Wind Engineering

This work focused on the prediction of generation of renewable energy (solar and wind) using the machine learning ML algorithms. Prediction of generation are very important to design the better microgrids storage. The various ML algorithms are as logistic regression LR and random forest RA and the ARIMA, time series algorithms. The performance of each algorithm is evaluated using the mean absolute error, mean squared error, root mean squared error, and mean absolute percentage error. The MAE value for the ARIMA (0.06 and 0.20) model for solar and wind energy is very less as compared to RF (15.65 and 61.73) and LR (15.78 and 54.65) of solar and wind energy. Same with MSE and RMSE, the MSE and RMSE value for the ARIMA of solar energy model obtained is 0.01 and 0.08 and wind energy is 0.07 and 0.27 respectively. Comparative analysis of all of these matrices of each algorithm for both the dataset, we concluded that the ARIMA model is best fit for the forecasting of solar energy and wind energy.

The combination of demand response as demand side management together with energy management system has become essential to minimize energy cost, to maintain continuous supply of electricity, and to improve the safety of power system operation. This paper studies the optimal energy dispatch of connected microgrid units containing photovoltaic panels, wind turbine generators, diesel generators, and the main grid. The optimal set point of microgrid’s units is determined to satisfy the required load demand for a day-ahead horizon time. As the demand response is an important way of demand side management, this paper proposes as the main contribution the implementation of demand response cost as one of the objective functions to be maximized to view its effect on load demand consumption, on MG energy production and on MG energy cost. The demand response is implemented by using an incentive based demand response program in the optimization model in addition to the fuel cost of diesel generators and the transfer cost of transferable power. The incentive payment offered by utilities is used to motivate consumers to change their energy consumption behavior and thus to reduce their power consumption and maintain the system reliability during on-peak periods. Thus the objective function is formulated to maximize microgrid operator’s demand response benefit, and to minimize both the fuel cost of diesel generators, and the transfer cost of transferable power. For this purpose, the defined objective function is solved by a Hybrid Particle Swarm Optimization with Sine Cosine Acceleration Coefficients (H-PSO-SCAC) algorithm for an optimal energy management system of the connected microgrid. For the simulation tests, different algorithms are examined in order to validate the effectiveness of the H-PSO-SCAC algorithm. The impact of demand response program is analyzed on the load demand consumption, on the microgrid energy production and its influence on the optimized microgrid cost function. The results demonstrate that the implementation of demand response has changed the previous situation that costumers do not participate in the operation of the power system. And it enables microgrid to decrease load consumption, microgrid energy production, as well as energy cost.

To increase the power yield, wind turbines have significantly grown in the last decades. Today, this growth is more and more limited by the weight of the structures and fatigue loads. To compensate these loads, especially flapwise root bending moments, trailing edge flaps can be used. They can change the lift of the blade with little delay to equalize the aerodynamic lift and by this reduce the fatigue amplitude. Such a trailing edge flap has been designed, developed, built and experimentally tested. It uses a flexible, morphing design to seal the entire mechanics against environmental influences, such as rain, dust, or insects. Therefore a design made from glass fiber reinforced plastics in combination with elastomer materials is used. In this paper the design process from the concept to two consecutive demonstrators is presented. Both are tested in the laboratory for their morphing characteristics.

In this paper the influence of the wave oscillation on the behavior of floating off-shore wind turbine is analyzed. Aerodynamic analysis of forces on wind turbine blade has been carried out as a function of the wind turbine tilt angle due to wave oscillation. This analysis has resulted in a theoretical model based on the variation of the angle of attack which allows the characterization of the turbine under the effect of the oscillation of the sea surface. The results obtained show the influence of the oscillation in a particular case and its impact on the generated power. Subsequently, a so-called “pitching compensation” system that allows eliminating the effect produced by the variation in the angle of attack and, therefore, minimizing the effect of oscillation on the power generated, is proposed.

In this paper is presented an optimal control technology of wind energy system recovering energy in a battery energy accumulator. This control technology makes it possible to regulate the speed of the electric generator at its optimum value, to avoid any over-speed problems leading to strong increases in current in the electrical components of the wind turbine, and subsequently to its destruction. This control technology also makes it possible to maintain the induced electromotive forces in phase with the phase’s currents of the generator to have an additive electromagnetic torque, and in this way the recovered energy will be optimal. This control technology is based on two conversions, one is an Alternative-Continuous conversion performed by an AC-DC converter with IGBTs, and the other is a DC-DC conversion performed by a booster chopper to regulate the voltage of recharges batteries at its nominal value.

This paper presents a study of the impact of icing on the performance of a pitch-regulated large wind turbine. Numerical simulations of six blade sections of the NREL 5 MW wind turbine at various free stream velocities are performed. Blade Element Momentum (BEM) method along Computational Fluid Dynamics (CFD) bases multiphase numerical simulations are used for this study. Analysis shows that the simulated parameters are in good agreement with the real conditions for each blade element during operation, except for the three-dimensional effects. The analysis of accreted ice shapes and air/droplet flow fields around the blade profile sections was carried out, and the calculation of aerodynamic performance, and energy production degradation was also performed. The tip of the blade is most affected by icing, it is characterized by the greatest changes in the aerodynamic performance. Maximum reduction in the wind turbine performance is estimated to be around 24%.

In this paper, is presented a design and control toll of electromechanical braking system dedicated to wind energy conversion chain. The braking system makes it possible to apply pressure strength at the level of the rotating shaft near to the propeller of the wind turbine. This pressure force is transformed into a dry friction force via the mechanical part of the braking system consisting of a piece of brake lining and steel of the rotating shaft of the propeller. A program of dimensioning by the analytical method of a permanent magnet linear motor structure is developed. The interactions between this program and the braking system control algorithm are taken into account. The integration of the global model under the Matlab-Simulink simulation environment allows full validation of the presented study.

This paper proposes an effective control technique for low voltage ride through (LVRT) capability in dual excited synchronous generator (DESG) wind turbines. The proposed control technique is dependent on controlling the field circuit parameters. Where the active power is controlled by the field-current space phasor magnitude and the reactive power is controlled by the field-voltage space phasor phase. With the proposed control strategy, the DESG can generate additional reactive power to support grid voltage recovery under grid faults. The DC-link voltage is kept within an acceptable limit since the excess power, due to the power mismatch between the mechanical and armature power is stored in the generator inertia. Using the proposed control strategy, the DESG can enhance the LVRT capability efficiently without using extra protection circuits or any additional control techniques during fault conditions. To test the proposed control method, simulation, and experimental results for a 1.1 kW DESG wind turbine system were obtained.

This paper proposes a novel fully sensorless synergetic algorithm that adopts concerns from both power, torque, and speed of brushless doubly fed induction machine (BDFIM) for application in wind energy conversion system (WECS). A sensorless fuzzy-based hills climb search algorithm (HCS-MPPT) is introduced to the control system along with a comparison study with the conventional tip speed ratio algorithm (TSR-MPPT). For studying the feasibility of the suggested control, a robust control of the BDFIM based on synergetic control theory is build up for the first time ever on this machine type with different scenarios where the active and reactive power, the torque, and speed of the machine are controlled. In the second step, the aim is maximizing the wind’s energy extraction by replacing the wind speed sensors with a fuzzy-based HCS-MPPT approach. Lastly, to increase the robustness of the suggested scheme control, an extended Kalman filter EKF is employed for the estimation of rotor speed in presence of considerable noise values in order to make it closer to reality as possible. Computational simulation results confirm that the proposed method, consistently outperforms other techniques and proves effectiveness under several conditions.

To verify the effectiveness of the GPU simulation of wake effects at a large-scale offshore wind farm, we ran an in-house large-eddy simulation (LES) solver with a CFD porous disk wake model for the Horns Rev 1 wind farm. For this numerical research, we prepared the latest workstation equipped with a Xeon W-2265 CPU and an NVIDIA RTX A6000 GPU. We clarified that the calculation speed of the single GPU of the NVIDIA RTX A6000 is approximately 10 times faster than the calculation speed of the Xeon W-2265. Careful data analysis and visualization of the unsteady turbulent flow fields obtained in the current LES study suggest that the mutual interference of the wakes developed by wind turbines may frequently form a local speed-up region around wind turbines, located on the downstream side of large offshore wind farms.

This paper reviews the recent advancement made in data-driven technologies based on SCADA data for improving wind turbines’ operation and maintenance activities (e.g. condition monitoring, decision support, critical components failure detections) and the challenges associated with them. Machine learning techniques applied to wind turbines’ operation and maintenance (O&M) are reviewed. The data sources, feature engineering and model selection (classification, regression) and validation are all used to categorise these data-driven models. Our findings suggest that (a) most models use 10-minute mean SCADA data, though the use of high-resolution data has shown greater advantages as compared to 10-minute mean value but comes with high computational challenges. (b) Most of SCADA data are confidential and not available in the public domain which slows down technological advancements. (c) These datasets are used for both, the classification and regression of wind turbines but are used in classification extensively. And, (d) most commonly used data-driven models are neural networks, support vector machines, probabilistic models and decision trees and each of these models has its own merits and demerits. We conclude the paper by discussing the potential areas where SCADA data-based data-driven methodologies could be used in future wind energy research.

In recent years, renewable energy generation, storage, and transmission has been the focus of research. Extraction of power from renewable energy sources is increasing rapidly. Progressively more wind farms are being fastened to the power grid. Large-scale merging of wind farms into electrical power grid presents a few challenges like voltage stability, system operation and control, and power quality due to usage of power electronic converters presenting a major bottleneck. This paper elucidates various types of wind power plant technologies, consequences of power electronic converters and reviews various types of system strength determination methods.

Increased penetration of renewable energy sources (RESs) in power system networks poses several challenges in system planning and management due to their uncertain and non-dispatchable nature. Consequently, this paper presents a thorough and precise review of recent solution methodologies for solving the optimal power flow (OPF) problems incorporated with stochastic RESs based on multiple peer-reviewed research publications in reputed journals. The Teaching Learning Based Optimization algorithm has been discussed and implemented to solve the OPF problem considering solar photovoltaic, wind turbine, and tidal energy systems. Weibull, Lognormal, and Gumbel probability density functions representing the uncertainty associated with the availability of wind speed, solar irradiance, and tidal energy systems, respectively. The results obtained from the proposed technique validate its novelty regarding OPF problems like minimization of operating cost, power loss in transmission lines, enhancement of voltage profile, and voltage stability. The proposed solution technique for OPF problems is tested on a modified IEEE 30-bus test system. Thus, this study assists in understanding the OPF problem for new researchers concerned with this domain and also gives the idea of implementing nature-inspired optimization algorithms on a defined test system to solve the OPF problem.

The Betz’s limit for the maximum efficiency of an ideal wind turbine imposes a maximum value of about 60% on the conversion of the kinetic energy of an airflow into work. In this paper, we analyze the reason for this value because, from a thermodynamic point of view, it can be 100%. The present work explains the reason for this difference, since it appears to be relevant from a didactic point of view. However, from a practical point of view, the Betz’s limit does not affect in any way the more useful and widespread expression for calculating the ideal maximum power of a wind turbine, which is at the origin of the referred limit. Complementarily, two approaches for the calculation of the theoretical maximum efficiency, in line with thermodynamics, are also presented in this work.