and gas platforms to provide part of the energy required. For this purpose, wind speed data are extracted from measurements at 10 meters, 30 meters and 40 meters above the ground. Average wind speed for the mentioned levels as 4.45 m/s, 4.99 m/s and 5.34 m/s respectively. In addition, it gives the annual power density as 128.36 W/m2, 173.80 W/m2 and 210.42 W/m2. The results obtained by using RETScreen software show that in this project with a life span of 30 years, the economic output of wind turbine systems is profitable, so that after 8.2 years, the entire cost of the project will return.

Design of a wind-PV system integrated with a hybrid energy storage system considering economic and reliability assessment

Hybrid energy systems (HESs) have garnered significant attention as a sustainable solution to meet the world's growing energy demands while minimizing environmental impact. Achieving cost-effective and resilient HES solutions requires considering economic and reliability factors for optimal design. This research delves into the optimization and design of a wind-PV system integrated with a hybrid energy storage system using the Multi-Objective African Vultures Optimization Algorithm (MOAVOA) in both standalone and grid-connected modes. A comprehensive case study is conducted in Tabuk, Saudi Arabia, focusing on a microgrid and incorporating wind speed, solar radiation, and load data. The effectiveness of MOAVOA is verified using the Non-Dominated Sorting Genetic Algorithm-II (NSGA-II) and Multi-Objective Particle Swarm Optimization (MOPSO) methods in achieving the Pareto front of optimal system designs. Then, the mean, entropy, and criteria importance using the inter-criteria correlation (CRITIC) methods are utilized to assign weights to each objective in the obtained Pareto front. Finally, the Technique for Order Preference by Similarity to the Ideal Solution (TOPSIS) method is employed to select the optimal system design from the Pareto front. The results underscore the significance of the optimization approach in assessing the cost, dependability, and ideal configurations of the system. Furthermore, they highlight the diverse outcomes produced by different optimization techniques. While the entropy method yields more cost-effective designs, the mean and CRITIC methods produce comparable results. However, the mean and CRITIC approaches exhibit higher levels of reliability. For instance, in the grid-connected mode, the entropy weighting method yields an optimal system design with a total cost of M$ 42.2 and a reliability index of 91.73 %, while using the mean method, the selected system configuration has a total cost of M$ 42.98 and a reliability index of 92.89 %. The study emphasizes the benefits of diversifying renewable resources by considering different scenarios involving wind and solar generation. For example, in the wind-PV grid-connected system, the total cost is 22.65 % less than in the PV-only grid-connected system with a higher system reliability. The findings provide valuable guidance for system designers in selecting optimal optimization techniques and promoting the integration of renewable energy sources in hybrid energy systems. By presenting a comprehensive framework that incorporates economic and reliability assessments through the utilization of MOAVOA, this study contributes to the field of HES design.

Enhancing rock and soil hazard monitoring in open-pit mining operations through ultra-short-term wind speed prediction

Wind speed exacerbates challenges associated with rock stability, introducing factors such as heightened erosion and the possibility of particle loosening. This increased sensitivity to erosion can result in material displacement, thereby compromising the overall stability of rock layers within the open-pit mining site. Therefore, accurate wind speed predictions are crucial for understanding the impact on rock stability, ensuring the safety and efficiency of open-pit mining operations. While most existing studies on wind speed prediction primarily focus on making overall predictions from the entire wind speed sequence, with limited consideration for the stationarity characteristics of the sequence, This paper introduces a novel approach for effective monitoring and early warning of geotechnical hazards. Our proposed method involves dividing wind speed data into stationary and non-stationary segments using the sliding window average method within the threshold method, validated by the Augmented Dickey-Fuller test. Subsequently, we use temporal convolutional networks (TCN) with dilated causal convolution and long short-term memory to predict the stationary segment of wind speed, effectively improving prediction accuracy for this segment. For the non-stationary segment, we implement complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) to reduce sequence complexity, followed by TCN with an attention mechanism (ATTENTION) to forecast wind speed one step ahead. Finally, we overlay the predictions of these two segments to obtain the final prediction. Our proposed model, tested with data from an open-pit mining area in western China, achieved promising results with an average absolute error of 0.14 knots, mean squared error of 0.05 knots2, and root mean squared error of 0.20 knots. These findings signify a significant advancement in the accuracy of short-term wind speed prediction. This advancement not only enables the rapid assessment and proactive response to imminent risks but also contributes to geotechnical hazard monitoring in open-pit mining operations.

Comprehensive typhoon hazard zoning in China based on historical records

The formulation of nationwide goals, strategic plans, and specifically zoned areas for typhoon hazard mitigation and prevention is paramount for addressing the threat in a spatially explicit manner. Utilizing historical rainfall and wind speed data associated with typhoon disasters in the Northwest Pacific and South China Sea from 1949 to 2017, we proposed a comprehensive typhoon hazard zoning delineated based on the assessment of typhoon disaster risks, considering hazard-causing factors, hazard-formative environments, and hazard-bearing bodies through an Analytic Hierarchy Process. High-risk areas were primarily located in the coastal regions of South and Southeast China. Medium-risk areas were situated in the Yangtze River Delta and towns adjacent to Bohai Bay. Low-risk areas were primarily located in the central region of Guangxi, the Guangdong-Hong Kong-Macao Greater Bay Area, the vicinity of Fuzhou, the majority of southern Jiangsu and northern Zhejiang, all of Shanghai, the northern and southern parts of the Shandong Peninsula, the area around Bohai Bay in Tianjin, a minor portion of Hebei, and the three northeastern provinces. Appropriate mitigation measures were proposed to address the threats posed by typhoons in these regions. This information is instrumental in providing reliable support for China in addressing the challenges posed by typhoons.

Short-term wind speed prediction based on improved Hilbert–Huang transform method coupled with NAR dynamic neural network model

Wind energy, as a renewable energy source, offers the advantage of clean and pollution-free power generation. Its abundant resources have positioned wind power as the fastest-growing and most widely adopted method of electricity generation. Wind speed stands as a key characteristic when studying wind energy resources. This study primarily focuses on predictive models for wind speed in wind energy generation. The intense intermittency, randomness, and uncontrollability of wind speeds in wind power generation present challenges, leading to high development costs and posing stability challenges to power systems. Consequently, scientifically forecasting wind speed variations becomes imperative to ensure the safety of wind power equipment, maintain grid integration of wind power, and ensure the secure and stable operation of power systems. This holds significant guiding value and significance for power production scheduling institutions. Due to the complexity of wind speed, scientifically predicting its fluctuations is crucial for ensuring the safety of wind power equipment, maintaining wind power integration systems, and ensuring the secure and stable operation of power systems. This research aims to enhance the accuracy and stability of wind speed prediction, thereby reducing the costs associated with wind power generation and promoting the sustainable development of renewable energy. This paper utilizes an improved Hilbert–Huang transform (HHT) using complementary ensemble empirical mode decomposition (CEEMD) to overcome issues in the traditional empirical mode decomposition (EMD) method, such as component mode mixing and white noise interference. Such an approach not only enhances the efficiency of wind speed data processing but also better accommodates strong stochastic and nonlinear characteristics. Furthermore, by employing mathematical analytical methods to compute weights for each component, a dynamic neural network model is constructed to optimize wind speed time series modeling, aiming for a more accurate prediction of wind speed fluctuations. Finally, the optimized HHT-NAR model is applied in wind speed forecasting within the Xinjiang region, demonstrating significant improvements in reducing root mean square errors and enhancing coefficient of determination. This model not only showcases theoretical innovation but also exhibits superior performance in practical applications, providing an effective predictive tool within the field of wind energy generation.

Modeling the dependency relationship between wind speed and wind power generation: An application of copula theory

The concern with global warming and pollution has increased interest in the development of renewable energy sources, which are less aggressive to the environment. Wind energy can provide adequate solutions to the above-mentioned problems. The use of this energy eliminates unwanted waste that is harmful to health and the environment from other energy sources such as coal and nuclear power plants. This work aims to analyse the dependence relationship between wind speed and wind energy production, a rather complex relationship, so this study seeks to understand the stochastic nature of both phenomena. As a methodological tool the copula theory was used. A copula function is used as a general method, which consists of formulating multivariate distributions so that different dependency structures can be represented. That is, the study is based on the analysis and modelling of the dependence between wind speed data and electrical energy generation, for an hourly database of a wind farm in the state of Bahia, collected in the entire year of 2017. Thus, this paper proposes a study aiming the search for the copula function referring to the data in the period mentioned.

Regression learner machine learning approach to predict wind speed considering various parameters and integration of DG in mesh distribution system through GWO

ABSTRACT The escalating global demand for electricity is driving a significant expansion in the size and complexity of electric power systems. Explosive growth strains power distribution, creating challenges for operators. Among these challenges, the imperative to minimise power losses is crucial. To enhance power efficiency and quality, Distributed Generation (DG) technology, particularly wind energy, is being integrated into distribution systems. However, the variable nature of wind speed poses a significant challenge to the seamless integration of wind energy into grids. To address this challenge, wind speed prediction methods are explored. In this work, a Machine Learning framework is employed for multivariate wind speed forecasting in Tamil Nadu, India 8.0883 ∘ N , 77.5385 ∘ E . Key performance metrics, such as Root Mean Square Error (RMSE) and Mean Absolute Percentage Error values (MAPE), are used to assess the accuracy of the wind speed predictions. The predicted wind speed data is then utilised to estimate wind farm power output which is seamlessly integrated into the distribution system. Load Flow analysis is conducted using the robust and straightforward current injection method, focusing on IEEE-33 and IEEE-69 bus-balanced Mesh distribution systems. The aim is to determine the impact of wind-powered Distributed Generation on power losses within these systems.

Football team training algorithm: A novel sport-inspired meta-heuristic optimization algorithm for global optimization

A more efficient optimization algorithm has always been the pursuit of researchers, but the performance of the current optimization algorithm in some complex test functions is not always satisfactory. In order to solve this problem, a new meta-heuristic optimization algorithm—Football Team Training Algorithm (FTTA) is proposed according to the training method of the football team, which simulates the three stages of the training session: Collective Training, Group Training and Individual Extra Training. By the test on two groups of test functions, CEC2005 and CEC2020, the proposed optimization algorithm (FTTA) achieves the best results, which far exceeds the traditional Grey Wolf Optimization (GWO), Whale Optimization Algorithm (WOA) algorithms and so on. In the engineering application, a new hybrid wind speed prediction system is proposed based on FTTA. The FTTA is used to optimize variational mode decomposition (VMD) to improve the effect of data denoising. At the same time, based on unconstrained weighting algorithm, FTTA and combination prediction model build a new hybrid prediction strategy. Through the experiments on four groups of wind speed data in Dalian, the accuracy, stability, advancement, and CPU running speed of the system are verified. It is obvious that the practical application ability of the system is much better than previous methods, which can effectively improve the utilization efficiency of renewable energy.

An empirical and a dynamic-empirical model for the estimation of maize seedbed temperature

Soil temperature is the main factor in determining the germination of maize seeds and the emergence time of the crop. It controls the rate of phenological development while the meristem is underground, which is until the V6 (six leaf collar) stage of maize. The research performed by the authors aimed to model maize seedbed temperatures at sowing depth (soil temperature at 5 cm depth) during the sowing-emergence-early development period. The research is based on measurements in ploughed plots of the maize experiments at the Látókép Experiment Site of the University of Debrecen (Eastern Hungary) in two growing seasons of 2021–2022. Two types of empirical models were established, a multilinear regression model (M1) and a dynamic-empirical model (M2), where the daily increase and decrease of soil temperature are determined by multilinear regression. Candidates for input variables for both models were various, easily available daily meteorological parameters. M2 model performed better than M1 when applied to an independent database of 2022. This is particularly valid for the maximum and minimum soil temperatures. It was found that both M1 and M2 can be used to predict the soil temperature of the maize seedbed before shading by the plants. For daily mean temperature, M1 and M2 give a similarly good estimation, while the dynamic-empirical model has to be preferred for the maximum and minimum temperatures. M2, which is based on daily temperature, global radiation and wind speed data, predicts the daily mean (RMSE = 1.4 °C), maximum (RMSE = 2.2 °C), and minimum (RMSE = 1.6 °C) of seedbed temperature not worse than many earlier soil temperature models do, even hybrid or mechanistic ones with a large number of parameters.

Neutrosophic Control Chart for Rayleigh Quality with Applications To Wind Speed Data

The application of neutrosophic statistics provides a novel approach to dealing with uncertain and imprecise data problems. In this study, we present an improved method called neutrosophic Rayleigh exponential weighted moving average chart. The chart is an extension of the traditional model and can be applied in various fields. The proposed scheme is designed to enhance the detection capability of the traditional chart. The key features of the suggested chart are discussed, highlighting its capability to handle vague, indeterminate, and fuzzy data situations. We evaluate the performance of the proposed scheme by analyzing the designated limits and charting parameters for different sample sizes. Moreover, we establish the performance metrics of the chart such as neutrosophic run length ( ) and neutrosophic power curve ( ).Performance metrics demonstrate that the chart is highly sensitive to persistent shifts in the scaling parameter of the neutrosophic Rayleigh distribution. Monte Carlo simulations are conducted to compare the suggested scheme with the existing model. A comparative study indicates that the proposed chart outperforms the competing design, particularly in detecting smaller shifts. Finally, we provide a charting structure for the proposed design using daily average wind speed data, which can be used as a practical implementation guideline for real-world applications.