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In the Inner Mongolia region, sand and dust storms are prevalent throughout the year, with sand erosion having a particularly significant impact on the performance of wind turbine blades. To enhance the performance stability of wind turbines and reduce operation and maintenance costs, this study delves into the specific impact of sand-laden wind erosion on the aerodynamic performance of scaled-down wooden wind turbine blades. The experiment conducts vehicle-mounted tests on scaled models of 1.5 MW wind turbine blades that have been eroded by wind-sand flows from different zones, analyzing the changes in aerodynamic performance of wind turbines caused by the erosion. The results indicate that with an increase in the angle of installation, both the overall power output and the wind energy utilization coefficient of the wind turbines show a declining trend. The power outputs of both the partially eroded group and the fully eroded group are unable to reach the rated power level of 100 W. Compared to the uneroded group, the leading-edge eroded group demonstrated higher power output and wind energy utilization coefficients across most wind speed ranges. This finding verifies the possibility that the drag-reducing effect caused by pits from leading-edge erosion has a positive impact on the aerodynamic performance of the blades. It also provides a new research perspective and strong evidence for the study of erosion effects on wind turbine blades and the optimization of their aerodynamic performance. [ABSTRACT FROM AUTHOR]

In this study, a segmented layered bistable piezoelectric energy harvester (SBPEH) has been developed and thoroughly investigated for its wind energy harvesting capabilities. The SBPEH's performance was comprehensively evaluated through the creation of a detailed 3D finite element model using Abaqus and XFlow software, and further validated via rigorous wind tunnel experiments. The results of this research demonstrate the SBPEH's remarkable potential for wind energy harvesting. With a wide operational wind speed range, it achieved a maximum measured power output of 5.476 mW, coupled with an outstanding power density of 68.45 mW/cm3, both of which were realized at a wind speed of 13 m/s. It is worth noting that as the wind speed surpasses a specific threshold, the energy harvesting efficiency of the SBPEH experiences a decline, emphasizing the importance of identifying the optimal operational wind speed range for maximizing energy output. These findings offer valuable insights for the enhancement and application of SBPEHs, contributing to the advancement of renewable energy technologies and providing an eco‐friendly solution for power generation. Highlights: The study introduces a new segmented layered bistable piezoelectric energy harvester (SBPEH) design for wind energy harvesting, addressing the limitations of traditional harvesters.Performance is evaluated through 3D finite element modeling, ABAQUS, Xflow simulations, and wind tunnel experiments.The SBPEH operates efficiently across a broad wind speed range (6–14 m/s), showcasing optimal output power of 5.476 mW at 13 m/s. [ABSTRACT FROM AUTHOR]

Compared to conventional energy sources, wind power is a clean energy source with high intermittence and uncertainty. As a system that converts wind energy into electricity, wind farms inevitably face severe reliability issues. In this paper, based on reliability theory, a new reliability modeling method for wind farms is proposed. Firstly, a belief reliability model for wind farms is constructed. Then, a power generation model based on two-directional rotor equivalent wind speed is established to represent the wind farm performance in the belief reliability model. Finally, several numerical studies are conducted to verify the power generation model under different wind speeds and directions, to demonstrate the belief reliability model with different levels of uncertainty, and to compare the belief reliability considering two-directional rotor equivalent wind speed with other methods. [ABSTRACT FROM AUTHOR]

The wind power generation system of a 5 MW horizontal axis wind turbine has a high wind energy conversion efficiency. The proportion of installed capacity in practical production applications is increasing year on year, so that the stability of its operation becomes a central factor in determining the productivity of the wind farm in question. This paper takes a 5 MW wind turbine as the research object and proposes a parameter‐adaptive robust model predictive control method to achieve self‐optimization of controller parameters through a Bayesian optimization approach. A robust model predictive control strategy, aiming to reduce the power fluctuation while maximizing the power output, is developed in this paper to enhance the dynamic economic performance under uncertain wind speed variation. A Bayesian algorithm is used in this paper to optimize the parameters of the controller. Moreover, wind speeds are simulated using TurbSim for different turbulence intensities of 5%, 10%, and 15% turbulence. Finally, the robust model predictive control toolbox in MATLAB is designed and simulated. The results show that the operational instability of the wind energy system is overcome. Meanwhile, the robustness of the wind energy system operation is improved compared to the traditional model predictive control approach. [ABSTRACT FROM AUTHOR]

Reliable and accurate wind-speed forecasts significantly impact the efficiency of wind power utilization and the safety of power systems. In addressing the performance enhancement of transformer models in short-term wind-speed forecasting, a multi-step prediction model based on variational mode decomposition (VMD), temporal convolutional network (TCN), and a transformer is proposed. Initially, the Dung Beetle Optimizer (DBO) is utilized to optimize VMD for decomposing non-stationary wind-speed series data. Subsequently, the TCN is used to extract features from the input sequences. Finally, the processed data are fed into the transformer model for prediction. The effectiveness of this model is validated by comparison with six other prediction models across three datasets, demonstrating its superior accuracy in short-term wind-speed forecasting. Experimental findings from three distinct datasets reveal that the developed model achieves an average improvement of 52.1% for R2. To the best of our knowledge, this places our model at the leading edge of wind-speed prediction for 8 h and 12 h forecasts, demonstrating MSEs of 1.003 and 0.895, MAEs of 0.754 and 0.665, and RMSEs of 1.001 and 0.946, respectively. Therefore, this research offers significant contributions through a new framework and demonstrates the utility of the transformer in effectively predicting short-term wind speed. [ABSTRACT FROM AUTHOR]

The study's main objective was to analyse daily wind speed data in Brazil collected on‐site from 1961 to 2020 and assess changes in two 30‐year climatological normals, denoted as P1 and P2. Data were sourced from 54 INMET‐managed meteorological stations, with missing data filled using the bootstrap expectation maximization algorithm. Imputation performance was evaluated, and results suggested that the 10% imputed level was most reliable, showing low RMSE values (below 0.5), SD values near 1, and r values exceeding 0.93. This method proved suitable for reconstructing historical wind speed data in Brazil. The analysis included the application of the Mann–Kendall test to detect linear trends and Sen's slope test to quantify trend intensity, both at a 5% significance level. Positive trends during P2 were observed in the Amazon basin and Brazil's semiarid region, while negative trends increased along the coast and in the Southeast region. Average wind speeds during P1 were higher than in P2. Comparing the two climatological normals revealed significant positive trends in some regions during P2, with an increase in average monthly variation of up to 0.85 m·s−1. The average slopes of significant trends were −1 and 1 m·s−1·decade−1 for all periods. The results indicated high wind magnitudes, with maximum power density of 778.08 W·m−2 for the NEB region, 170.90 W·m−2 for the SEB region and 401.67 W·m−2 for the SUB, making these regions promising for future wind power projects. These findings have the potential to enhance the optimization of renewable wind energy generation in Brazil. [ABSTRACT FROM AUTHOR]

This study aims to conduct resource assessments at five offshore locations (L1–L5) in the Gulf of South Suez for possible development of wind farms. For this purpose, the hourly mean wind speed and direction data at 100 m above mean sea level (AMSL) and other meteorological data at near water surface from ERA5 (fifth-generation reanalysis for the global climate weather) is used. Statistical analyses, wind power, energy estimation, plant capacity factor, etc., have been performed using Windographer software. The cost of energy and greenhouse gas emissions are conducted based on international installation cost per kW and emissions factor per kWh from the literature. The data covers 43 years, between 1979 and 2021. The results show that at L1 to L5 locations, the long-term mean wind speed (WS) and wind power density (WPD) values are 8.678 m/s and 533 W/m2, 4.371 m/s and 78 W/m2, 8.917 m/s and 587 W/m2, 7.856 m/s and 406 W/m2, 8.072 m/s and 453 W/m2, and 7.876 m/s and 406 W/m2 at 100 m ASL, respectively. Based on the site-specific parameters (WS, WPD, WPC, MWVI, MWSI, AWVI, AWSI, minimum zero power, maximum rated power, PCF, COE, and maximum wind duration), values of 8.917 m/s, 587 W/m2, 1.125, 34.92, 0.358, 101.9, 6.26%, 5.76%, 54.04%, 0.007 USD/kWh, and 91%; L2 are identified as the most relevant area for wind farm development among the studied locations. At L2 site, the wind turbine WT2 can deliver annually 21.266 GWh of energy at respective plant capacity factors of 48.55%. [ABSTRACT FROM AUTHOR]

Wind energy in the atmospheric boundary layer serves as the primary source for energy absorption and structural load on wind turbines. However, the impact of turbulent coherent structures on the aerodynamic performance and wake characteristics of wind turbines has not been comprehensively evaluated. In this study, the proper orthogonal decomposition (POD) method is employed to assess the influence of turbulent coherent structures of varying scales on the aerodynamic performance and wake characteristics of wind turbines in the neutral atmospheric boundary layer. The results show that turbulent coherent structures are the main factor that determines the wind velocity fluctuation, aerodynamic performance and wake characteristics of wind turbine in the atmospheric boundary layer. When considering the 13th or lower order POD mode, the wind velocity fluctuation increases with the increase of energy content (more POD modes) of the turbulent coherent structures. When considering the first 19 POD modes, the dynamic loads and power of wind turbine fluctuate with high frequencies, the thrust fluctuates in an amplitude range between 2.4 % and 13.9 % around the mean value, and the power fluctuates from 4.5 % to 28.6 % of the mean value. When considering the first 40 POD modes, the average power generation of the wind turbine increases by 26 % compared to the case with no turbulent structures considered. The study of turbine wake shows that turbulent coherent structures can expand the wind turbine wake approximately to a width of 2.5 D and a height of 3 D (D is the diameter of the wind turbine), offset the wake approximately to 2 D , and move forward the position of the wake vortex beginning to dissipation approximately to 7 D behind the wind turbine. In addition, turbulent coherent structures can accelerate the wake velocity recovery by increasing the momentum exchange between the atmospheric boundary layer and wind turbine wake. • A POD-based inflow generation method is established for wind turbine simulation. • Study into the coherent structures with various energy contributions on wind turbine performance. • Obtained the most influence energy contribution of coherent structures in the ABL. • Unveil the influence of coherent structures on the expansion and deflection of wind turbine wakes. [ABSTRACT FROM AUTHOR]

We evaluate the temporal complementarity in daily averages between wind and solar power potential in Chile using Spearman's correlation coefficient. We used hourly wind speed and solar radiation data for 176 geographic points from 2004 to 2016. The results allow us to identify four zones: Zone A1 on the coast and in the valleys in the north of Chile between latitudes 18° S and 36° S, with moderate positive correlation; Zone A2 in the north Andes between latitudes 25° S and 33° S, with weak negative correlation; Zone B in the center-south part of the country between latitudes 36° S and 51° S with moderate negative correlation; and Zone C in the south, between latitudes 51° S and 55° S with null or weak positive correlation. On the one hand, the interannual analysis shows that Zone A1 keeps uniform correlation values with negative asymmetry, i.e., higher correlation values. On the other hand, there is positive asymmetry in most of the years in Zone A2, i.e., lower (or negative) values of correlation. Zone B shows an interannual oscillation of the median correlation, while Zone C shows a larger dispersion in the interannual results. Significance analysis shows that 163 out of the 176 points are statistically significant, while Zones A1, A2, and B have significant correlations, with Zone C being marginally significant. The results obtained are relevant information for further studies on the location of hybrid generation facilities. We expect our methodology to be instrumental in Chile's energetic transition to a 100% renewable generation matrix. [ABSTRACT FROM AUTHOR]

The energy produced from renewable sources (solar, wind, hydro, geothermal, and biomass) provides direct access to clean and safe energy. Offshore wind energy, generated through wind farms, has traditionally relied on fixed structures, whereas innovative floating structures have been commercially applied since 2017. This study investigates offshore areas in Montenegro suitable for wind farm construction. Research on average annual wind speeds has successfully identified a surface area deemed suitable for constructing a wind farm in the Montenegrin part of the Adriatic Sea. Analysis of available bathymetric databases has pinpointed technical solutions for the supporting structures of wind turbines required to construct an offshore wind farm. Applying an assessment method to the defined surface of Montenegrin waters, seven blocks have been identified as suitable for wind farm construction. The research results indicate that wind farms can be built in Montenegrin waters with a technical potential corresponding to a total capacity of 2299.794 MW, which includes 2034.48 MW for floating structures, 126.759 MW for fixed structures, and 138.555 MW for jacket-fixed structures. [ABSTRACT FROM AUTHOR]

The applicability of the ERA5 reanalysis for estimating wind and solar energy generation over the contiguous United States is evaluated using wind speed and irradiance variables from multiple observational data sets. After converting ERA5 and observed meteorological variables into wind power and solar power, comparisons demonstrate that significant errors in the ERA5 reanalysis exist that limit its direct applicability for a wind and solar energy analysis. Overall, ERA5-derived solar power is biased high, while ERA5-derived wind power is biased low. During winter, the ERA5-derived solar power is biased high by 23% on average, while on an annual basis, the ERA5-derived wind power is biased low by 20%. ERA5-derived solar power errors are found to have consistent characteristics across the contiguous United States. Errors for the shortest duration and most extreme solar negative anomaly events are relatively small in the ERA5 when completely overcast conditions occur in both the ERA5 and observations. However, longer-duration anomaly events on weekly to monthly timescales, which include partially cloudy days or a mix of cloudy and sunny days, have significant ERA5 errors. At 10 days duration, the ERA5-derived average solar power produced during the largest negative anomaly events is 62% greater than observed. The ERA5 wind speed and derived wind power negative biases are largely consistent across the central and northwestern U.S., and offshore, while the northeastern U.S. has an overall small net bias. For the ERA5-derived most extreme negative anomaly wind power events, at some sites at 10 days duration, the ERA5-derived wind power produced can be less than half of that observed. Corrections to ERA5 are derived using a quantile–quantile method for solar power and linear regression of wind speed for wind power. These methods are shown to avoid potential over-inflation of the reanalysis variability resulting from differences between point measurements and the temporally and spatially smoother reanalysis values. The corrections greatly reduce the ERA5 errors, including those for extreme events associated with wind and solar energy droughts, which will be most challenging for electric grid operation. [ABSTRACT FROM AUTHOR]

With the continuous warming of the global climate, expanding the use of renewable energy has become one of the main social responsibilities. However, as the number of installed wind turbines and their physical dimensions continue to increase, the issue of generated noise has become increasingly significant in influencing the acceptance and endorsement of wind power projects by neighboring communities. In this paper, we investigated the noise generated by two wind turbine units with rated powers of 1.5 MW and 4.5 MW and analyzed the variations in low-frequency noise during their operation and shutdown periods. This research shows that the power of a single unit has a significant impact on the low-frequency noise emitted into the environment. Compared with 1.5 MW wind turbines, 4.5 MW wind turbines generate more low-frequency noise when operating at the same wind speed. Further analysis of the narrowband frequency spectra and one-third octave spectra of the measured noise indicates that the low-frequency noise from the 4.5 MW wind turbine increases significantly in the range of approximately 80 Hz to 300 Hz, with more pronounced variations below 250 Hz corresponding to changes in wind speed. However, the overall variations in low-frequency noise with wind speed are not as notable as those observed for the 1.5 MW wind turbine. Due to the relatively weak attenuation of low-frequency noise in the atmosphere, the higher low-frequency content of large wind turbines may cause more distress to residents near wind farms. The result of this study emphasizes that in the planning and design of wind power projects, in addition to considering the efficiency of single-unit power generation and the contribution of renewable energy, it is also necessary to pay full attention to noise emission issues to ensure that the project is widely supported and accepted in the community. [ABSTRACT FROM AUTHOR]

The article deals with the results of a comparative analysis carried out on the construction of wind turbines applied in energy generation systems using the wind of passing vehicles. The structures of turbines with horizontal and vertical axes of rotation were considered. Vertical axis wind turbines (VAWTs) have been observed to operate in various wind directions, including highly turbulent winds. Therefore, for further experimental research, VAWTs have been selected, i.e., Darrieus and Savonius turbines and their modifications. For the purpose of experimental research, the authors developed and implemented their own laboratory setup. This setup enabled the measurement of quantities such as torque, power, and power coefficient and allowed for the determination of the start-up parameters of the investigated turbines. Moreover, as part of the research, wind speed was also measured in field conditions at a distance of 1 m from the expressway. The wind speed obtained from passing vehicles did not exceed 9 m/s. For this reason, the tests of the considered turbines using the experimental setup were performed for wind speeds in the range of 5.8–8.6 m/s. The investigations were conducted based on the obtained results, and it was concluded that the helical Savonius turbine possessed the highest efficiency (0.2047 from a wind speed of 5.8 m/s) in generating energy from the wind produced by vehicles traveling on roads. [ABSTRACT FROM AUTHOR]

An enhanced database (RetroDB) of the Spanish wind energy resources, derived from a high spatial resolution integration with the WRF model, is proposed and evaluated. RetroDB provides hourly capacity factor (CF) values for the Spanish regions, along the period of 2007–2020, with an unprecedented spatial resolution. RetroDB estimates were benchmarked based on the ERA5 global reanalysis. A comprehensive evaluation study of both RetroDB and ERA5 estimates was conducted using surface and tall mast measurements, along with actual CF values. The extent to which RetroDB and ERA5 reproduced the CF spatial variability, distribution, and ramp distribution were specifically addressed. The results showed no differences between the global and regional reanalysis performance regarding nationally aggregated wind energy estimates. Nevertheless, RetroDB clearly shows a superior performance reproducing the wind speeds' and CFs' spatial and temporal distributions. This was found to be related to the higher reliability of RetroDB reproducing the aloft winds in complex topographic areas. Overall, the results clearly indicate that, in areas such as the study region, where the wind resources are mostly associated with topographic enhancements, high spatial resolution regional reanalyses are preferable over relative coarse reanalyses (e.g., ERA5), particularly for wind energy integration studies. RetroDB database is made publicly available. [ABSTRACT FROM AUTHOR]

In the past few years, different kinds of structural nonlinearities, such as external magnetic interaction and multistable structures, have been introduced into galloping energy harvesters to enhance energy harvesting efficiency. However, since the galloping-based piezoelectric energy harvester (GPEH) is a self-excited system, the conventional impedance matching method that is widely used in vibration energy harvesters is no longer valid for it. Therefore, the power characteristics of the nonlinear GPEH are still an open question to be solved. To this end, this paper is motivated to derive the approximate analytical solution of a monostable galloping-based piezoelectric energy harvester through the harmonic balance method and impedance matching theory. The analytical maximum power, power limit, and critical electromechanical coupling are studied from the perspective of the influence of structural nonlinearity on the power performance. Firstly, the approximate analytical solutions of output power, power limit, optimal resistance, and critical electromechanical coupling coefficient are derived. The accuracy of the analytical solution is verified by numerical simulation. It is found that the influence of structural nonlinearity on the power characteristics of the nonlinear GPEH is quite different under different wind speeds. After that, the power characteristics of the system under different wind speeds and different coupling conditions are investigated. The results showed that the maximum power of the system can be increased by introducing stiffness nonlinearity under low wind speed and weakly coupled configuration. Even more, the system can be shifted into a strongly coupled system when the nonlinearity is enhanced to a certain level. Reasonable design of stiffness nonlinearity can effectively reduce the critical electromechanical coupling, which indicates that stiffness nonlinearity is a feasible and effective way to improve the power performance of low-speed wind energy harvesting. [ABSTRACT FROM AUTHOR]

Wind profiling within operating wind farms is important for both wind resource assessment and wind power prediction. With increasing wind turbine size, it is getting difficult to obtain wind profiles covering the turbine-affecting area due to the limited height of wind towers. In this study, a stepwise quality control and optimizing process for deriving high-quality near-surface wind profiles within wind farms is proposed. The method is based on the radial wind speed obtained by the Doppler Wind Lidar velocity-azimuth display (VAD) technique. The method is used to obtain the whole wind profile from ground level to the height affected by wind turbines within a utility-scale onshore wind farm, in northern China. Compared with the traditional carrier-to-noise ratio (CNR) filter-based quality control method, the proposed data processing method can significantly improve the accuracy of the derived wind. For a 10 m wind speed, an increase in coefficient of determination (R2) from 0.826 to 0.932, and a decrease in mean absolute error (MAE) from 1.231% to 0.927% are obtained; while for 70 m wind speed, R2 increased from 0.926 to 0.958, and MAE decreased from 1.023% to 0.771%. For wind direction, R2 increased from 0.978 to 0.992 at 10 m, and increased from 0.983 to 0.995 at 70 m. The optimized method also presents advantages in improving the accuracy of derived wind under complex wind environments, e.g., inside a wind farm, and increasing the data availability during clear nights. The proposed method could be used to derive wind profiles from below the minimum range of a vertically operating scanning Doppler Lidar to a height affected by wind turbines. Combined with Doppler beam-swinging (DBS) scanning data, the method could be used to obtain the complete wind profile in the boundary layer. These wind profiles could be further used to predict wind power and evaluate the climate and environmental effects of wind farms. [ABSTRACT FROM AUTHOR]

The paper proposes a nonlinear controller called dual super-twisting sliding mode command (DSTSMC) for controlling and regulating the rotor side converter (RSC) of multi-rotor wind power systems that use doubly-fed induction generators. It was proposed that this controller be developed as an alternative to the direct power control (DPC), which makes use of a pulse width modulation (PWM) strategy to regulate the RSC's functioning. Overcoming the power/current quality issue with the proposed technique (DPC-DSTSMC-PWM) is characterized by great robustness and excellent performance. The designed strategy was contrasted with the standard method of control and other methods already in use. So, the unique proposed control strategy's robustness, performance, efficiency, and efficacy in enhancing system characteristics were tested and validated in Matlab/Simulink. In both tests, the proposed method resulted in significant improvements, reducing active power ripples by 83.33%, 57.14%, and 48.57% in the proposed tests. When compared with the traditional regulation method, the reduction rates of reactive power ripples are 64.06%, 52.47%, and 68.7% in the tests. However, in contrast to the conventional method, the proposed tests showed a decrease of between 72.46%, 50%, and 76.22% in the value of total harmonic distortion (THD) of the provided currents. These ratios show how effective the proposed plan is in ameliorating and enhancing aspects of the energy system. [ABSTRACT FROM AUTHOR]

Optimization of wind energy integration requires knowing the relationship between weather patterns and winds they cause. For a region with less-studied weather such as the Middle East, climatology becomes more vital. The Shagaya Renewable Energy Park in development in Kuwait experiences regional wind regimes that affect wind power production. Weather Research and Forecasting (WRF) model output allowed investigation into the weather regimes most likely to impact Shagaya Park. The self-organizing maps (SOMs) machine-learning method clustered the WRF output into six primary weather regimes experienced by the Middle East. According to the wind regimes mapped by the SOM, two of the six regimes have average wind speeds of approximately 9.9 and 8.6 m s−1 at 80 m near Shagaya Park, as well as wind speed and estimated wind power distributions that are more favorable to wind power production in Kuwait. One regime depicts a strong northwesterly wind called the summer shamal, and the second is associated with strong westerlies. Regimes less favorable for Kuwaiti wind power production are represented by the remaining four SOM nodes: local weak southeasterlies, an African nocturnal low-level jet, a daytime planetary boundary layer, and local northwesterlies from autumn to spring. The remaining four SOM nodes have average wind speeds of 5.7–7.2 m s−1 and wind speed and estimated wind power distributions which indicate regimes less favorable for wind power production in Kuwait. [ABSTRACT FROM AUTHOR]

With the increasing application of wind energy, reliable wind speed prediction has become imperative. However, prior studies predominantly concentrated on single-model predictions, disregarding the inherent uncertainty in wind speed. This oversight resulted in inadequate deterministic and probabilistic forecasting outcomes across varying scenarios. To make up for these shortcomings, a novel forecasting system combining a data preprocessing technique, a sub-model selection method, and a modified multi-objective integrate optimization strategy is designed in this paper. According to the data obtained from China's wind farm, the forecasting efficiency of this system is verified from multiple perspectives. The findings show that the system takes advantage of each model to boost the precision and stability of point prediction successfully. Furthermore, it achieves higher interval coverage and narrower interval width under distinct confidence levels. These results highlight the system's potential as a reliable technical support for efficient dispatching of the entire power system. [ABSTRACT FROM AUTHOR]

Efficient integration of wind energy requires accurate wind power forecasting. This prediction is critical in optimising grid operation, energy trading, and effectively harnessing renewable resources. However, the wind's complex and variable nature poses considerable challenges to achieving accurate forecasts. In this context, the accuracy of wind parameter forecasts, including wind speed and direction, is essential to enhancing the precision of wind power predictions. The presence of missing data in these parameters further complicates the forecasting process. These missing values could result from sensor malfunctions, communication issues, or other technical constraints. Addressing this issue is essential to ensuring the reliability of wind power predictions and the stability of the power grid. This paper proposes a long short-term memory (LSTM) model to forecast missing wind speed and direction data to tackle these issues. A fractional-order neural network (FONN) with a fractional arctan activation function is also developed to enhance generated wind power prediction. The predictive efficacy of the FONN model is demonstrated through two comprehensive case studies. In the first case, wind direction and forecast wind speed data are used, while in the second case, wind speed and forecast wind direction data are used for predicting power. The proposed hybrid neural network model improves wind power forecasting accuracy and addresses data gaps. The model's performance is measured using mean errors and R2 values. [ABSTRACT FROM AUTHOR]

The precise forecasting of wind speeds is critical to lessen the harmful impacts of wind fluctuations on power networks and aid in merging wind energy into the grid system. However, prior research has predominantly focused on point forecasts, often overlooking the uncertainties inherent in the prediction accuracy. For this research, we suggest a new approach for forecasting wind speed intervals (PI). Specifically, the actual wind speed series are initially procured, and the complete ensemble empirical mode decomposition coupled with adaptive noise (CEEMDAN) method decomposes the actual wind speed series into constituent numerous mode functions. Furthermore, a generative adversarial network (GAN) is utilized to achieve the wind speed PI in conjunction with the multivariate linear regression method. To confirm the effectiveness of the suggested model, four datasets are selected. The validation results suggest that this suggested model attains a superior PI accuracy compared with those of numerous benchmark techniques. In the context of PI of dataset 4, the PINAW values show improvements of 68.06% and 32.35% over the CEEMDAN-CNN and VMD-GRU values in single-step forecasting, respectively. In conclusion, the proposed model excels over the counterpart models by exhibiting diminished a PINAW and CWC, while maintaining a similar PICP. [ABSTRACT FROM AUTHOR]

Analyzing historical and reanalysis datasets for wind energy climatic characteristics offers crucial insights for wind farms and short-term electricity generation forecasting. However, large-scale wind farms in Chinese deserts, the Gobi, and barren areas often lack sufficient wind measurement data, leading to challenges in assessing long-term power generation revenue and introducing uncertainty. This study focuses on the Tengger Desert as the study area, processes the Coupled Model Intercomparison Project Phase 6 (CMIP6) data, and analyzes and compares wind energy's future characteristics utilizing a developed deep learning (DL) downscaling algorithm. The findings indicate that (1) the Convolutional Neural Network (CNN) downscaling model, with the Weather Research and Forecasting Model (WRF) numerical simulation results as the targets, exhibits spatial distribution consistency with WRF simulation results in the experimental area. (2) Through testing and validation with three practical wind measurements, the annual average wind speed error is below 4%. (3) In the mid-term future (~2050), the average wind speed in the experimental area remains stable with a multi-year average of approximately 7.00 m·s−1. The overall wind speed distribution range is significant, meeting the requirements for wind farm development. [ABSTRACT FROM AUTHOR]

Cup anemometers measure average wind speed in the atmosphere and have been used for one and a half centuries by meteorologists. Within the last half century, cup anemometers have been used extensively in wind energy to measure wind resources and performance of wind turbines. Meteorologists researched cup anemometer behaviour and found dynamic overspeeding to be an inherent and significant systematic error. The wind energy community has strong accuracy requirements for power performance measurements on wind turbines, and this led in the last 2 decades to new research on cup anemometer characteristics, which was taken to a new level with the development of improved calibration procedures, cup anemometer calculation models and classification methods. Research projects in wind energy demonstrated, by field and wind tunnel measurements, that angular response was a significant contributor to uncertainty and that dynamic overspeeding was a significant but less important contributor. Earlier research was mainly made on cup anemometers with hemispherical cups on long arms, ​​​​​​​and dynamic overspeeding was considered an inherent and high uncertainty error for cup anemometers. Research on conical cups on short arms has now shown that zero or low overspeeding is present on a well-designed cup anemometer, providing a much lower overspeeding uncertainty error. Different cup anemometer calculation models were investigated in order to find derived overspeeding characteristics. The general and often used parabolic torque coefficient model showed that zero overspeeding is present when the speed ratio roots of the torque coefficient curve go through the equilibrium speed ratio and zero. The two-cup drag model is a special case of the parabolic torque coefficient model but with the second root being reciprocal to the equilibrium speed ratio. The drag model always results in a positive maximum overspeeding of the order of 1.1 times the turbulence intensity squared. A linear torque coefficient results in maximum overspeeding levels equal to the turbulence intensity squared. Torque characteristics of a cup anemometer with hemispherical cups fit slightly well to the drag model, but a cup anemometer with conical cups does not fit to the drag model nor the parabolic model; it fits better to a partial linear model and even better to an optimized torque model. The most accurate modelling of cup anemometer characteristics is at present made with the ACCUWIND model (Dahlberg et al., 2006). This model uses tabulated torque coefficient and angular response data measured in a wind tunnel. The ACCUWIND model is found in International Electrotechnical Commission (IEC) wind turbine power performance standards, where it is used in a classification system for estimation of operational uncertainties. For an actual comparison of two cup anemometers, with respectively hemispherical and conical cups, the influence of dynamic overspeeding was found to be relatively low compared to angular response, but for conical cups it was specifically low. [ABSTRACT FROM AUTHOR]

This paper proposes a wind-speed-adaptive resonant piezoelectric energy harvester for offshore wind energy collection (A-PEH). The device incorporates a coil spring structure, which sets the maximum threshold of the output rotational frequency, allowing the A-PEH to maintain a stable output rotational frequency over a broader range of wind speeds. When the maximum output excitation frequency of the A-PEH falls within the sub-resonant range of the piezoelectric beam, the device becomes wind-speed-adaptive, enabling it to operate in a sub-resonant state over a wider range of wind speeds. Offshore winds exhibit an annual average speed exceeding 5.5 m/s with significant variability. Drawing from the characteristics of offshore winds, a prototype of the A-PEH was fabricated. The experimental findings reveal that in wind speed environments, the device has a startup wind speed of 4 m/s, and operates in a sub-resonant state when the wind speed exceeds 6 m/s. At this point, the A-PEH achieves a maximum open-circuit voltage of 40 V and an average power of 0.64 mW. The wind-speed-adaptive capability of the A-PEH enhances its ability to harness offshore wind energy, showcasing its potential applications in offshore wind environments. [ABSTRACT FROM AUTHOR]

In the practical environment, it is very common for the simultaneous occurrence of base excitation and crosswind. Scavenging the combined energy of vibration and wind with a single energy harvesting structure is fascinating. For this purpose, the effects of the wind speed and random excitation level are investigated with the stochastic averaging method (SAM) based on the energy envelope. The results of the analytical prediction are verified with the Monte-Carlo method (MCM). The numerical simulation shows that the introduction of wind can reduce the critical excitation level for triggering an inter-well jump and make a bi-stable energy harvester (BEH) realize the performance enhancement for a weak base excitation. However, as the strength of the wind increases to a particular level, the influence of the random base excitation on the dynamic responses is weakened, and the system exhibits a periodic galloping response. A comparison between a BEH and a linear energy harvester (LEH) indicates that the BEH demonstrates inferior performance for high-speed wind. Relevant experiments are conducted to investigate the validity of the theoretical prediction and numerical simulation. The experimental findings also show that strong random excitation is favorable for the BEH in the range of low wind speeds. However, as the speed of the incoming wind is up to a particular level, the disadvantage of the BEH becomes clear and evident. [ABSTRACT FROM AUTHOR]

There is an urgent need to develop accurate predictions of power production, wake losses and array–array interactions from multi-GW offshore wind farms in order to enable developments that maximize power benefits, minimize levelized cost of energy and reduce investment uncertainty. New, climatologically representative simulations with the Weather Research and Forecasting (WRF) model are presented and analyzed to address these research needs with a specific focus on offshore wind energy lease areas along the U.S. east coast. These, uniquely detailed, simulations are designed to quantify important sources of wake-loss projection uncertainty. They sample across different wind turbine deployment scenarios and thus span the range of plausible installed capacity densities (ICDs) and also include two wind farm parameterizations (WFPs; Fitch and explicit wake parameterization (EWP)) and consider the precise WRF model release used. System-wide mean capacity factors for ICDs of 3.5 to 6.0 MWkm−2 range from 39 to 45% based on output from Fitch and 50 to 55% from EWP. Wake losses are 27–37% (Fitch) and 11–19% (EWP). The discrepancy in CF and wake losses from the two WFPs derives from two linked effects. First, EWP generates a weaker 'deep array effect' within the largest wind farm cluster (area of 3675 km2), though both parameterizations indicate substantial within-array wake losses. If 15 MW wind turbines are deployed at an ICD of 6 MWkm−2 the most heavily waked wind turbines generate an average of only 32–35% of the power of those that experience the freestream (undisturbed) flow. Nevertheless, there is no evidence for saturation of the resource. The wind power density (electrical power generation per unit of surface area) increases with ICD and lies between 2 and 3 Wm−2. Second, EWP also systematically generates smaller whole wind farm wakes. Sampling across all offshore wind energy lease areas and the range of ICD considered, the whole wind farm wake extent for a velocity deficit of 5% is 1.18 to 1.38 times larger in simulations with Fitch. Over three-quarters of the variability in normalized wake extents is attributable to variations in freestream wind speeds, turbulent kinetic energy and boundary layer depth. These dependencies on meteorological parameters allow for the development of computationally efficient emulators of wake extents from Fitch and EWP. [ABSTRACT FROM AUTHOR]

In recent years, the proportion of wind power in new energy generation has gradually increased. The natural wind in wind farms is subject to velocity attenuation by the wake effect, so improving the efficiency of wind farm power generation has become a problem that must be solved for wind power generation. Considering the uncertainty of wind farms, we regard wind farm layout optimization (WFLO) as a strongly nonlinear problem. In this paper, we improve the sparrow search algorithm (SSA) using dynamic opposite learning (DOL) strategy. Twenty-eight benchmark test results prove that compared with other algorithms, the improved algorithm DOLSSA has excellent robustness and the ability of searching for a better solution when solving a strongly nonlinear optimization problem, and the DOL strategy effectively improves the shortcomings of the original algorithm which is prone to local optimization and space limitation. In this paper, the authors establish the dynamic rotational coordinates of wind farms and set six different physical scenarios by considering the wind direction and wind speed variables, and the results prove that the performance of DOLSSA is optimal. [ABSTRACT FROM AUTHOR]

This document is a correction notice for an article titled "Modelling the wind potential energy for metallurgical sector in Albania" published in Scientific Reports. The correction addresses errors in the affiliations of the authors and the legends of several figures and tables. The corrected version of the article is now available. The article is licensed under a Creative Commons Attribution 4.0 International License. [Extracted from the article]

This study proposes a novel framework that couples the general likelihood uncertainty estimation (GLUE) method with a deterministic forecasting approach to conduct a new uncertainty analysis approach for assessing the energy production of operational wind turbines installed in the Jhongtun wind farm at Penghu (an island in the middle of Taiwan Strait). The 10-year measured data of wind speeds and energy output collected on these wind turbines is divided into two 5-year data sets for the present analysis framework of execution and validation to demonstrate the predictability of the GLUE method. The present study considers 15 scenario testing cases with various time periods, i.e., twelve months, the strong-wind (October-March) regime, the weak-wind (April-September) regime, and one year, for the framework to investigate the applicability of the GLUE method on long-term wind energy forecasting. In the execution framework, the 5-year measured data is used by the GLUE method to access the uncertainties involved in the deterministic approach (i.e., the shape and scale parameters of the Weibull wind speed distribution (WWSD), the performance curve, and the capacity factor) with two confidence intervals of 50% and 90%. The framework is then validated by the measured capacity factors in the last 5-year data and compared with the results of the uncertainty analysis approach by the Monte Carlo (MC) approach to discover the applicability of the new uncertainty analysis approach. From the simulated results, it is found that the proposed uncertainty analysis approach provides predictions of confidence intervals that match the measured data better than the MC-based uncertainty analysis approach. Specifically, the proposed approach can match the measured capacity factors in all the simulated scenarios. Conversely, the MC-based approach is found to create narrow confidence intervals that cannot completely capture the measured capacity factors, particularly for the strong-wind, weak-wind, and one-year scenarios. Therefore, this novel uncertainty analysis approach is proven to be useful in predicting the uncertainties of wind energy production. [ABSTRACT FROM AUTHOR]

The accurate characterization of offshore wind resources is crucial for the efficient design and operation of wind energy projects. However, the scarcity of in situ observation in marine environments requires exploration of alternative approaches. For this reason, this study presents a comprehensive comparison between wind profiles derived from the Advanced Scatterometer ASCAT) satellite observations and the ERA5 reanalysis dataset against ship-based lidar measurements in the Northern Baltic Sea. In order to extrapolate ASCAT observations to wind turbine relevant heights, a long-term correction approach has been implemented. Due to the sensitivity of this method to the accurate characterization of the atmospheric stability, two different approaches were assessed to characterize the stability conditions, showing a great robustness of the methodology employed and leading to noticeable differences only in specific coastal locations. The comparison reveals a close agreement between ASCAT and ERA5 beyond 40 km distance from the coast. Specifically, ASCAT tends to overestimate the mean wind speed derived from lidar measurements, while ERA5 exhibits a consistent underestimation. In terms of vertical accuracy, ERA5 displays a consistent bias of approximately 0.5 m s-1 along the profile, whereas ASCAT exhibits a smaller bias within the lower 200 m of the profile. These findings underline the potential and limitations of ASCAT-derived wind profiles and ERA5 for offshore wind characterization. [ABSTRACT FROM AUTHOR]

Recently, wind energy has been utilized globally as a renewable, sustainable, and eco-friendly energy source. However, wind energy's unpredictable and stochastic nature influences its entry into the national electrical grid. An effective wind speed prediction is required to meet these challenges. In this article, the Nonlinear Autoregressive Neural Network (NARNN) model is used and investigated for short-term wind speed forecasting by taking a dataset from the Kandıra wind farm in Kocaeli- Türkiye. The crux of the paper is to improve the actual application of the existing NARNN model with factual data using a different number of neurons of the hidden layer, delays, and training functions in the learning phase called the model's hyperparameters. The mean squared error (MSE) and determination coefficient (R2) are used as performance measures. As a result, the hyperparameter optimization for wind speed prediction using the NARNN increased the forecasting performance. Suggested NARNN model is compared with its exogenous version (NARXNN) using three extra inputs. It is observed that NARNN is not falling behind NARXNN because they provide close results, and NARNN has been shorter to run. Likewise, the learning algorithms were also compared, and it turned out that Bayesian Regularization (BR) is the best learning algorithm. Still, Levenberg Marquardt (LM) algorithm is much faster to execute and provides close results to BR. [ABSTRACT FROM AUTHOR]

In wind power prediction, the input probability distributions in the different sub-periods are shifted owing to the strong randomness of the input features, such as wind speed and direction. This may violate the assumption for machine learning that the training and test data meet the condition of being independent and identically distributed, resulting in an insufficient generalization ability of the prediction model that is trained with the training data and applied to unknown test data. To address this problem, this study proposes an adaptive temporal transformer method for short-term wind power forecasting. First, a temporal transformer model with a gate recurrent unit and multi-head attention layers was used to extract the short- and long-term temporal information of the multiple input variables. Then, an adaptive learning strategy consisting of two stages—temporal distribution characterization and temporal distribution matching—was developed to explore the common knowledge hidden in each sub-period. The case results for an actual wind farm in northwest China showed that the proposed method could effectively weaken the adverse effects of the shifts in time series distribution on forecasting and improve the accuracy and generalization of short-term wind power prediction. [ABSTRACT FROM AUTHOR]

Airframe noise currently is a bottle neck in various applications, e.g., wind energy, maritime applications, and aircraft. Airframe noise is significantly increased by the presence of inflow turbulence. High inflow turbulence influences the boundary layer and wall-pressure fluctuations close to the trailing edge of airfoils. In this research, measurements of boundary layer and wall-pressure fluctuations near the trailing edge of an airfoil are conducted to investigate how the inflow turbulence affects the trailing-edge noise generation mechanism. Far-field noise measurements of additional three airfoils are shown to understand the role of the airfoil geometry in the dominant noise source for the cases of inflow turbulence and to generalize the observed increase in trailing-edge noise. Inflow turbulence leads to an increase in both the wall-pressure spectrum and spanwise correlation length. Trailing-edge noise increases due to the inflow turbulence in the entire frequency range at least 2 dB up to more than 15 dB for all the cases. The contribution of leading- and trailing-edge noise to the total noise varies with the airfoil geometry and inflow velocity, with the trailing-edge noise dominating in a larger frequency range for the thickest airfoil and for lower velocities. [ABSTRACT FROM AUTHOR]

The exploitation of offshore wind resources is a crucial step towards a clean energy future. It requires an advanced approach for high-resolution wind resource evaluations. We explored the suitability of the Sentinel-1 Level-2 OCN ocean wind field (OWI) product for offshore wind resource assessments. The SAR data were compared to in situ observations and three reanalysis products: the global reanalysis ERA5 and two regional reanalyses CARRA and NORA3. This case study matches 238 scenes from 2022 for the Goliat station, an oil platform located 85 km northwest of Hammerfest in the Barents Sea, where a new offshore wind park has been proposed. The analysis showed that despite their unique limitations in spatial and temporal resolutions, all data sources have similar statistical properties (RMSE, correlation coefficient, and standard deviation). The Weibull parameters characterizing the wind speed distributions showed strong similarities between the Sentinel-1 and all reanalysis data. The Weibull parameters of the in situ measurements showed an underestimation of wind speed compared to all other sources. Comparing the full reanalysis datasets with the subsets matching the SAR scenes, only slight changes in Weibull parameters were found, indicating that, despite its low temporal resolution, the Sentinel-1 Level 2 OWI product can compete with the more commonly used reanalysis products in the estimation of offshore wind resources. Its high spatial resolution, which is unmatched by other methods, renders it especially valuable in offshore areas close to complex coastlines and in resolving weather events at a smaller scale. [ABSTRACT FROM AUTHOR]

This article dealt the application of three-phase all-pass filter integrated with phase-locked loop (3Ø-APF-PLL) control approach in islanded electrical supply system for enhanced power quality (PQ). It also covers the effectiveness of proposed control under various operating conditions such as intermittent wind speed, variable solar irradiance, and unbalanced nonlinear load. An islanded electrical supply system includes wind energy generation, solar photovoltaic (SPV), and battery energy storage (BES). The proposed control technique provides a coordinated and balanced operation under a dynamical load. A 3Ø-APL-PLL-based control approach is proposed to extract the maximum value of fundamental component of load current and phase angle. The proposed control approach is utilized to operate voltage source converter (VSC) that performs multi-functions, such as reducing source current harmonics, power compensation (real & reactive), balancing of loads, and improving the overall power quality of the islanded SPV-wind-BES electrical supply system. The SPV system is developed using an incremental conductance (IC)-based maximum power point tracking (MPPT) technique to extract maximum power during the intermittent conditions of solar irradiance. A bidirectional controller (BDC) is designed to operate the charging/discharging modes of a BES. Generally, an islanded electrical supply system suffers from poor voltage & frequency regulation due to the intermittency of power generation sources, which can be solved by utilizing a BES with BDC control to manage the power leveling between the power generation sources and the unbalanced load. A 3Ø-APF-PLL control approach is compared to existing controllers like a least mean square (LMS) (Singh et al. in IET Power Electron 13:2767–2774, 2020) and second-order generalized integrator-based frequency locked loop (SOGI-FLL) (Golestan et al. in IEEE Trans Power Electron 34:213–224, 2018). Numerous parameters, such as the evaluation of the maximum value of the fundamental component, stability, steady-state error, and harmonic compensation capabilities, are taken into account when comparing 3Ø-APF-PLL to the control approaches described above. The dynamic parameters of the islanded electrical supply system are justified under the IEEE-519 standard. [ABSTRACT FROM AUTHOR]

This study compared the efficacy of the actuator line and actuator surface models in carrying out large-eddy simulations of a utility-scale wind turbine. A large-eddy simulation with the actuator surface and line models was employed to study the wake flow and power production of the turbine. While both the actuator models were employed for the blade representation, the nacelle was modeled using the actuator surface approach. Both of the actuator models demonstrated agreement in the mean velocity field, power production, and turbulence kinetic energy of the wake flow. Comparing the wake flow, power production, and turbulence kinetic energy results, it was found that the mean discrepancy between the two models was 0.6 % , 0.3 % , and 2.3 % , respectively. Despite the minor discrepancies, both actuator models accurately captured the hub vortex in the wake of the nacelle, evidenced by an energy peak in wind speed spectra at f / f ω ≈ 0.34 . [ABSTRACT FROM AUTHOR]

This computational study explores the potential of several soft-computing techniques for wind turbine (WT) output power (kW) estimation based on seven input variables of wind speed (m/s), wind direction (°), air temperature (°C), pitch angle (°), generator temperature (°C), rotating speed of the generator (rpm), and voltage of the network (V). In the present analysis, a nonlinear regression-based model (NRM), three decision tree-based methods (random forest (RF), random tree (RT), and reduced error pruning tree (REPT) models), and multilayer perceptron-based soft-computing approach (artificial neural network (ANN) model) were simultaneously implemented for the first time in the prediction of WT output power (WTOP). To identify the top-performing soft computing technique, the applied models' predictive success was compared using over 30 distinct statistical goodness-of-fit parameters. The performance assessment indices corroborated the superiority of the RF-based model over other data-intelligent models in predicting WTOP. It was seen from the results that the proposed RF-based model obtained the narrowest uncertainty bands and the lowest quantities of increased uncertainty values across all sets. Although the determination coefficient values of all competitive decision tree-based models were satisfactory, the lower percentile deviations and higher overall accuracy score of the RF-based model indicated its superior performance and higher accuracy over other competitive approaches. The generator's rotational speed was shown to be the most useful parameter for RF-based model prediction of WTOP, according to a sensitivity study. This study highlighted the significance and capability of the implemented soft-computing strategy for better management and reliable operation of wind farms in wind energy forecasting. [ABSTRACT FROM AUTHOR]

Wind energy is gaining importance owing to its renewable and environmentally friendly characteristics. However, the variability and stochastic nature of wind speed makes accurate forecasting difficult. Hence, this study introduces a novel approach (VMD-SCINet) for wind speed forecasting (WSF) by integrating the strengths of variational mode decomposition (VMD) and sample convolution and interaction network (SCINet) architecture for the prediction of wind speed. This study utilizes VMD as a denoising technique for wind speed data and incorporates SCINet to capture global patterns and long-range dependencies for the WSF. The wind speed data acquired from two distinct sites: Leicester, and Portland is used for the evaluation. To evaluate the WSF capability of the proposed hybrid model, it's performance is compared to robust models using data from two wind farms across six different time horizons such as 5-min, 10-min, 15-min, 30-min, 1-hour, and 2-hours. The results from two experiments demonstrate that the proposed approach outperforms other models, leading to a significant improvement in WSF accuracy across all evaluated time intervals. [ABSTRACT FROM AUTHOR]

Wind energy has gained attention as an alternative to fossil fuels due to its effectiveness as a renewable energy source. The precise and reliable prediction of wind speed is crucial for the effective utilization and harnessing of wind power. This study proposes a novel hybrid methodology for wind speed prediction (WSP) by combining improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) and a memory-efficient Reformer model. This hybrid methodology is proposed to improve the accuracy of WSP. This approach involves decomposing the input data into multiple sub-series using the intrinsic mode function (IMF) obtained from the ICEEMDAN algorithm. The resulting sub-series are then processed through a Reformer (REF) model to predict wind speed. The verification of the efficiency and progression of the hybrid model that is proposed is conducted through the utilization of wind speed data obtained from two distinct wind farms. The current WSP methods exhibit a decline in performance as the time ahead increases. Therefore, this study addresses the viability of the proposed model by examining six different time horizons: 5-minutes, 10-minutes, 15-minutes, 30-minutes, 1-hour, and 2-hours. For evaluating the efficiency of proposed model for WSP, ten individual WSP models and ten hybrid WSP models are used for the comparative analysis. Based on the results of the experiments and comparative analysis, it has been observed that the hybrid model proposed in this study exhibits superior performance compared to other models across all time horizons while simultaneously preserving memory efficiency. [ABSTRACT FROM AUTHOR]

This study examines the seasonal and intraseasonal modulation of near-inertial wind power associated with fluctuations in unidirectional wind speed in the Bay of Bengal (BoB). For that purpose, we use concurrent measurements of high-resolution in situ near-surface current and wind speed from six moorings in the BoB. It is found that the annual mean of near-inertial wind power in the BoB shows roughly similar magnitude (0.25–0.35 mW m−2) at all the mooring locations. However, in response to the seasonal evolution of monsoonal wind forcing, near-inertial wind power shows significant annual variability, with a maximum during summer (~ 0.4–0.5 mW m−2) and fall (~ 0.3–0.4 mW m−2) and a minimum during winter (~ 0.1 mW m−2) and spring (~ 0.2 mW m−2). In addition, it is also found that modulation of near-inertial wind power due to summer monsoon intraseasonal oscillation (MISO), such as its magnitude, reaches as large as ~ 1 mW m−2 at the mooring in the northern BoB during phases 3–4 of MISO. Using a high vertical resolution of current profile data, the near-inertial kinetic energy (NIKE) budget in the mixed layer in the northern BoB shows good temporal correspondence with the magnitude of the rate of change of NIKE and near-inertial wind power, with a maximum magnitude of the rate of change of NIKE lags the wind power by 24 hr. The NIKE budget also indicates that a significant portion of near-inertial wind power dissipates in the mixed layer and rarely energises the depth regime underneath the mixed layer. [ABSTRACT FROM AUTHOR]