Current Wind Speed Research Articles

Natural sorting of sediments in the wave run-up zone works for microplastics as well

The distribution of plastic pollution in the marine environment is highly variable in time and space, making it difficult to assess pollution levels. This study shows that mixing and natural sorting of material in the wave run-up zone of a sandy beach results in a relatively stable abundance of microplastics in the size range 0.5–2 mm (S-MPs). Based on 175 samples collected over 14 months during 42 monitoring surveys at 6 stations along the shore of the Vistula Spit (Baltic Sea), the mean abundance of S-MPs was found to be 64 ± 36 items/kg DW (98.6 % fibers), with a coefficient of variation of only 56 % over more than one year. Statistical tests confirmed its independence from current wind speed, significant wave height, mean sediment grain size, sediment sorting, percentage of certain sand fractions, month, season, or location along the shore. It can therefore be used as a suitable indicator for long-term monitoring of increasing plastic pollution in the marine environment.

Application of DPC to improve the integration of DFIG into wind energy conversion systems using FOPI controller

While the direct power control (DPC) approach has proven effective in improving the efficiency of wind energy conversion systems (WECS) using doubly fed induction generators (DFIG), its applicability is currently confined to a single usage and has not been extended to meet numerous applications. This work aimed to modify the implementation of DPC in WECS-DFIG for several objectives. This is accomplished by updating the reference power of the conventional DPC method into an adapted one to achieve two goals independently. The first objective is to track the maximum power during wind speed variations. This tracking is performed by updating the reference power to match the maximum available power at the current wind speed. The second purpose is to ensure that the WECS remains connected to the grid and continues to operate smoothly even in the event of faults; supporting fault-ride through (FRT) capability. That is achieved by reducing the reference power during these faults. The discrimination between these two objectives is based on the voltage level at the point of connecting WECS to the grid. The controller provided is an improved fractional order PI controller developed using arithmetic optimization technique (AOA). A comparison between the AOA and cuckoo search is presented. The results demonstrate the efficacy of the suggested configuration and regulator in enhancing the performance of integrating DFIG into the WECS in the presence of wind fluctuations and short circuit faults occurring. It is worth noting that AOA is better than cuckoo search in fine-tuning the settings of the FOPI controller.

A marine accident analysis based on data-driven Bayesian network considering weather conditions and its application to Taiwanese waters

Few marine accident studies contemplate the weather effects on the places where and dates when shipping casualties occur. This study conducts a marine accident analysis based on Bayesian Network (BN) considering the weather characteristics of the accident spots and occurrence dates. The weather element is adopted as a node in the BN that comprises a group of five clusters illustrating the states of current speed, wave height and wind speed. A total of 431 accidents occurring in the western coast of Taiwan during the period from 2014 to 2022 are analyzed. Wave with height greater than slight sea has a certain effect on the accident frequency and severity. Mishaps are likely to suffer severe consequences if encountering the weather conditions featured by strong current, moderate/fresh breeze and slight/moderate sea. In addition, Accident Type is the most influential node of Accident Severity among the parameters, followed by Ship Type, Gross Tonnage (GT), Weather Cluster, Location and Ship Age. Finally, the three most crucial parameters leading to Very Serious Marine Casualty are Accident Type, Ship Type and GT.

The Analysis of Sub-Synchronous Resonance in a Wind Farm for a Doubly-Fed Induction Generator Using Modern Analytical Method

The occurrence of Sub-Synchronous Resonance (SSR) phenomena can be attributed to the interaction that takes place between wind turbine generators and series-compensated transmission lines. The Doubly-Fed Induction Generator (DFIG) is widely recognized as a prevalent generator form employed in wind energy conversion systems. The present paper commences with an extensive exposition on modal analysis techniques employed in a series of compensated wind farms featuring Doubly Fed Induction Generators (DFIGs). The system model encompasses various components, including the aerodynamics of a wind turbine, an induction generator characterized by a sixth-order model, a second-order two-mass shaft system, a series compensated transmission line described by a fourth-order model, controllers for the Rotor-Side Converter (RSC) and the Grid-Side Converter (GSC) represented by an eighth-order model, and a first-order DC-link model. The technique of eigenvalue-based SSR analysis is extensively utilized in various academic and research domains. The eigenvalue technique depends on the initial conditions of state variables to yield an accurate outcome. The non-iterative approach, previously employed for the computation of initial values of the state variables, has exhibited issues with convergence, lack of accuracy, and excessive computational time. The comparative study evaluates the time-domain simulation outcomes under different wind speeds and compensation levels, along side the eigenvalue analysis conducted using both the suggested and non-iterative methods. This comparative analysis is conducted to illustrate the proposed approach efficacy and precision. The results indicate that the eigenvalue analysis conducted using the proposed technique exhibits more accuracy, as it aligns with the findings of the simulations across all of the investigated instances. The process of validation is executed with the MATLAB program. Within the context of the investigation, it has been found that increasing compensation levels while simultaneously decreasing wind speed leads to system instability. Therefore, modifying the compensation level by the current wind speed is advisable.

Improving the accuracy of wind speed spatial interpolation: A pre-processing algorithm for wind speed dynamic time warping interpolation

Wind power is one of the most vital renewable energy resources in the world. Wind energy production is directly correlated with the quality and quantity of wind speed data. Interpolation techniques can be employed to fill in the gaps in the current wind speed observation data series. However, existing methods for obtaining blank data do not pre-regulate the regional spatial wind speed sequence and instead rely on direct interpolation, which leads to low accuracy in the interpolation. This is not a problem with the model itself, but rather with the wind speed flowing in space and exhibiting sequence misalignment on the timeline. To address this issue, this study proposes a Wind Speed Dynamic Time Warping (WSDTW) algorithm based on Dynamic Time Warping (DTW) to match similar wind speed reduction sequences in terms of time error. We used the shape context descriptor to encode wind speed and introduced wind rose descriptors to represent wind direction initially. The matching cost of DTW was then optimized. Finally, five common interpolation methods were selected to evaluate the method. The research results indicate that interpolation after WSDTW matching and warping can significantly improve the accuracy of wind speed interpolation and reduce the spatial dependence of wind speed. This method demonstrates good stability and generalization, performing exceptionally well in situations where there are gaps in regional wind speed data or missing data.

A wind speed forecasting method based on EMD-MGM with switching QR loss function and novel subsequence superposition

The ultra-short-term forecasting of wind speed is of great significance to the stable power supply of the power system. Current wind speed forecasting methods aim to improve forecasting precision while disregarding model training speed and model deployment complexity. This research proposes a lightweight hybrid model named SLF-EMD-MGM-NS for wind speed forecasting. EMD-MGM is designed as the network’s fundamental structure for reducing the hybrid model’s training time and ensuring the hybrid model has high forecasting precision. The study presents the switching loss function (SLF) mechanism. When the quantile is 0.5, an MSE-based loss function is employed for training all subsequences. When the quantile is 0.5 and 0.95, first use the wind speed fluctuation threshold to select primary subsequence, and then use the Log-Cosh-based loss function for training primary subsequences. The SLF mechanism can increase point prediction precision and interval prediction boundary stability. Moreover, a novel subsequence superposition (NS) mechanism is proposed for getting high confidence level and narrow-width interval prediction results. The NS mechanism superimposes the interval prediction results of the fluctuation subsequence with the point prediction results of the model to generate the final interval prediction results. According to the experimental results, the SLF-EMD-MGM-NS model has a high confidence level, acceptable prediction results, a narrow-width interval prediction result, and a significantly shorter training time than the other hybrid models.

Stochastic modelling of wind speed over northern states in Nigeria

This paper presents a stochastic modeling of wind speed over sixteen (16) Northern states in Nigeria using thirty-seven years (1984-2020) wind speed real time data. A Markov Chain Model was developed for the monthly wind speed state across study locations. In order to obtain the Markov chain transitional probabilities, the wind speed data was categories into various states using the Beaufort wind scale. It was observed that only the first four description of wind speed state A, B, C and D exist in the study locations. Uniform random states were also formed by generating uniform random number. The comparison of monthly simulated and actual wind speed state clearly shows that the model simulated over six months correctly across study locations except Niger. Given a current wind speed state conditions, the stochastic models available in this paper can be adapted to generate future wind speed state condition. The understanding of wind speed state helps in wind turbine design and selection of wind farm sites for wind energy generation.

17 Years of Coastline Changes Observation in the Northern Part of Rupat Island Using Landsat 7 ETM + Time Series Imagery (2000-2017); A Case Study of the Country’s Border Between Indonesia – Malaysia

Rupat Island is the outermost part of Indonesia which borders Malaysia. This island is part of Bengkalis Regency, Riau Province, located at 02° 4.911'N-101° 27,191'E. This study uses primary data from field observations and secondary data in Landsat 7ETM+ Time Series image data and other supporting data. The descriptive method is carried out in the form of geological interpretation in the field, determination of validation points and then analysis using GIS/visual interpretation to obtain shoreline changes that occurred in the northern part of Rupat Island from the year 2000 to 2017. Additional data used were sea current and pattern and wind direction and speed. Validation points are used to obtain detailed calculations related to this coastline change: Tanjung Api area to Teluk Rhu and Tanjung Lapin get the measurement results experienced the most accretion occurred in 2013 was 167.21 m, and the most abrasion in 2016 was 72.77 m. The biggest change occurred in 2013 when the accretion occurred at 375.5 Ha, and the most abrasion occurred in 2014 at 50.63 Ha. Meanwhile, for shoreline changes, the minimum annual coastline length in 2004 was 19.04 km, and the normal coastline length in 2015 was 28.26 km. The maximum length of the coastline occurred in 2013 was 36.53 km.

Универсальная система визуализации геопривязанных данных – данные вектора скорости

In 2016, an article [1] was published in the Scientific Visualization No. 1 journal, which marked the beginning of a series of publications, in one way or another related to the visualization of georeferenced data. In 2023, an article [2] was published in the journal Scientific Visualization, which continued this series. This article describes step by step the process of developing and debugging a software product for visualizing geophysical data with their subsequent layout on a Yandex satellite map. The initial information for building maps was a text file containing three columns of data: two columns are geographic coordinates, the third column is a scalar parameter, such as temperature or salinity. The presented work is a direct continuation of this series. It describes the procedure for visualizing vector geophysical parameters on the Yandex satellite map, for example, such as current velocity or wind speed. The visualization procedure for vector parameters is based on a similar procedure previously developed for scalar parameters. The paper describes in detail the process of developing and debugging, modifying a software product, the ultimate goal of which is to visualize only the geophysical velocity parameter with subsequent display on the Yandex satellite map in accordance with the geographic coordinates that are set as input. The software product is a fully debugged complete module that can work both in standalone mode and in a complex aimed at superimposing the result obtained on the Yandex satellite map.

A novel wind speed forecasting combined model using variational mode decomposition, sparse auto-encoder and optimized fuzzy cognitive mapping network

The nonlinear, random and fluctuating characteristics of wind speed bring great challenges to its accurate forecast, so no model that can adapt to all situations. In order to solve the problem of unbalanced forecast accuracy and stability in the current wind speed forecast model, a novel and advanced wind speed combined forecast model (CFM) is proposed in this study. The CFM adopts a two-phase data processing strategy composed of variable mode decomposition-sparse autoencoder (VMD-SAE) to extract the original wind speed features, high-order fuzzy cognitive mapping (HFCM) neural network modeling and batch gradient descent optimization algorithm to make up for its shortcomings. The two-phase data processing strategy performs smoothing and feature information extraction processing on the original data. The forecast module adopts the SAE-HFCM combination strategy, and utilizes their respective advantages to achieve accurate and stable result output. The results show that this CFM has the best forecast accuracy and generalization performance compared with 7 benchmark models in datasets from three different sites. Compared with the benchmark models, the performance of CFM in point forecasting and interval forecasting, the average improvement percentage of IP_RMSE, IP_MAE and IP_MAPE minimum values are 40.9%, 40.1% and 40.6%, respectively. The performance of interval indicators is also the best, and its application prospects are broad.

Weather image-based short-term dense wind speed forecast with a ConvLSTM-LSTM deep learning model

Short-term wind speed predication is of great significance for scholars (e.g., understanding wind profiles), practitioners (e.g., building energy management), regulators (e.g., urban microclimate regulation), and even the general public. Current wind speed forecasting methods either generate sparse predictions or occur high cost. This paper reports a novel, inexpensive framework to forecast urban local dense wind speed. The central tenet is a convolutional long short-term memory (ConvLSTM) and LSTM combinatorial deep learning model to learn the features of input historical weather image series coupled with spatial-temporal correlations. The model was trained and tested using Hong Kong datasets. The feasibility and effectiveness of the proposed model are verified and compared with parallel models under different criteria, including mean absolute error (MAE), root mean square error (RMSE) and R-squared (R2). The experimental results show that: (1) the proposed ConvLSTM-LSTM deep learning model can effectively forecast wind speed regardless of location; (2) the overall MAE, RMSE, and R2 value of the proposed model are improved by 14.84%, 15.04%, and 7.51%, respectively, compared to the ConvLSTM-full connected (ConvLSTM-FC) model, and by 22.12%, 22.80%, and 12.24%, respectively, compared to the convolutional neural network-LSTM (CNN-LSTM) model; and (3) compared with parallel models, the proposed model has better performance in predicting wind speed series with large amplitude variations and rapid frequency changes.

Session Assessment of Seaplane Operations: The Case of Marine Tourism Development on Gili Iyang Island, Indonesia

The development of marine tourism in Indonesia aims to promote economic growth, enhance the well-being of the citizenry, and conserve the environment. Gili Iyang Island in East Java Province has the second highest oxygen concentration in the world and is a popular health tourism destination. The transportation industry plays a crucial role in facilitating access and connectivity between islands and other regions. Seaplanes are an alternative mode of transportation for reaching tourist destinations on small islands with limited land area that are surrounded by water. This study aims to determine the optimal conditions for reaching the island and the number of effective days for seaplane operations. In assessing seaplane operating factors, water conditions such as current speed (1), wind speed (2), waves height (3), and tides (4) have a significant impact. Therefore, seaplanes cannot effectively operate for an entire year. Using a comparative methodology, the characteristics of the seaplane mode of transportation in the waters of Gili Iyang will be examined to determine the optimal operational time. The results of the study indicate that the 19-seat seaplane can operate 258 days per year and recommend the use of air-sea transportation modes for the growth of tourism in Gili Iyang Island.

Ensemble forecaster based on the combination of time-frequency analysis and machine learning strategies for very short-term wind speed prediction

• A time series-frequency forecaster is proposed for wind speed prediction. • The hyperparameters of different time series-frequency analysis were optimised. • The forecaster combines time series-frequency analysis and machine learning algorithms. • Final forecaster makes more accurate predictions than the benchmark and other models from the literature. Based on the predictions of fossil fuels depletion in the following years, as well as their negative impact due to generated exhaust fumes, eco-friendly generators, and more specifically wind generators, have arisen as a solution for the electric demand challenge. Wind energy consists in extracting energy from wind speed, and because of the uncertain and intermittent behaviour of this meteorological parameter, wind turbines output power cannot be optimally exploited. Although the vast majority of the research in wind speed forecasting field has consisted in the purpose of novel algorithms, these studies have not made a pre-processing step of the data in order to try to extract the maximum information from databases. Therefore, the goal of this paper consists in analysing whether the combination of time-frequency decomposition of wind speed data with different machine learning algorithms can increase the accuracy of current wind speed predictions for 10 min ahead. Obtained error metrics demonstrated that the deviation of developed wind speed forecaster was lower than 0.1% in 62% of the validation database. In addition, the root mean square error of the final forecaster was 0.34 m/s. This means an accuracy increase of 51.5% if the result is compared with benchmark model's results.

Site- and building height-dependent design extreme wind speed vertical profile of tropical cyclone

Tropical cyclone (TC) wind is one of the dominant loadings in the design of building structures. The radial distance-dependent vertical profile and prominent jet structure or super gradient of TC winds make the conventional terrain-dependent design profile inadequate for describing TC wind loadings along the height. Herein, a stochastic simulation-based algorithm is developed to determine the design extreme wind speed vertical profile of TC using a quasi-3D TC wind field model. The wind field model is first described and validated by comparing the vertical wind speed profiles with the upper-level dropsonde data and lower-level tower observation results. The evolution of the wind profile during the passage of historical real storms is reproduced by introducing optimal fitting results for the wind field parameters. Ten-thousand-year synthetic tracks are simulated to obtain the design extreme wind speeds for several coastal cities. The design extreme wind speed composite profile and structure height-dependent outer envelope profile (OEP) are proposed, which are applicable to a certain height of interest and buildings with different heights, respectively. These profiles also depend on terrain exposure, return period, and location of the site of interest. The code-suggested profiles are confirmed to underestimate the TC-induced wind loadings along the height, especially at sites with lower roughness lengths. The power law profile is still applicable, but larger power coefficients should be utilized, and the design extreme wind speed at 10m should be increased with an increase in the structure height. This study provides a forward step towards the rational estimation of TC-induced wind loadings for high-rise structures. • The radial distance-dependent vertical profile and prominent jet structure of tropical cyclone winds are reproduced. • The design extreme wind speed composite profile and structure height-dependent outer envelope profile (OEP) for tropical cyclone are proposed. • The inadequacy of current code-suggested wind speed vertical profiles to tropical cyclone winds is discussed. • The design extreme wind speed vertical profiles of tropical cyclone in several coastal cities are developed.

Effective wind speed estimation study of the wind turbine based on deep learning

Wind speed is the driver of wind turbines, and the precise estimate of that makes it possible to improve the control effects and the efficiency of energy production. This paper proposes a method of effective wind speed estimator that considers the variation in blade radius and reconstructs the mapping of the aerodynamic. First, the proposed method utilizes available data of the wind turbine to train two neural network models based on radial basis functions (RBF). The models estimate the effective radius and reconstruct the aerodynamic mapping surface, respectively. Then, on this basis, train a wind speed estimation model based on the Long Short-Term Memory (LSTM) neural network, which can effectively estimate the current wind speed in real-time and predict the wind speed of the next time step as the reference for the following estimation. In addition, the RBF model and LSTM model can be updated and improved adaptively based on new data to ensure the accuracy of an effective wind speed estimator. Finally, the proposed wind speed estimation method is compared with the existing methods. The experimental results show that the proposed method has strong anti-interference characteristics, and improves the effective wind speed estimation accuracy over 70% on average.

Application of Facebook’s Prophet Model for Forecasting Meteorological Data

The wildfire risk index was calculated based on current meteorological information, for example, temperature, humidity, and wind speed. Thus, meteorological data forecasting could help estimate the probability of fire occurrence or spreading speed to prevent large wildfires. This study predicts meteorological data (e.g., temperature, humidity, and wind speed) using Facebook's Prophet library. We trained the Prophet model using meteorological data between 2016 and 2018 in Goseong, Gangwon-do (where the wildfire occurred in 2019) and predicted meteorological data for the first four months in 2019. We obtained that Facebook's Prophet model was effective in computing speed and predicting the overall trend. However, it could not predict sudden irregular changes satisfactorily. Considering its rapidity, these results could play an important role in future research, especially as a basic research for time-series forecasting.

Estimating Daily Pan Evaporation Data using Adaptive Neuro Fuzzy Inference System: Case Study within Van Local Station-Turkey

The aim of this study is to model the evaporation data, which is one of the important parameters of the hydrological cycle, by using the Adaptive Neuro Fuzzy Inference System (ANFIS). Four different models were designed starting from one input up to four inputs used average daily temperature (ºC), average daily relative humidity (%), average daily current pressure (hPa) and average daily wind speed (m/s) as inputs parameters. Total daily pan evaporation (mm) was selected as output parameter. The normalized daily data of the Van Local Station between 2013 - 2017 was used for training of the model. Data for 2018 were used for testing purposes. Also, two stations in different cities were selected for comparison in order to determine whether the models prepared using data from Van Local Station can be used in other stations. For this purpose, a station from Konya province with climatic characteristics similar to Van province and a station from Kocaeli province with different climatic characteristics were selected. In all models, similar results between Van Local Station and the station selected from Konya were observed, while the results between Van Local Station and the station selected from Kocaeli were observed as relatively low compared to the previous comparison. The fourth model, which was designed using four input parameters, achieved the lowest error values at all stations and Kocaeli station got the best R2 value at this model.

Energy efficiency of wind power plants in various wind condition

The present paper determines the optimum energy operation area of an electric wind power system, under time varying wind speed conditions. The determinations of the optimal area, from the energy point of view, is done based on experimental values from the Romanian black sea cost, Dobrogea area. The optimum operation area is defined by the optimal mechanical velocity, ωOPTIM. For this, the dependence between the wind speed, v, and ωOPTIM are computed based on the current wind speed and the current mechanical velocity ω. Due to the high inertia of the wind turbine (WT), the generators speed is not able to follow the changes of the wind speed. Knowing the optimum value ωOPTIM, the solution is to determine the load at the permanent magnet synchronous generator (PMSG) in order that the mechanical speed should timely reach his optimum.

Wind resource assessment system based on time-scale-dependent roughness

Wind speed intermittence and its forms of pattern change represent sources of significant uncertainties related to wind power. This article introduces a methodological system designed to contribute to an effective assessment of wind resources, by complementing the current wind speed evaluation procedures with the capability of capturing time-scale-dependent pattern properties. The proposed approach to nonstationary wind speed time series produces a comprehensive picture of the wind patterns, in which wind speed variability is quantitatively explored in terms of both time and temporal scale. This approach can thus support responses to the challenges posed by the task of assessing and comparing locations for wind turbines and wind farms, especially in the context of pattern changes in wind resources expected to occur due to climate change.

Environmental and Biological Factors Influencing Dispersal of Neonate Leatherback Turtles (Dermochelys coriacea) From an Endangered Costa Rican Nesting Population

Quantifying early life movements is essential to understanding migratory pathways and habitat use that can impact individuals’ success later in life. To gauge how neonatal movements set the stage for later habitat use, we tracked neonate leatherback turtles (n=94) with acoustic tags from Pacuare, Costa Rica, in 2016 and 2018. We analyzed movements using a first passage time analysis and random walk models, the results of which indicated neonates followed a fixed compass direction as they traveled away from shore and that strong currents in these areas resulted in advection. We combined the tracking data with concurrent environmental variables in a generalized additive mixed model framework. Our results showed the south-east current flow in this area has spatial and temporal structure that was not driven by the tidal current or local wind speed. Accounting for advection by the currents, in-water neonate swimming speed was significantly related to current speed, first passage time, and the year. Neonates had three main response strategies to currents above 0.5 m s-1, with most increasing their swimming speed and the rest maintaining either a constant or decreased swimming speed. Neonates were significantly larger in 2018 than in 2016 but their average swimming speed was not significantly related to body size, indicating that environmental factors were more important contributors to their dispersal. We conclude that abiotic factors, including the strength and direction of the currents, significantly affect the swimming and dispersal strategy of neonate leatherback turtles and these results can help to inform strategies for releases of neonate turtles from hatcheries, future tracking studies, and conservation efforts.