Wind Speed Model Research Articles

The Mixture of Probability Distribution Functions for Wind and Photovoltaic Power Systems Using a Metaheuristic Method

The rising use of renewable energy sources, particularly those that are weather-dependent like wind and solar energy, has increased the uncertainty of supply in these power systems. In order to obtain considerably more accurate results in the analysis of power systems, such as in the planning and operation, it is necessary to tackle the stochastic nature of these sources. Operators require adequate techniques and procedures to mitigate the negative consequences of the stochastic behavior of renewable energy generators. Thus, this paper presents a modification of the original probability distribution functions (PDFs) where the original PDFs are insufficient for wind speed and solar irradiance modeling because they have a significant error between the real data frequency distribution and the estimated distribution curve. This modification is using a mixture of probability distributions, which can improve the fitting of data and reduce this error. The main aim of this paper is to model wind speed and solar irradiance behaviors using a two-component and a three-component mixture of PDFs generated from the integration of the original Weibull, Lognormal, Gamma, and Inverse-Gaussian PDFs. Three statistical errors are used to test the efficiency of the proposed original and mixture PDFs, which are the root mean square error (RMSE), the coefficient of correlation (R2), and the Chi-square error (X2). The results show that the mixture of PDFs gives better fitting criteria for wind speed and solar irradiance frequency distributions than the original PDFs. The parameters of the original and the mixture of PDFs are calculated using the innovative metaheuristic Mayfly algorithm (MA). The three-component mixture of PDFs lowered the RMSE by about 73% and was 17% more than the best original and the two-component mixture distributions.

Typhoon-Resistant Performance Assessment of Coastal Rural Residential Keel Brick Walls Reinforced with High Ductility Concrete

The keel brick wall belongs to the rural residential exterior wall unique to the southeast coastal region of China. The wooden keel inside the wall and the external tie members can improve the stability of the whole wall. However, due to the age of construction, the wooden keel inside the existing residential keel brick walls has basically rotted and lost the tie with the external members, which seriously reduces the stability of the whole wall. Moreover, the collapse of keel brick walls caused by typhoons has become more and more frequent in recent years, seriously endangering the lives and properties of residents. Therefore, it is necessary to carry out reinforcement and repair for the existing residential buildings with keel brick walls. In this paper, the keel brick walls of rural houses in Ningbo area were taken as the research object, a framework for evaluating the performance of keel brick walls against typhoons was established, tests on the lateral force resistance of keel brick walls reinforced with high ductility concrete and mortar were carried out. With the help of typhoon full-track simulation and wind field simulation technology, the 600-year near-surface typhoon wind speeds affecting Ningbo area were obtained. A joint probability distribution model of multi-directional extreme wind speed was constructed using t-Copula function to determine the mixed wind climate of Ningbo area. The extreme wind loads that keel brick walls can withstand under four types of reinforcement were finally given. The results show that the high ductility concrete reinforcement has great advantages over mortar reinforcement and avoids brittle damage of the masonry structure. The keel brick walls reinforced with single-sided mortar can withstand wind loads of a 3-year return period, the keel brick walls reinforced with single-sided high ductile concrete can withstand extreme wind loads of a 110-year return period, while the keel brick walls reinforced with double-sided mortar as well as double-sided high ductile concrete can withstand extreme wind loads of well over a 110-year return period.

Fixed-time observer-based control of DFIG-based wind energy conversion systems for maximum power extraction

To extract maximum power from the available wind energy, it is necessary that the doubly-fed induction generator (DFIG) based wind energy conversion system (WECS) outputs that maximum power at each instant of time for different wind speeds. This can be achieved by controlling the rotor voltage of the DFIG. To make it possible, a novel maximum power point tracking (MPPT) controller in combination with state and disturbance observers and fixed-time stability notion is proposed in this paper. The sliding mode control (SMC) method is used to design the observer-based controller and ensure the robustness. The sliding mode state observer is designed while only the measurement of the stator reactive power and rotor speed is required. The modelling uncertainties and external disturbances are estimated by the sliding mode disturbance observer without information about their upper bounds in advance. Then, the combinatorial effect of uncertainties and disturbances are fully compensated by the designed controller. The fixed-time convergence issue is addressed where the bound on the settling time is user-defined using design parameters regardless of initial conditions. The elimination of chattering issue is considered in the design of the SMC laws. The stability analysis of the closed-loop system is obtained via the Lyapunov stability theory. The validity and robustness of the proposed method is tested in Simulink/MATLAB for a DFIG under two different scenarios of wind speed modelling. The proposed fixed-time method is also compared with a classical finite-time method. Comparing the results demonstrate that the proposed method outperforms the other method.

Computing reliability indices of a wind power system via Markov chain modelling of wind speed

Statistical modelling of wind speed is of great importance in the evaluation of wind farm performance and power production. Various models have been proposed in the literature depending on the corresponding time scale. For hourly observed wind speed data, the dependence among successive wind speed values is inevitable. Such a dependence has been well modelled by Markov chains. In this paper, the use of Markov chains for modelling wind speed data is discussed in the context of the previously proposed likelihood ratio test. The main steps for Markov chain based modelling methodology of wind speed are presented and the limiting distribution of the Markov chain is utilized to compute wind speed probabilities. The computational formulas for reliability indices of a wind farm consisting of a specified number of wind turbines are presented through the limiting distribution of a Markov chain. A case study that is based on real data set is also presented.

Impacts of accelerating deployment of offshore windfarms on near-surface climate

The European Union has set the ambitious goal of becoming climate neutral by 2050, which has stimulated renewable energy production and accelerated the deployment of offshore wind energy in the North Sea. Here, a high-resolution regional climate model was used to investigate the impact on the sea surface climate of large-scale offshore wind farms that are proposed for the North Sea. The results show a significant reduction in the air-sea heat fluxes and a local, annual mean net cooling of the lower atmosphere in the wind farm areas down to more than 2.0 Wm−2, due to a decrease in 10 m wind speed and turbulent kinetic energy and an increase in low-level clouds. Mean surface winds decreased by approximately 1 ms−1 downstream of wind farms. Furthermore, an increase of approximately 5% in mean precipitation was found over the wind farm areas. At a seasonal timescale, these differences are higher during winter and autumn than in other seasons. Although the offshore wind farms reduce the heat transport from the ocean to the atmosphere in the region of large wind farms, the atmospheric layers below the hub height show an increase in temperature, which is on the order of up to 10% of the climate change signal at the end of the century, but it is much smaller than the interannual climate variability. In contrast, wind speed changes are larger than projected mean wind speed changes due to climate change. Our results suggest that the impacts of large clustered offshore wind farms should be considered in climate change impact studies. Moreover, the identified offshore windfarm impacts on the sea surface climate and the introduced spatial pattern in atmospheric conditions, in particular the modeled wind speed changes, suggest potential impacts on local ocean dynamics and the structure of the marine ecosystem. This should be considered in future scenarios for the North Sea marine environment and taken into account as a structuring influence in the offshore environment.

A coupled system of stochastic differential equations for probabilistic wind speed modelling

The Journal of the National Science Foundation of Sri Lanka publishes the results of research in all aspects of Science and Technology. The journal also has a website at http://www.nsf.gov.lk/. 2021 Impact Factor: 0.682The JNSF provides immediate open access to its content on the principle that making research freely available to the public supports a greater global exchange of knowledge.

Synchronous three-dimensional detection method for multiple parameters of wind fields based on vector principle.

In the area of air-assisted spray, conventional detection of speed and direction of the wind fields for spray are separately conducted, and multiple kinds of sensors have to be laid on each coordinate axis during multidimensional detection. It limits the optimization of operation effect of sprayers based on wind-field distribution characteristics. This paper proposes a novel detection method to achieve synchronous measurement of wind speed and direction in three dimensions. Wind flow was considered as vectors and the sensing structure with a regular triangular pyramid shape supported by cantilever pieces was established. Strain gauges were utilized to detect the deformation in each direction by the wind thrust onto a ball before and after wind flow. Moreover, the calculation models of wind speed and direction were developed respectively based on the relationship of 'strains-force-wind pressure-wind velocity' and the principle of space operation of vectors, so multiple parameters of wind fields could be obtained simultaneously. Calibration was conducted based on a wind tunnel and the Testo 405i anemometers. The results showed that: the minimum relative error of wind-speed values was about 0.06%, while the maximum was about 10%. The average relative error of all the directions was less than 5%. Furthermore, the measurement of the wind among artificial tree canopies demonstrated that the proposed method could effectively measure both speed value and direction of the wind among canopies, and it also helped to find the wind distribution characteristics of the fan, SFG4-2R. The results highlighted both the reliability and the practical meaning of the proposed method, which could be a technical solution for measuring and evaluating wind-field characteristics of sprayers.

Autocorrelation of wind speed: A sliding window approach

This article aims to analyze the daily fluctuations of the time series of wind speed in some municipalities in the State of Bahia, Brazil from January 2009 to December 2018 with the approach of sliding windows. The analysis will be performed, mainly, with the method known in the literature as Detrended Fluctuation Analysis (DFA) able to identify and measure autocorrelation in non-stationary time series on different time scales (Peng et al., 1994). To meet the proposed objective, we chose five cities of Bahia, by methodological option: Barreiras, Feira de Santana, Guanambi, Salvador and Vitória da Conquista. The results indicated a persistent and statistically significant behavior at the level of 5% for all the studied cities and period. The description with sliding windows (w = 365) found a predominance of relative variation above 15%, kurtosis and positive asymmetry. Our findings can be used as one more proposal to model wind speed data and contribute to research related to climatological data.

Field measurement analysis of wind parameters and nonstationary characteristics in mountainous terrain: Focusing on cooling windstorms

Cold fronts passing through the mountain areas may produce continuous windstorm and cooling processes, potentially impacting the mountain areas' infrastructure. Cooling windstorms belong to the dynamically-driven wind in the mountain wind system. To accurately estimate the wind field parameters of these storms and provide a basis for structural wind resistance design, cooling windstorms should be extracted and analyzed separately from the wind field observation data. Based on the long-term field measurement in complex mountain terrain, a framework for identifying cooling windstorms is established according to temperature and wind speed features. The mean and fluctuating wind parameters of these intense winds are then obtained. Strong nonstationary is detected for cooling windstorms. This paper focuses on the time-varying regularity of fluctuating parameters during intense wind with cooling based on the evolutionary power spectral density. From the perspective of turbulent energy, the basis for selecting the optimal wavelet decomposition levels is put forward when extracting the time-varying mean wind through discrete wavelet transform in the nonstationary wind speed model. This investigation can provide a reference for studying the nonstationary wind field.

Wind Power Generation Forecast Based on Multi-Step Informer Network

Accurate forecast results of medium and long-term wind power quantity can provide an important basis for power distribution plans, energy storage allocation plans and medium and long-term power generation plans after wind power integration. However, there are still some problems such as low forecast accuracy and a low degree of integration for wind power physical processes. In this study, the Multi-step Informer network is proposed to add meteorological parameters to wind power generation forecast and make network interpretable. The Multi-step Informer network uses Informer to obtain the initial training model according to the historical data of wind power generation, introduces the Informer model of wind speed and air pressure training involved in the dynamic pressure model, and compares the historical data of wind power generation to obtain model modification, so as to further improve the forecast accuracy of Multi-step Informer network. The backpropagation process of the pre-trained Informer should be truncated to avoid being influenced by the pre-trained Informer during training of the Multi-step Informer network, which also guarantees the interpretability of the running results of the network. The Multi-step Informer network has the advantage of error correction of wind power generation, which improves the forecast accuracy. From the calculation results of the root mean square error, Multi-step Informer network improves forecast accuracy by 29% compared to Informer network.

Wind Turbine Energy Cost Optimisation Using Various Power Models

In modern times, the worldwide wind turbine installations have developed swiftly resulting in the decrease of green gas emissions. Though wind is a free gift of nature, it is expensive to harness this energy for useful applications like electricity generation. The cost of installation of the wind turbine at a particular station does not depend only on the wind resource, but also on the structure of the turbine and the energy conversion technology. The wind turbine Cost of Energy (CoE) is used to estimate the payback time for the return on the investment made by the wind farm owners for the turbine. Meticulous research is required to optimize the turbine CoE which will make wind a very competent source of energy. In this article, in order to minimize the wind turbine CoE, the wind speed is modelled using three different distributions namely, Dagum, Gamma and Weibull and the evaluation of the turbine Annual Energy Production (AEP) is carried out. Mathematical functions such as linear, quadratic and cubic have been used to model the wind power. For the cost analysis of the turbine, the price model which was established by United States, National Renewable Energy Laboratory (NREL) is employed. The comparative study of the proposed methodology have been done for six different stations. The turbine CoE model is an element of two factors, the rated power Pr of a turbine and the rated wind speed Vr of a turbine. Based on the results obtained, a broad recommendation to reduce the turbine CoE is presented. This study enables us to figure out the minimum turbine CoE among the three discussed mathematical distributions, the finest distribution for wind speed modelling and the optimum mathematical function for wind power modelling. The suitable size of the wind turbine also can be found by optimizing the rotor radius R of the turbine for each data.

Combined wind profile characteristics based on wind parameters joint probability model in a mountainous gorge

Long-span bridges in mountainous areas are greatly disturbed by wind, and the wind field at the mountain gorge bridge site is extremely complex. Therefore, it is of great engineering significance to accurately evaluate the wind field characteristics of this kind of terrain. In this paper, to enhance understanding of this kind of wind field, the wind field in a mountainous gorge is measured for a long time using wind radar, and the mean wind parameters are statistically analyzed. The results show that the mean wind parameters vary greatly under different wind directions, and the wind speed profile does not meet the power-law model. Therefore, a mixed model suitable for the wind speed profile in a mountainous gorge is proposed. Additionally, GEV distribution and Logistic distribution are found to be suitable for describing the distribution characteristics of wind speed and angle of attack, respectively. In addition, considering the correlation between wind parameters, this paper also constructs the joint probability model of wind speed and angle of attack at different heights by the Copula function. Thus, a combined wind parameters profile model is developed under different exceedance probabilities based on the inverse first-order reliability method (IFORM). This study can provide a reference for the construction of the joint probability model of wind parameters.

Wind speed prediction over Malaysia using various machine learning models: potential renewable energy source

Modeling wind speed has a significant impact on wind energy systems and has attracted attention from numerous researchers. The prediction of wind speed is considered a challenging task because of its natural nonlinear and random characteristics. Therefore, machine learning models have gained popularity in this field. In this paper, three machine learning approaches – Gaussian process regression (GPR), bagged regression trees (BTs) and support vector regression (SVR) – were applied for prediction of the weekly wind speed (maximum, mean, minimum) of the target station using other stations, which were specified as reference stations. Daily wind speed data, gathered via the Malaysian Meteorological Department at 14 measuring stations in Malaysia covering the period between 2000 and 2019, were used. The results showed that the average weekly wind speed had superior performance to the maximum and minimum wind speed prediction. In general, the GPR model could effectively predict the weekly wind speed of the target station using the measured data of other stations. Errors found in this model were within acceptable limits. The findings of this model were compared with the measured data, and only Kota Kinabalu station showed an unacceptable range of prediction. To investigate the prediction performance of the proposed model, two models were used as the comparison models: the BTs model and SVR model. Although the comparison of GPR with the BTs model at Kuching station showed slightly better performance for the BTs model in maximum and minimum wind speed prediction, the prediction outcomes of the other 13 stations showed better performance for the proposed GPR model. Moreover, the proposed model generated smaller prediction errors than the SVR model at all stations.

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.

Joint Model of Wind Speed and Corresponding Direction Based on Wind Rose for Wind Energy Exploitation

Joint Model of Wind Speed and Corresponding Direction Based on Wind Rose for Wind Energy Exploitation

Multi-step ahead wind speed forecasting approach coupling maximal overlap discrete wavelet transform, improved grey wolf optimization algorithm and long short-term memory

Accurate and reliable wind speed forecasting is of great significance to the management and utilization of wind energy. An improved deep learning model for wind speed forecasting, abbreviated as MODWT-RF-IGWO-LSTM, is presented in this paper. Firstly, the maximum overlap discrete wavelet transform (MODWT) is applied to denoise the original wind speed series. Secondly, the random forest (RF) algorithm is used for feature selection. Thirdly, the improved grey wolf optimization algorithm (IGWO) is applied to optimize the parameters of the long short-term memory (LSTM) model. Finally, the denoised wind speed data is entered into the well-trained LSTM model to obtain the final wind speed forecasting result. The performance of the proposed model is assessed by actual wind speed data for three different months of the year. The experimental results show that the proposed deep learning model for wind speed forecasting has good predictive ability. And the proposed model performs better than other benchmark models in this paper.

Reliability Evaluation of Nondispatchable Energy Sources in Generation Planning: A Wind Electrical System Case Study

Summary. Generation planning is an important aspect of the probabilistic production simulation (PPS) process for power systems. The inclusion of nondispatchable energy sources in the conventional system needs efficient algorithms for reliability evaluation. Moreover, it is crucial to represent the availability of nondispatchable energy sources with a suitable probability distribution function (PDF) that can be easily integrated with the conventional system. The present work extends the generation reliability to include nondispatchable energy sources in the reliability evaluation methodology. The wind electrical system (WS) is represented using a simple wind speed model based on the normal distribution function. The multistate wind speed model based on the mean and standard deviation of wind speed at a particular site is integrated with the conventional system for reliability study. The proposed fast Fourier transform (FFT)-based method eliminates extensive calculations encountered in the conventional approach due to cumbersome time-domain convolution. The efficacy of the methodology is validated with the case studies using the IEEE RTS-wind system with loss of load probability (LOLP) and expected energy not served (EENS) reliability indices.

Modeling and Control of Wind Speed in Renewable Energy Power Generation and Wind Power Generation Systems

Wind energy is one of the most used clean energy sources in renewable energy, and its renewable and sustainable nature is one of the reasons why it is used for power generation. In the current environment where all countries in the world are facing energy problems, research on wind power generation systems is also increasing. This article aims to study the problem of modeling and controlling wind speed in the wind power generation system of renewable energy power generation. To this end, this article proposes a modeling method for wind power generation systems, which can be used to study the momentum problems in wind power generation and the mechanical torque of the generator. And at the end of the article, related experiments and analysis are designed to explore and compare its operating cost, speed, and wind wheel speed. The experimental results in this paper show that through effective modeling and control of its wind speed, the economic risks in the actual wind power generation system can be controlled, with a maximum reduction of 24%, and the actual operating cost is also reduced by 8.66%, so wind power has high practical value.

Wind Speed Analysis for Coastal Regions of Pakistan using Extended Generalized Lindley Distribution

The wind energy potential of a specified area can be estimated using wind speed distribution. In this study, the selection of probability density functions is used to model wind speed data recorded at two stations in Pakistan. The suitability of fitted distributions is evaluated using the goodness of fit criterion, power density error, log-likelihood, root mean square error, coefficient of determination, AIC, and BIC. The wind speed data are obtained from two coastal regions of Pakistan at 10m/s average rate for session 2017-2018. Findings indicated that the extended generalized Lindley distribution provide generally the best fit to the wind speed data for both stations. However, it is also observed that power Lindley and extended generalized Lindley distributions have better performance based on power density error criteria in Gwadar and Haripur, respectively.

Lagrangian particle dispersion (HYSPLIT) model analysis of the sea breeze case with extreme mean daily PM10 concentration in Split, Croatia.

The case of a sea breeze where the mean daily PM10 concentration reached the recommended limit value for human health for the period from 2007 to 2009 at the air quality station AMS3 Split-1 in Split, Croatia, is analysed. The Pennsylvania State University/National Center for Atmospheric Research (PSU/NCAR) atmospheric model MM5 and the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model are used to simulate the lower atmospheric flow and dispersion within 100km from the coast. The simulation is made with four point sources in the hinterland of Split: Kaštel-Sućurac, Solin and Klis, where the local cement plants are located, and Vranjic, where an asbestos-cement plant is located. Statistical analysis and rotational statistics showed good agreement of the measurement data with the modelled wind speed and direction at 10m and temperature at 2m height. The backward trajectories show that the pollutants are caught in the sea breeze circulation from all sources and in the early afternoon the plume is placed over parts of Split from all sources. From the peak concentrations during the selected day, it can be concluded that pollution from Kaštel-Sućurac had the greatest impact on the high PM10 concentrations measured at the Split-1 station.