Wind Stations Research Articles

A machine learning approach to nearshore wave modeling in large lakes using land-based wind observations

This study presents a novel machine learning-based (ML) framework that utilizes the ConvLSTM-1D model to simulate wave heights at a nearshore location in Lake Michigan using a nonuniform land-based array of wind observations from a broad geographic region as an input. This approach was applied to Lake Michigan to perform a 70-year ice-free hindcast of waves near Chicago, IL (USA). Because of annual winter gaps in Great Lakes buoy observations, output from the Wave Information System model (WIS) was used for the training, validation, and testing of the ML model. Models with different numbers of wind stations were tested, showing that a single wind station as an input feature produced a reasonably accurate wave height. However, the wave height model accuracy increased as more wind input data was included from around the lake, largely plateauing beyond the inclusion of four stations that spanned Lake Michigan’s southern basin. The optimized model lookback period was found to be 10 h for all models, suggestive of a fetch-limited temporal coupling between the wind observations and nearshore waves. The extended time series showed a statistically significant increase in the interannual standard deviation of the storm wave height and the storm height over the mean wave height while no correlation was found with the annual water level time series. The ML framework offers a promising avenue for utilizing historical wind records to extend wave height time series for nearshore locations, particularly in enclosed and semi-enclosed basins where waves are strongly linked to local winds.

Post-Processing Maritime Wind Forecasts from the European Centre for Medium-Range Weather Forecasts around the Korean Peninsula Using Support Vector Regression and Principal Component Analysis

Accurate wind data are crucial for successful search and rescue (SAR) operations on the sea surface in maritime accidents, as survivors or debris tend to drift with the wind. As maritime accidents frequently occur outside the range of wind stations, SAR operations heavily rely on wind forecasts generated by numerical models. However, numerical models encounter delays in generating results due to spin-up issues, and their predictions can sometimes exhibit inherent biases caused by geographical factors. To overcome these limitations, we reviewed the observations for the first 24 h of the 72-hour forecast from the ECMWF and then post-processed the forecast for the remaining 48 h. By effectively reducing the dimensionality of input variables comprising observation and forecast data using principal component analysis, we improved wind predictions with support vector regression. Our model achieved an average RMSE improvement of 16.01% compared to the original forecast from the ECMWF. Furthermore, it achieved an average RMSE improvement of 5.42% for locations without observation data by employing a model trained on data from the nearest wind station and then applying an adaptive weighting scheme to the output of that model.

Wind Potential Assessment for Songkhla, Thailand

The analysis and evaluation of wind energy projects are important to understand their economic and environmental suitability for installation and operation. Using wind data from the Meteorological Department, Used WAsP software for calculating, simulating, and analyzing the Annual Energy Production (AEP) and then applied it to calculate the Levelized Cost of Electricity (LCOE) of wind turbines installed in the Songkhla province area. The selected areas are suitable for all three models of wind turbines in the simulation. From the wind data analysis, wind direct was found that in the east sector (sector 4), there is the highest wind frequency at the wind station. The simulation results from Laem son-on area and Hua khao area show suitable mean wind speeds for wind farm installations. In the wind turbine simulation, three models were compared: Bonus 300 kW MkIII, Bonus 1.3 MW, and PowerWind 56 900 kW. At the Songkhla cape area, the positions with the highest AEP were 132.485 MWh, 344.419 MWh, and 332.597 MWh, respectively. Hua Khao area, the positions with the highest AEP were 225.284 MWh, 586.303 MWh, and 540.045 MWh, respectively. In the LCOE calculations for all model of wind turbines, at both Laem Son- On and Hua Khao locations, the most cost-effective wind turbine is the Bonus 300 kW. It has an LCOE of $142.43/MWh and $83.76/MWh, respectively. Therefore, the most cost-effective location is Hua Khao.

Simultaneous forecasting of wind speed for multiple stations based on attribute-augmented spatiotemporal graph convolutional network and tree-structured parzen estimator

The global increase in energy demand and environmental concerns have made the development of wind energy increasingly important. Accurate wind speed prediction is crucial for maximizing the benefits of wind energy. In order to further improve the accuracy of multi-site wind speed prediction, this study adopts an evolutionary algorithm-based deep learning model that fully considers the spatiotemporal relationships among multiple station's wind speed data. Firstly, the mutual information (MI) method is used to select variables with stronger correlations to wind speed as auxiliary input factors. Then, an improved version of Attribute-Augmented Spatiotemporal Graph Convolutional Network (IASTGCN) is employed to process data from multiple stations, taking into account both temporal and spatial factors. Additionally, an MI-based wind speed data relationship matrix between multiple stations is calculated to replace the original distance relationship matrix in the model, enabling the model to better capture and utilize the relationships between stations. Next, the Tree-structured Parzen Estimator (TPE) is used to optimize the hyperparameters of the model. This ultimately achieves multi-site multi-step wind speed prediction. Experimental results demonstrate that the proposed model outperforms baseline models and models that only consider either temporal or spatial factors in various scenarios, exhibiting better predictive performance.

Development of a wind turbine with two multidirectional wind wheels

The object of research is a wind generator with counter-rotating blades. A special feature of this design is the presence of two wind wheels that rotate in opposite directions. Wind wheels are on the same axis, between them there is a certain distance, which is determined based on research data. The problem of modern wind power is the low range of operating wind speeds, weak generation at low wind speeds. The upper speed limit is 25 m/s, exceeding which leads to breakdowns of various units of the wind station, especially this affects the integrity of the blades, rupture of the wind wheel, cracking of the metal parts of the bearings and their fasteners. The wind turbine presented in the article allows to achieve an increase in the generation of electric energy by 50–70 %. This is achieved by increasing the relative rotational speed of the rotor relative to the stator. Therefore, even at low speeds, the rotor speed relative to the stator increases, which leads to an increase in power generation. The design of the device includes: two wind wheels, one transmits its rotation to the stator, the second to the rotor axis, a metal base, a current collector mechanism. For conducting the research, an experimental model and a semi-industrial installation were used. Results studies have confirmed the theoretical increase in the generation of electrical energy by this design. The peculiarity of the obtained results is connected with the determination of the distance between two wind wheels, the optimal distance between them corresponds to the maximum energy generation. A distinctive feature of the results obtained can be considered an increase in the number of blades on the second wind wheel

Wind Power Potential of Kaniga Region, Rwanda

The purpose of this study was to investigate the potential of wind power in Kaniga, Rwanda. Wind speed and wind direction are the most important characteristics for assessing wind energy potential of a location using suitable probability density functions. In this study, a hybrid-Weibull probability density function was used to analyze data from Kaniga station. Kaniga is located in Gicumbi District, in the Northern Province of Rwanda. Onsite hourly wind speed and wind direction data for the year 2022 were analyzed using Computer programmes. The research was conducted to reveal the wind power potential of Kaniga and other major wind stations in the country as an approach to address energy shortage problem in the country. It was found that the average annual wind speed for Kaniga, Nyarwambu, and Byumba were 4.5m/s, 3.7m/s and 4.1m/s respectively, while corresponding dominant wind directions for the stations were 3200, 1800 and 1500 respectively. The average annual wind power density of Kaniga was found to be 1,285.4W/m2, while the annual power densities for Nyarwambu and Byumba were determined as 689.4W/m2 and 805.4W/m2 respectively. The other stations in Rwanda have the average annual power densities ranging from 6.99 W/m2 to 138.2W/m2. From these findings, it is clear that Kaniga has the greatest wind power potential among all other studied wind stations in the country.

Monthly wind distribution prediction based on nonparametric estimation and modified differential evolution optimization algorithm

To copy with global warming and increasing energy demand, wind power has been rapidly developed around the world in recent years. It is important to analyse the characteristics of wind speed distribution to improve the development and utilization of wind energy. Many studies focus on improving the estimation accuracy of wind speed distribution, but there are few studies on variation characteristics and predictability of it. Here, we present a novel horizontal-vertical-integration framework to predict wind speed distribution. To address the predictability problem of nonparametric estimation, we proposed a data sampling and mapping method. To indirectly optimize the learning rate of the hybrid neural network, an improved differential evolution optimization algorithm was designed. The effectiveness of the proposed methods was verified by using data of 9 wind stations and 6 comparison models. The results show that the absolute error of the proposed prediction framework is less than 0.0059, which is better than other comparison models.

Improved Prediction of Local Significant Wave Height by Considering the Memory of Past Winds

AbstractWave and water depth were measured with an instrumented tripod in the Yellow River Delta from 9 December 2014 to 29 April 2015. Concurrent wind data were also collected from a nearby wind station. A high‐precision model for predicting local significant wave height (Hs) with wind speed (vw) is constructed using an improved data‐driven approach. The proposed model realized high accuracy as it solves the problem that the Hs falls too fast during the wind‐decreasing periods. It was tackled by considering the remaining influence of historical vw on the present Hs via incorporating a memory curve of the past wind effect. This innovative approach significantly improves the prediction (R2 from 0.60 to 0.83). The winds in the past 24 hr still left an influence on the waves at the observation site although the influence decreases with time. Physically, it is an implicit but simpler consideration of wind fetch/duration. Further data modeling experiments indicated that the decisive factor for the Hs at the site is the wind speed. Wind directions slightly improve the prediction, indicating that waves are slightly affected by the underwater seabed slope along different wind directions, and northwest winds cause the strongest waves at the site. Adding atmospheric pressure or water depth even reduces the accuracy, which indicated that storm surges and wave deformations under different tide levels have a weak impact on Hs. The proposed local wave model can be easily constructed with available wind and wave data, making it expandable to other regions dominated by wind waves.

Management of green investment of countries in terms of their technological development

In the conditions of technological development and informatization of social relations, the formation of effective organizational support for the processes of investing in green technologies is becoming more and more actualized. The object of the research is the management processes of green investment of the countries of the world by various stakeholders in the conditions of increasing movement and development of productive forces towards Industries 4.0 and 5.0. Among the problems that need to be solved, the following should be noted: – low level of management decision-making and implementation of green investment in countries with transition economies and developing countries; – high level of divergence in technological development among the countries of the world in the system of technological structures. Effective organizational and economic support for green investment at the level of the state, investor, manufacturer, and client should be included among the tools for solving these problems. A comparative analysis of green investment of the countries of the world was carried out. The factors of positive and negative impact on the green investment management process in countries with different levels of economy and technological development were formed. Modern trends in the development of green investment were investigated. The effects of the implementation of green technologies by companies of the world were noted. The benchmarking approach has been improved, with regard to the selection of constituent countries-leaders, which can be used in the management of green investments. The criterion indicators of the SWOT analysis of the green investment management system were formed on the example of the innovative project of the university. The necessary conclusions have been drawn based on the research results. The obtained results are explained by the possibility of their application in the system of implementation of projects and programs regarding investment in green technologies (solar stations, wind stations, sewage treatment system, landscaping and greening of territories, geothermal energy). Practical implementation is possible in various countries of the world under the conditions of sustainable technological development from Industry 3.0 to Industry 5.0.

Renewable energy sources for backup energy supply of small wastewater plants and individual households

These researches concern the use of renewable sources of electricity during emergency shutdowns for uninterrupted operation of water management systems. The ability to purify drinking water during a power outage requires a backup power source. A mobile power plant, which is usually used as a backup power source during blackouts and emergencies, is known to cause environmental pollution and greenhouse gas emissions. The use of wind energy for these purposes requires the location of wind stations next to small treatment plants and households in order to quickly switch to autonomous mode, which does not always meet the territorial requirements and wind resources of the region. Open areas for the location of solar panels make the use of this type of renewable sources as a reserve in the conditions of military operations in Ukraine not quite suitable. On the basis of foreign experience in the use of small hydroelectric power plants (MHPs) and with different layouts of their location when working at sewage treatment plants, four possible options for providing electricity to sewage treatment plants and households during a blackout are proposed for the conditions of Ukraine. The option of the location of the MHP, which uses the energy of water transported through pipelines and which has successfully proven itself at operating treatment plants, is considered. Variants of using the pressure of an artesian well are proposed: MHP located in a vertical well that supplies artesian water under pressure; when an active artesian well supplies water under natural pressure at a constant flow rate to an above-ground (or underground) tank that simultaneously supplies normal household needs and a connected turbine-powered alternator. In addition, an option for the location of the MHPS was considered for the treatment and further use of rainwater sewage, including for the use of rainwater collected from "green roofs" in a tank-reservoir. The peculiarities of using this scheme are that it is easy to combine it with the case of using energy obtained from artesian wells. The proposed technological solutions allow for significant energy savings, including in places where high-quality water supply from artesian wells is possible, depending on the hydrogeological conditions of the area.

Automatic testbed with a visual motion tracking system for airborne wind energy applications

AbstractThe architecture and a flight test campaign of a small‐scale testbed aimed at aerodynamic and dynamic characterization of airborne wind energy systems are presented. The testbed involves a two‐line rigid‐framed delta kite and an automatic ground station for the lateral control of the kite and reel‐in/reel‐out of the two tethers. The environment, and the states of the kite, the tethers and the actuators are measured by a set of on‐ground and onboard sensors that include, among others, an inertial measurement unit, GNSS receivers, load cells, actuator encoders, a wind station, and a visual motion tracking (VMT) system based on three cameras and an artificial neural network (YOLOv2). The results of a 5‐min flight, including the take‐off, cross‐wind flight, and landing, were used to analyze the capabilities of the testbed. It was shown that the time derivative of the kite course angle exhibits a linear correlation with both the delayed steering input and the delayed differential tether tension, being the dispersion lower for the latter. The intrinsic and extrinsic calibrations proposed for the VMT system led to a good agreement between the estimation of the kite position and course angle provided by the VMT system and the onboard computer. Moreover, although the YOLOv2 algorithm failed in the detection of the kite within around 5% of the images, the simultaneous non‐detection from the three cameras was below 0.1% during the full flight. Such a reliability suggests that a VMT system can be used as a redundant or backup sensor for the GNSS.

Investigation of the impact of integrating renewable sources with variable power generation, taking into account restrictions on the voltage stability limit

This article is devoted to the development of a methodology for estimating the amount of balancing power in a system with a large share of renewable energy sources (RES). For this purpose, an analysis was made of the influence of an increase in the installed capacity of wind power plants (WPPs) on the value of the balance power of the energy system, random characteristics of its variability for various combinations of the number of workers in the WPP system. The possibilities of realizing the distribution of balance power in characteristic critical loaded modes of the system are considered. Using the example of a real power system, the wind speed measurement data obtained for the geographic locations of wind stations on the power systems using the developed programs, simulations of the system modes were carried out for various combinations of these stations. The dependences for the balancing power are constructed, the conditions for reducing this power, as well as the conditions for its limitation on the stability of the voltage in the balancing part of the system, are determined.

Novel methods for wind speeds prediction across multiple locations

This article provides two unique methodologies that may be coupled to study the dependability of multidimensional nonlinear dynamic systems. First, the structural reliability approach is well suited for multidimensional environmental and structural reactions and is either measured or numerically simulated over sufficient time, yielding lengthy ergodic time series. Second, a unique approach to predicting extreme values has technical and environmental implications. In the event of measurable environmental loads, it is also feasible to calculate the probability of system failure, as shown in this research. In addition, traditional probability approaches for time series cannot cope effectively with the system's high dimensionality and cross-correlation across dimensions. It is common knowledge that wind speeds represent a complex, nonlinear, multidimensional, and cross-correlated dynamic environmental system that is always difficult to analyze. Additionally, global warming is a significant element influencing ocean waves throughout time. This section aims to demonstrate the efficacy of the previously mentioned technique by applying a novel method to the Norwegian offshore data set for the greatest daily wind cast speeds in the vicinity of the Landvik wind station. This study aims to evaluate the state-of-the-art approach for extracting essential information about the extreme reaction from observed time histories. The approach provided in this research enables the simple and efficient prediction of failure probability for the whole nonlinear multidimensional dynamic system.

A novel method for prediction of extreme wind speeds across parts of Southern Norway

The paper describes a novel structural reliability method, particularly suitable for multi-dimensional environmental systems, either measured or numerically simulated over a sufficient period, resulting in sufficiently long ergodic time series. This study illustrates the efficiency of the proposed methodology by applying it to predict extreme wind speeds of a group of selected measured sites in Southern Norway in the region near the Landvik wind station. It is well known that wind speeds at different locations are highly non-linear, multi-dimensional and cross-correlated dynamic environmental responses, which can be challenging to analyse accurately. Unlike other environmental reliability methods, the new method does not require restarting the simulation each time the system fails, e.g., in the case of numerical simulation. In the case of measured environmental system response, an accurate prediction of system failure probability is also possible, as illustrated in this study. Moreover, in contrast to classical reliability methods, the proposed method can handle systems with high dimensionality and cross-correlation between the different dimensions.

Modelling wind speed across Zambia: Implications for wind energy

AbstractWind energy is a key option in global dialogues about climate change mitigation. Here, we combined observations from surface wind stations, reanalysis datasets, and state‐of‐the‐art regional climate models from the Coordinated Regional Climate Downscaling Experiment (CORDEX Africa) to study the current and future wind energy potential in Zambia. We found that winds are dominated by southeasterlies and are rarely strong with an average speed of 2.8 m·s−1. When we converted the observed surface wind speed to a turbine hub height of 100 m, we found a ~38% increase in mean wind speed for the period 1981–2000. Further, both simulated and observed wind speed data show statistically significant increments across much of the country. The only areas that divert from this upward trend of wind speeds are the low land terrains of the Eastern Province bordering Malawi. Examining projections of wind power density (WPD), we found that although wind speed is increasing, it is still generally too weak to support large‐scale wind power generation. We found a meagre projected annual average WPD of 46.6 W·m−2. The highest WPDs of ~80 W·m−2 are projected in the northern and central parts of the country while the lowest are to be expected along the Luangwa valley in agreement with wind speed simulations. On average, Zambia is expected to experience minor WPD increments of 0.004 W·m−2 per year from 2031 to 2050. We conclude that small‐scale wind turbines that accommodate cut‐in wind speeds of 3.8 m·s−1 are the most suitable for power generation in Zambia. Further, given the limitations of small wind turbines, they are best suited for rural and suburban areas of the country where obstructions are few, thus making them ideal for complementing the government of the Republic of Zambia's rural electrification efforts.

Real-time Wind Predictions for Safe Drone Flights in Toronto

Tall buildings in urban environments can negatively affect drone navigation, by creating turbulence and local regions of high-velocity winds. This jeopardizes the safety of the public in addition to the security of any items being transported by the drone such as medical equipment, packages, or expensive sensors. This study focuses on studying the effect of buildings located in the downtown area of the City of Toronto on the wind field. The City of Toronto was chosen due to the recent structures' development of and the increased usage of drones by hospitals and other businesses. The city geometry was divided into 20 zones and Computational Fluid Dynamics was utilized to study the effects of the local building geometry on the wind field. Wind velocities were extracted from the CFD data and coupled with historical meteorological data so that they could be compared to local weather station measurements. The coupled velocities and wind bearings from CFD were found to be within 12% and 8% RMSE respectively of the recorded data at the wind station for 85% of the sampled data. Wind velocities were then extracted at regular elevation intervals within the flight environment and coupled with meteorological weather data to generate historical, real-time, forecast, and statistical visualization of local wind fields. This study proposes a technique for obtaining real-time, historical, forecast, and statistical wind speeds and wind bearings in the drone flight environment or along a specified path. Visualization of flow fields is also presented to improve drone flight safety and efficiency.

How to Improve an Offshore Wind Station

The ocean is approximately 71% of the Earth’s surface and has a lot of resources available. Nowadays, human beings are looking for renewable ways to obtain energy. Offshore power can be obtained in several different ways. Offshore wind power is the most used renewable offshore energy. Since 2017, offshore wind power has a competitive price in comparison with conventional sources. In the 2010s, offshore wind power grew at over 30% per year. Although it has remained less than one percent of the overall world electricity generation, offshore wind power becomes quite relevant on the northern European countries from 2020. However, there are other ways to obtain energy offshore such as using tides and the sun. These types of farms are expensive and difficult to install and, therefore, we propose a combination of several renewable energies in one farm. The main ambition of this work is to try to reduce the installation and maintenance costs of the two types of offshore renewable energies by creating a structure capable of supporting the two types of turbines. To accomplish it, a theoretical study will be made, a brief state-of-the-art will be presented, the chosen items and the environment chosen for installation will be referred to, a prototype will be simulated using a multiphysics software and, finally, the results and conclusions will be presented, based on a Portuguese case study. How piezoelectric materials can enter offshore farms to increase efficiency is also referred to. The project proved to be possible of producing approximately 12.5 GWh of energy annually, more or less enough to supply 10 thousand homes. However, the installation of the piezoelectric materials did not prove to be viable as it is an expensive technology and does not produce a large amount of energy.

Exploring Wind Speed for Energy Considerations in Eastern Jerusalem-Palestine Using Machine-Learning Algorithms

Wind energy is one of the fastest growing sources of energy worldwide. This is clear from the high volume of wind power applications that have been increased in recent years. However, the uncertain nature of wind speed induces several challenges towards the development of efficient applications that require a deep analysis of wind speed data and an accurate wind energy potential at a site. Therefore, wind speed forecasting plays a crucial rule in reducing this uncertainty and improving application efficiency. In this paper, we experimented with several forecasting models coming from both machine-learning and deep-learning paradigms to predict wind speed in a metrological wind station located in East Jerusalem, Palestine. The wind speed data were obtained, modelled, and forecasted using six machine-learning techniques, namely Multiple Linear Regression (MLR), lasso regression, ridge regression, Support Vector Regression (SVR), random forest, and deep Artificial Neural Network (ANN). Five variables were considered to develop the wind speed prediction models: timestamp, hourly wind speed, pressure, temperature, and direction. The performance of the models was evaluated using four statistical error measures: Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and coefficient of determination (R2). The experimental results demonstrated that the random forest followed by the LSMT-RNN outperformed the other techniques in terms of wind speed prediction accuracy for the study site.

Analysis of Wind Speed Characteristics Using Probability Distribution in Johor

Renewable energy and energy efficiency are the key factors to ensure a safe, reliable, affordable as well as sustainable energy system for a better future. One of the most congruous, environment-friendly, and renewable energy sources is wind energy. However, it is consequential to examine the suitable probability distribution function to study the wind speed characteristics before the element can be harnessed as a source of energy. In this study, five probability distributions, Gamma, Generalized Extreme Value (GEV), Lognormal, Rayleigh and Weibull distribution were selected to model the wind speed data from four wind stations in Johor in a ten-year period. In addition, the method of maximum likelihood estimation (MLE) was applied to obtain the parameter estimation for each selected distribution function, followed by the plotting the graphical representation of probability distribution function (PDF) and cumulative distribution function (CDF) for the theoretical distributions against the provided wind speed data. To determine the best-fitted model of the probability distribution, the Kolmogorov Smirnov (KS) test and Anderson Darling (AD) test were employed to assess the goodness-of-fit for each model distribution. Based on the plotted graph and calculated goodness-of-fit results, GEV distribution was found to be the best-fitted model for the wind speed dataset in Senai, Mersing, and Batu Pahat wind station, while Gamma distribution established the optimum model for the actual wind speed dataset in Kluang station.

Turbine recommender: The selection of wind turbine type using one of a machine learning technique

This study aims to utilize the machine learning technique to build a model to recommend the suitable wind turbine type based on some variables, such as air speed and air density, as well as visualize the location of the recommended wind turbine selection on a 3D map. Particularly, we applied the K-nearest neighbor model (KNN) to determine the amount of energy produced by a single wind turbine. We applied it on 10 separate wind farms in Saudi Arabia. The results indicate that the model performs very well in predicting the best wind turbine type with the mean accuracy of 88%, where ten wind stations resulted from the optimized model with the suggested turbine type in each station. Adding more wind attributes and other factors may assist in increasing the model mean accuracy. The project’s findings will assist decision-makers in Saudi Arabia to make informed decisions as to what kind of wind turbine is suitable for a specific location. In the long run, this will help to make wind energy-a sustainable source of energy-one of the main goals of the 2030 vision, specifically under National Industrial Development and Logistics Program.