Variable Speed Wind Research Articles

Influence of spatiotemporal and meteorological variation on Norwegian atmospheric pollen seasonality

Climate change is and will continue to alter plant responses to their environment. This is especially prominent concerning the adaptive tracking in reproductive phenology. For wind pollinated plants, this will substantially influence their pollen seasonality, yet there are gaps in knowledge about how environmental variation influences pollen seasonality. To investigate this, we monitored daily atmospheric pollen concentrations of seven pollen types from ecologically, economically and allergenically important plants (alder, hazel, willow, birch, pine, grass and mugwort) in twelve Norwegian locations spanning the entire country for up to 28 years. Six daily meteorological variables (maximum temperature, precipitation, wind speed, relative humidity, solar radiation and atmospheric pressure) was obtained from the MET Nordic dataset with full data cover. The pollen seasonality was then modelled using four spatial, three temporal and the six meteorological variables in a generalized linear model approach with a negative binomial distribution to investigate how each variable group thematically and individually contribute to variation in pollen seasonality. We found that the full models explained the most variation, ranging from R2 = 20.3 % to 59.5 %. The models were also highly accurate, being able to predict 54.5 % to 99.1 % of daily pollen concentrations to within 20.1 pollen grains/m3. Overall, the temporal variables were able to explain more variation than spatial and meteorological variables for most pollen types. Month, altitude and maximum temperature were the most important single variables for each category. The importance of each variable could be traced back to their individual effects of reproductive phenology, plant metabolism, species distributions and pollen release processes. We further emphasise the importance of source maps and atmospheric regional transport models in further model improvements. By understanding the relevance of environmental variation to pollen seasonality we can make better predictions regarding the consequences of climate change on plant populations.

Wind speed estimation and maximum power point tracking using neuro-fuzzy systems for variable-speed wind generator

This paper proposes a novel method using a machine learning-based Adaptive Neuro-Fuzzy Inference System (ANFIS) to optimize Maximum Power Point Tracking (MPPT) in variable-speed Wind Turbines (WT). The ANFIS algorithm, blending artificial neural networks and fuzzy logic, addresses issues with traditional wind speed sensors, such as cost, imprecision, and susceptibility to adverse weather conditions. An initial offline-trained ANFIS is suggested to understand turbine power characteristics, and subsequently estimate varying wind speed, addressing strong nonlinearity due to WT aerodynamics and wind speed fluctuations. A second ANFIS efficiently tracks the maximum power point, overcoming limitations of linear controllers. Implemented in Matlab/Simulink for a 3.5 kW WT, the approach demonstrates effectiveness, precision, and faster response time in wind speed estimation and accurate MPPT compared to alternatives. A notable advantage is its independence from instantaneous wind speed measurement, providing a cost-effective solution for wind energy systems.

Tropical cyclone low-level wind speed, shear, and veer: sensitivity to the boundary layer parametrization in the Weather Research and Forecasting model

Abstract. Mesoscale modeling can be used to analyze key parameters for wind turbine load assessment in a large variety of tropical cyclones. However, the modeled wind structure of tropical cyclones is known to be sensitive to the boundary layer scheme. We analyze modeled wind speed, shear, and wind veer across a wind turbine rotor plane in the eyewall and outer cyclone. We further assess the sensitivity of wind speed, shear, and veer to the boundary layer parametrization. Three model realizations of Typhoon Megi are analyzed over the open ocean using three frequently used boundary layer schemes in the Weather Research and Forecasting (WRF) model. All three typhoon simulations reasonably reproduce the cyclone track and structure. The boundary layer parametrization causes up to 15 % differences in median wind speed at hub height between the simulations. The simulated wind speed variability also depends on the boundary layer scheme. The modeled median wind shear is smaller than or equal to 0.11 used in the current IEC (International Electrotechnical Commission) standard regardless of the boundary layer scheme for the eyewall and outer cyclone region. However, up to 43.6 % of the simulated wind profiles in the eyewall region exceed 0.11. While the surface inflow angle is sensitive to the boundary layer scheme, wind veer in the lowest 400 m of the atmospheric boundary layer is less affected by the boundary layer scheme. Simulated median wind veer reaches values up to 1.7×10-2° m−1 (1.2×10-2° m−1) in the eyewall region (outer cyclone region) and is relatively small compared to moderate-wind-speed regimes. On average, simulated wind speed shear and wind veer are highest in the eyewall region. Yet strong spatial organization of wind shear and veer along the rainbands may increase wind turbine loads due to rapid changes in the wind profile at the turbine location.

Machine Learning Approach to Predict the Dengue Cases Based on Climate Factors

Dengue is a global health issue threatening public health, particularly in developing countries. Effective disease surveillance is critical to anticipate impending outbreaks and implement appropriate control responses. However, delays in dengue case reporting are frequent due to human resource shortfalls. Improved outbreak predictive capacity also requires additional input on vector presence and abundance, which is currently not captured in the surveillance platform. Thus, we developed a prototype AI application, “Dengue Forecasting", that leverages machine learning methods in filing the dengue case report and incorporates dengue vector and climatic parameters. This application simplifies the recording of dengue cases, vector abundance (Angka Bebas Jentik/ABJ), and selected climatic variables (sun exposure, temperature, humidity, wind speed, and precipitation) in Bandung City. The relevant data were extracted from Indonesia’s Ministry of Health and the Meteorological, Climatological, and Geophysical Agency. The entire process, from developing the model to deployment, was conducted under R programming language version 4.2.2 using packages (caret, shiny.io). The linear regression model demonstrated the highest precision (RMSE= 268.32 and MAE= 164.1) in predicting the dengue cases and outbreaks. We also applied this to the application deployment. “Dengue Forecasting” has the potential to assist policymakers at the district level, complementing Dengue EWARS, in anticipating and mitigating dengue outbreaks, especially in Bandung City.

How frequent and which variables of automatic weather station data should be assimilated into WRF-3DVar model? A case study of a squall line event in Beijing

Accurate precipitation forecasting is one of the most challenging problems in mesoscale Numerical Weather Prediction (NWP) models. The utilization of 3-Dimensional Variational (3DVar) Data Assimilation technique based on automatic weather station (AWS) data can significantly enhance the accuracy of precipitation simulations and forecasts using the Weather Research and Forecasting (WRF) model. However, the impacts of different assimilation frequencies and meteorological variables on the accuracy of precipitation forecasts remain unclear. This study comprehensively evaluated the impact of different assimilation frequencies and meteorological variables on precipitation forecasts, as illustrated by a case study of a squall line event on August 2, 2017, in Beijing. Eight experiments were conducted by assimilating different combinations of meteorological variables (air pressure, temperature, relative humidity, wind speed, and wind direction) at various time intervals (1 h, 3 h, and 6 h). The results indicated that the WRF model roughly simulated the evolution of this event but overestimated the precipitation amount, accompanied by a large deviation in precipitation areas. Assimilating detailed AWS data significantly improves the model performance in precipitation forecasts. The experiment assimilating all variables at a 3 h interval yielded the most accurate forecasts, with the maximum threat score (TS) increasing from 0.02 to 0.56. Higher assimilation frequencies do not guarantee a better performance. In practice, a 3 h assimilation frequency emerges as an optimal choice for AWS data assimilation. The assimilation of various variables enhanced precipitation forecasts to varying degrees, with the optimum results achieved when all variables were assimilated. Wind speed and direction were the most significant factors in dynamically enhancing the simulation of precipitation areas. Relative humidity and temperature influenced the precipitation intensity by affecting the evolution of convective precipitation thermodynamically. The findings of this study can contribute to the development of AWS data assimilation strategies for the WRF-3DVar model, thereby enhancing the precipitation forecast accuracy.

Integrating Pix2Pix and computational fluid dynamics for enhanced indoor airflow prediction: A case study with wing-walls

Computational Fluid Dynamics (CFD) is often used for analyzing natural ventilation but is limited in early design stages due to its high computational demands and the need for detailed inputs. To overcome such limitations of CFD simulation, this paper proposes a method using a conditional Generative Adversarial Network (cGAN) based image-to-image translation (Pix2Pix) to predict the indoor airflow condition. Compared to other traditional machine learning models that predict averaged values like indoor air velocity, Pix2Pix can predict contour plot of indoor airflow for the given building floor plan image, which provides more intuitive feedback to designers. The proposed method was tested to understand indoor air movement caused by wing-walls attached to the windows. The model was trained using 1153 pairs of floor plan images, incorporating variations in window widths, wing-wall depth, wing-wall angle, wind speed, and wind direction. It achieved a prediction accuracy of 94 % and produced results in less than a second. Moreover, the model showed relatively better performance with the changes in windows and wing-walls properties rather than wind properties. This high performance indicates that Pix2Pix can serve as an efficient proxy model of conventional CFD simulations, helping designers optimize ventilation in building designs at an early stage without the need for complex inputs.

LVRT Enhancement of DFIG-based WECS using SVPWM-based Inverter Control

Abstract: Across many countries, wind turbine generation systems (WTGS) have been established over the past few decades. In this paper, we augment the low voltage ride-through (LVRT) enrichment facility of driving a DFIG-based wind energy conversion system (WECS) using space vector pulse width modulation (SVPWM)-based inverter control. The proposed technique employs an SVPWM-based control algorithm to regulate the voltage and frequency of the output power during grid faults, thereby enhancing the WECS's ability to remain connected to the grid and provide power. The study focuses on decreasing transient current throughout the instant of fault. Modeling and control approaches were also discussed in this study. The performance of the proposed technique is evaluated using MATLAB/Simulink simulations, and the results demonstrate that the technique effectively improves the LVRT capability of the DFIG-based WECS. Background: Due to the variation in wind speed, the power generated by wind turbines is inconsistent. The power generated and the losses in wind turbines change correspondingly with changes in wind speed. The only type of machine that can generate power at speeds below the fixed speed is the doubly-fed induction generator (DFIG). But DFIG is oversensitive to network faults, which makes the bidirectional converters and DC link capacitor fail due to high inrush current and over-voltage. Methods: The converters connected to DFIG consist of an AC-to-DC converter, a boost converter, and a space vector pulse width modulation (SVPWM)-based DC-AC converter. The performance of the SVPWM controller is analyzed during symmetrical and unsymmetrical fault conditions. Results: The anticipated control provides adequate reactive power support to the network through the time of the fault and improves voltage and current waveform. The reactive power flow is also analyzed, and the effectiveness of the proposed controller is verified using MATLAB and Simulink. Conclusion: SVPWM (Space Vector Pulse Width Modulation)-based inverter control is an effective technique for wind energy conversion systems (WECS). The use of SVPWM can provide accurate and precise control of the AC voltage generated from the DC voltage source, resulting in improved system efficiency and reduced harmonic distortion in the output waveform. : The comparative analysis of THD suggests that SVPWM is a superior technique compared to other inverter control techniques such as sine-triangle pulse width modulation (SPWM) and carrier-based pulse width modulation (CPWM). SVPWM can help to reduce the distortion in the output waveform, leading to improved system efficiency, reduced wear on the system components, and overall better performance of the WECS. Furthermore, SVPWM offers several advantages over other inverter control techniques, including better utilization of DC voltage, improved voltage control, and better utilization of switching devices. These advantages make SVPWM a valuable tool for optimizing the operation of WECS and improving the reliability and performance of renewable energy systems. The value of THD for SVPWM inverter control in WECS is 1.53 under symmetrical fault and 1.34 for unsymmetrical fault, respectively. In summary, the use of SVPWM-based inverter control for WECS is an effective way to improve the efficiency and performance of the system while reducing the distortion in the output waveform and providing adequate reactive power support. The advantages of SVPWM over other inverter control techniques make it a valuable tool for the development and optimization of renewable energy systems.

Synthetic wind speed time series generation by dynamic factor model

The global transition to renewable energy is driven by the fight against climate change. Wind power plays a crucial role in reducing dependence on fossil fuels and greenhouse gas emissions. Therefore, addressing uncertainties in wind speed variations requires innovative solutions. This study proposes a Bayesian-based approach using a Dynamic Factor Model to generate synthetic monthly average wind speed series. The Dynamic Factor Model framework captures temporal and spatial correlations, improving wind resource representation in operational planning models. The model's autoregressive configuration with common factors, prior distributions, and Bayesian inference techniques enhances predictive capabilities. Validation exercises confirm the model's reliability, accurately capturing seasonal oscillations and spatial correlations across eight wind farms. The study highlights the usefulness of the Dynamic Factor Model in evaluating wind projects and optimizing energy generation strategies, effectively mitigating wind uncertainty, and facilitating renewable energy integration in Brazil's power mix.

Investigation of the integration of an oscillating aerofoil-based energy harvester into the building roof

The increasing energy efficiency requirements have led to innovative renewable energy solutions being integrated into buildings. Several works have shown the potential of micro wind turbines on buildings. However, their implementations have been hindered by several challenges. Flow energy harvesting devices such as oscillating aerofoils provide potential, yet a literature gap exists regarding their integration into buildings. This study addresses this gap by investigating oscillating aerofoil wind energy harvesting system performance through numerical modelling. A coupled numerical model was introduced, integrating a novel computational approach using ANSYS Fluent with one degree of freedom in rotational motion. This methodology was introduced to analyze the dynamic behaviour and predict mechanical power output of the oscillating aerofoil wind energy harvesting system, which is a novel exploration in existing literature. Moreover, validation of the computational fluid dynamics model was conducted through using experimental wind tunnel data in the literature, and results demonstrated agreement between numerical and experimental results. To improve the wind energy harvesting system of a single oscillating aerofoil, it was integrated into building roof to take advantage of acceleration effects of building roof shape. The design improvement comprises of a novel wind energy harvesting system design of an oscillating aerofoil integrated into three different building roof including flat, pitched, and curved shapes to enhance energy efficiency. The numerical analysis demonstrated that integrating an oscillating NACA 0012 aerofoil into a curved roof resulted in the highest average power output. Specifically, the integration of the curved roof and the aerofoil yielded 18 watts, while the flat roof generated only 0.6 watts at a wind speed of 3 m/s. Integration into a pitched roof achieved 12 watts at 9 m/s. Comparatively, integrating the curved roof with the aerofoil increased power output by 50% compared to the pitched roof design at 9 m/s wind speed. Thus, variations in wind speed significantly impact performance. In addition, changes in wind direction from 0 to 10 degrees led to reduced efficiency and lower predicted power output. Lastly, the power spectral density for NACA 0012 integrated into the pitched and curved roof buildings revealed peaks at 5.8 Hz in 9 m/s wind speed conditions.

Improvements in Fitting Accuracy of Weibull Distribution for Wind Data by Capturing Monthly and Diurnal Variability in Wind Speeds

Improvements in Fitting Accuracy of Weibull Distribution for Wind Data by Capturing Monthly and Diurnal Variability in Wind Speeds

Estimating Solar Energy within the scope of environmental factors by the Neural Network algorithm

The efficiency of solar energy systems requires a complicated forecasting process due to the variability of sunlight and environmental conditions. Among environmental factors, cloud coverage (% range), temperature (0C), wind speed (Mph), and humidity (%) variables were taken into account in this study. Neural networks (NN), which are machine learning (ML) algorithms with a flexible structure that can define complex relationships and process large amounts of data for solar energy prediction, were used in this study. The NN algorithm showed a high performance, with mean square error (MSE), root mean square error (RMSE), mean absolute error (MAE), and R-squared (R2) values calculated as 0.019, 0.139, 0.053, and 0.977, respectively. This study emphasized that solar energy predictions made with the NN algorithm, considering environmental factors, are an essential tool that helps use solar energy systems more efficiently and sustainably.

Analysis of temperature, humidity, rainfall, and wind velocity on dengue hemorrhagic fever in Bandung municipality

The trend of dengue hemorrhagic fever prevalence every year continues to show an increase and number of deaths. This is due to an increase in the population of aedes aegypti mosquitoes. Climate change has the potential to affect mosquito-borne diseases, including dengue fever, which is a vulnerability for residents in Bandung Municipality. This research aims to analyse the relationship between temperature, humidity, rainfall, and wind velocity with dengue hemorrhagic fever in Bandung Municipality. The methodology research used in this study is descriptive analysis with a cross-sectional approach. This research was conducted in Bandung Municipality. The study samples were taken from data on dengue hemorrhagic fever sufferers, as well as data on temperature, humidity, rainfall, and wind speed. This study used secondary data. The data collected is in the form of data on temperature, humidity, rainfall, and wind speed, and the number of cases. To assess the correlation between variables using the person correlation test. To test the effect of all four variables simultaneously on the incidence of dengue hemorrhagic fever using a linear regression test. Average value of air temperature is 25.8°C, air humidity is 69.9%, rainfall is 201.5 mm, and the wind velocity is 1.8 knots. The prevalence of dengue hemorrhagic fever is 232.5 cases. There is a significant relationship between humidity with dengue hemorrhagic fever prevalency (p = 0.018, r = 0.873). Wind velocity with dengue hemorrhagic fever prevalency (p = 0.018, r = 0.629). The result of the coefficient of determination test on temperature, humidity, rainfall, and wind velocity with DHF cases is R2 = 0.745. The increase in dengue prevalence in Bandung City occurred from January to June, the decrease in prevalence occurred from July to December. Variations in temperature, humidity, rainfall and wind speed can simultaneously affect the incidence of dengue fever in Bandung. Therefore, in the future it is necessary to increase mosquito nest eradication activities to prevent dengue transmission considering that this disease has the potential to spread at any time.

Wake measurement of wind turbine under yawed conditions using UAV anemometry system

This study employs a UAV anemometry system to assess the wind field around a yawed wind turbine, particularly focusing on its wake during operational conditions. The research findings reveal that the evolution of wind turbine wakes follows distinct patterns at various downstream distances. Turbulence intensity notably amplifies within regions characterized by significant fluctuations in mean wind speed. Specifically, in yawed conditions, the areas with the highest turbulence generated by the rotor coincide with zones exhibiting pronounced variations in mean wind speed. The heightened turbulence within the wake region to some extent constrains the safety and economic viability of wind farms. Turbulence intensity increases significantly in the region where the average wind speed changes greatly, that is, under the yaw state of the wind turbine, this region is characterized with the strongest turbulence generated by the rotor. The UAV anemometry system's wind speed assessment closely matches predictions from the Y-3DJGF model, accounting for wake experience coefficient adjustments. Furthermore, in yaw conditions, the wind turbine's wake trajectory exhibits some deviation from the incident flow's direction, with an initial increase in slope followed by gradual stabilization. As the downstream distance increases, the trajectory will eventually establish a consistent trend with the incident flow. The cost-effective and flexible UAV anemometry system enhances wind field measurements, offering an innovative approach for wind energy sector research and engineering.

ASV station keeping under wind disturbances using neural network simulation error minimization model predictive control

AbstractStation keeping is an essential maneuver for autonomous surface vehicles (ASVs), mainly when used in confined spaces, to carry out surveys that require the ASV to keep its position or in collaboration with other vehicles where the relative position has an impact over the mission. However, this maneuver can become challenging for classic feedback controllers due to the need for an accurate model of the ASV dynamics and the environmental disturbances. This work proposes a model predictive controller using neural network simulation error minimization (NNSEM–MPC) to accurately predict the dynamics of the ASV under wind disturbances. The performance of the proposed scheme under wind disturbances is tested and compared against other controllers in simulation, using the robotics operating system and the multipurpose simulation environment Gazebo. A set of six tests was conducted by combining two varying wind speeds that are modeled as the Harris spectrum and three wind directions (, , and ). The simulation results clearly show the advantage of the NNSEM–MPC over the following methods: backstepping controller, sliding mode controller, simplified dynamics MPC (SD‐MPC), neural ordinary differential equation MPC (NODE‐MPC), and knowledge‐based NODE MPC. The proposed NNSEM–MPC approach performs better than the rest in five out of the six test conditions, and it is the second best in the remaining test case, reducing the mean position and heading error by at least % ( m) and % (), respectively, across all the test cases. In terms of execution speed, the proposed NNSEM–MPC is at least 36% faster than the rest of the MPC controllers. The field experiments on two different ASV platforms showed that ASVs can effectively keep the station utilizing the proposed method, with a position error as low as m and a heading error as low as within time windows of at least s. This would increase the potential applications of ASVs for launch, recovery, and replenishment in long‐term surveys in collaboration with other autonomous systems.

Maximum Power Point Tracking control of a variable speed wind turbine via a T-S fuzzy model-based approach

This paper proposes a multi-objective H2/H ∞ maximum power tracking control of a variable speed wind turbine to minimize the H2 tracking error and ensure the H ∞ model reference-tracking performance, simultaneously. The optimal condition is obtained via a boost converter use, which adapts the load impedance to the wind turbine generator. Thus, based on the fuzzy T-S model, a multi-objective Maximum Power Point Tracking (MPPT) controller is developed, ensuring maximum power transfer, despite wind speed variation and system uncertainty. To specify the optimal trajectory to follow, a TS reference model is proposed taking as input the optimal rectified DC current. The conditions of stability and stabilization are expressed in terms of linear matrix inequality (LMI) for uncertain and disturbed T-S models leading to determining the controller gains. Finally, an example of MPP tracking applied to a Wind Energy Conversion System (WECS) illustrates the effectiveness of the proposed fuzzy control law.

The Optimal Span Length of the Overhead Catenary System for the Gemas-Johor Bahru Electrified Double Track Project

The span term in the Overhead Catenary System (OCS) refers to the distance between two subsequent supports in the running track direction. Longer spans are preferable for economic reasons because they reduce the number of supporting structures. The maximum span along the track was suggested by OCS designers using a structure spacing table and chart with varying wind speeds and radius of curvature. The structure spacing chart is a logarithmic curve that is plotted based on the specifications of the project and design constraints. According to the current design, the maximum span length for the Gemas-Johor Bahru Electrified Double Track Project is 72 metres (GJBEDTP). The current OCS design can be upgraded to provide a greater maximum span. This study looked at the economic benefits of increasing the maximum span length of the GJBEDTP in terms of reducing the total number of supporting structures. The track is separated into many equal-length track sections, and their individual spans are selected using the wind study assessment report and the structure spacing chart. Divide the total track length by the average span to get the estimated total number of supporting structures. In this study, a graph showing the percentage of cost savings and the maximum allowable span is plotted. In this optimization issue, the Elbow approach is used to select a point where diminishing returns are no longer worth the extra expense of going for a longer span. According to the results, the maximum span of 72 m is the ideal span for the GJBEDTP.

Variability and trends of near-surface wind speed over the Tibetan Plateau: The role played by the westerly and Asian monsoon

Near-surface wind speed exerts profound impacts on many environmental issues, while the long-term (≥60 years) trend and multidecadal variability in the wind speed and its underlying causes in global high-elevation and mountainous areas (e.g., Tibetan Plateau) remain largely unknown. Here, by examining homogenized wind speed data from 104 meteorological stations over the Tibetan Plateau for 1961–2020 and ERA5 reanalysis datasets, we investigated the variability and long-term trend in the near-surface wind speed and revealed the role played by the westerly and Asian monsoon. The results show that the homogenized annual wind speed displays a decreasing trend (−0.091 m s−1 per decade, p < 0.05), with the strongest in spring (−0.131 m s−1 per decade, p < 0.05), and the weakest in autumn (−0.071 m s−1 per decade, p < 0.05). There is a distinct multidecadal variability of wind speed, which manifested in an prominent increase in 1961–1970, a sustained decrease in 1970–2002, and a consistent increase in 2002–2020. The observed decadal variations are likely linked to large-scale atmospheric circulation, and the correlation analysis unveiled a more important role of westerly and East Asian winter monsoon in modulating near-surface wind changes over the Tibetan Plateau. The potential physical processes associated with westerly and Asian monsoon changes are in concordance with wind speed change, in terms of overall weakened horizontal air flow (i.e., geostrophic wind speed), declined vertical thermal and dynamic momentum transfer (i.e., atmospheric stratification thermal instability and vertical wind shear), and varied Tibetan Plateau vortices. This indicates that to varying degrees these processes may have contributed to the changes in near-surface wind speed over the Tibetan Plateau. This study has implications for wind power production and soil wind erosion prevention in the Tibetan Plateau.

Improved Maximum Power Point Tracking Control for D-PMSG Systems: Fuzzy Gradient Step Approach

Improved Maximum Power Point Tracking Control for D-PMSG Systems: Fuzzy Gradient Step Approach

Analysis of short-term wind speed variation, trends and prediction: A case study of Tamil Nadu, India

AbstractPurposeThe purpose of this research article is to analyze the short-term wind speed and develop a framework model to overcome the challenges in the wind power industry.Design/Methodology/ApproachReal data with a case study of wind speed is presented to illustrate the advantages of this new wind speed analytical framework. Hourly measurements of wind speed are observed, and the experiments are conducted using tools such as ANOVA, control charts, trend analysis, and predictive models. The August month data for over 13 years from modern era retrospective-analysis for research and applications (MERRA) National aeronautics and space administration (NASA) for Coimbatore and Erode locations in Tamil Nadu, India, have been used. The results were considered for the study to understand the wind speed data and the implementation of new wind power projects in India.FindingsThe essence of the proposed wind speed analytical framework is its flexible approach, which enables the effective integration of wind firms’ individual requirements by developing tailor-made analytical evaluations.Originality/ValueThis article derives the wind speed analytical framework with the application of statistical tools and machine learning algorithms.

Optimizing Power Control for Dual Excited Synchronous Generators in Wind Turbine Systems

This study presents a MATLAB Simulink-based approach for optimizing power control in wind turbine systems with dual excited synchronous generators (DESGs), implementing an Adaptive Neuro-Fuzzy Inference System (ANFIS) controller. DESGs offer advantages in efficiency and reliability, necessitating effective power control strategies. The proposed methodology integrates Simulink models of wind turbine dynamics, DESG behavior, and an ANFIS controller to optimize power output while ensuring stability and minimizing losses. Various factors such as wind speed variations and grid conditions are incorporated into the simulation environment. The ANFIS controller dynamically adjusts DESG control parameters based on real-time inputs, enhancing system adaptability and performance. Simulation graphs validate the efficiency of proposed approach in improving DESG performance and overall wind turbine system efficiency. This research contributes to advancing renewable energy technologies by leveraging MATLAB Simulink and ANFIS controllers for optimized power control in DESG-based wind turbine systems. Key Words: Wind power generation, Grid, DESG, MSC, GSC, and ANFIS.