Techniques to Increase Wind Turbine Efficiency

Purpose This study aims to optimize control systems for offshore wind turbine technology improvement through advanced electricity generation capabilities. A new pitch angle system design emerges solve the simultaneous issues of power optimization with reduced structural wear in systems. Design/methodology/approach The implements three vital elements: a stable baseline controller that maintains stability, predictive foresight and reference value preassessment tool improves response accuracy. mechanism continuously evaluates performance enables proactive strategy adjustments. proposed is implemented on 6.8 MW turbine. Findings approach demonstrates 14.8% increase average output 10.9% reduction fatigue load, based comprehensive tests compared robust methods operation. exhibits practical usability this algorithm during experimental testing, which enhances operational as well life duration across different conditions. Research limitations/implications Analysis shows using increased systems, reducing loading effects by 10.9%, supports performance. combined implementation multiple strategie... Read More

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Wind turbine gearboxes are critical components in wind energy systems but highly susceptible to mechanical failures due high loads, variable operating conditions, and material fatigue. This study presents an integrated approach utilizing condition monitoring (CMS), predictive maintenance algorithms, finite element analysis (FEA) enhance gearbox reliability. By analyzing stress distribution, vibration, temperature trends, we establish a robust methodology for early fault detection. Our results show that artificial intelligence (AI)-based can reduce unplanned by 40% increase overall efficiency 20%. provides insights into optimizing operation through data-driven strategies.

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A rotor design for permanent magnet electrical machines like wind turbine generators that eliminates the need for expensive welding and baseplates while reducing losses and temperature. The rotor has a stacked lamination structure with permanent magnets attached to the inner side. The laminations provide a sealed rotor body without needing machining on the inside or outside. Tensioning bolts hold the stacked laminations together. This allows direct attachment of the magnets to the rotor without additional baseplates. The laminated rotor can have cooling protrusions on the outer side for improved cooling.

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Optimizing power generation from clusters of fluid turbines like wind turbines by coordinating their operations under variable fluid conditions. Techniques include adjusting turbine orientations to mitigate interference, synchronizing rotational speeds, and using maximum power point tracking (MPPT) algorithms to extract maximum power from low fluid flows. This coordinated control can increase aggregate power production compared to uncoordinated turbines.

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Hydraulic energy conversion system for wind turbines that replaces the mechanical gearbox. The system uses a radial piston hydraulic pump with a multi-lobe concentric cam that produces multiple strokes per revolution. The pump has hydraulically connected cylinders, variable stroke capability, and an integrated reservoir and pressure tank. This allows tailoring the hydraulic drive to wind speeds and replacing the gearbox with a more reliable and efficient solution.

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Optimizing pitch angle of wind turbine blades to improve power generation without costly blade modifications or extensive measurements. The method involves finding the optimal pitch angle for each wind speed condition by using current sensor data and forecasts. When certain conditions are met, like stable wind and low variability, the turbine adjusts the blade pitch angles for that wind speed range. This adaptive calibration compensates for blade wear and tolerances without requiring precise blade specifications.

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Controlling blade pitch of a wind turbine during grid outages to reduce power consumption of the pitch system. The method involves using a less responsive pitch control mode during standby when connected to the grid is lost. This reduces power consumption compared to normal mode. The less responsive mode allows blade pitch adjustment but with reduced sensitivity. If needed, the normal mode is reactivated to fully control blade pitch.

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This study explores the influence of overlap ratio on performance two- and three-bladed Savonius hydrokinetic turbines to identify optimal configuration for enhanced efficiency. Utilizing computational fluid dynamics simulations in ANSYS Fluent, six distinct blade profiles were analysed under an inlet velocity 0.5 m/s, considering cases with without ratio. The employed unsteady sliding mesh technique accurate flow assessment. Results demonstrated that a 0.15 exhibited superior compared designs ratio, achieving maximum power coefficient 0.2 at tip speed 0.9. These findings underscore critical role geometric optimization, particularly enhancing efficiency turbines, thereby advancing their potential reliable effective energy generation.

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Dynamic modelling of wind turbines and their simulation is a very useful tool for studying behaviour. One the key elements concerning physical models power coefficient Cp, which acts as an efficiency in extraction from wind. Unfortunately, this often unknown priori, it does not usually appear information provided by manufacturers. This paper first describes methodology obtaining parameters commercial turbine model using curve manufacturer, indicates theoretical that can produce at each speed. To achieve this, parameter estimation problem formulated solved to determine parameters. Nevertheless, insufficient, requiring additional knowledge, such operational data, improve fit. Finally, new performed only real data measured process, validating proposed methodology. The dataset was obtained SMARTEOLE project France corresponds pitch-controlled variable-speed Senvion MM82/2050 turbine.

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The increasing integration of renewable energy, electric vehicles, and industrial applications demands efficient power converter control strategies that reduce switching losses while maintaining high waveform quality. This paper presents a Finite-Control-Set Model Predictive Control (FCS-MPC) strategy for three-phase, two-level voltage source inverters (VSIs), incorporating secondary objective frequency minimization. Unlike conventional MPC approaches, the proposed method optimally balances performance efficiency trade-offs by adjusting weighting factor (min). Real-time implementation using OPAL-RT platform validates effectiveness approach under both linear non-linear load conditions. Results demonstrate significant reduction in losses, accompanied improved tracking; however, distortion are observed scenarios. These findings provide insights into practical real-time predictive high-performance converters.

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Method for optimizing wind farm performance by coordinating wind turbine control in a wind farm. The method involves commanding an upstream wind turbine in a wind farm to steer its wake away from a downstream turbine, but only if load levels on the downstream turbine are below a threshold. This prevents excessive loads on the downstream turbine that can lead to maintenance issues. The threshold is adjusted based on demand to balance load reduction vs power loss. The upstream turbine can also perform induction control to weaken wakes, but this is separate from wake steering.

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This study explores optimal control strategies for integrating renewable energy sources into power grids, addressing challenges such as intermittency, grid stability, and demand-supply mismatch. The methodology combines model predictive control, robust optimization, load forecasting using machine learning algorithms. Data from solar wind outputs, along with historical demand, were analyzed to build regression models. results demonstrate that adaptive significantly enhance efficiency, reduce operational costs, improve reliability. Findings offer insights designing intelligent systems can dynamically respond fluctuating inputs while maintaining stability. Keywords: Renewable energy, integration, learning, efficiency

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Active wake control (AWC) has emerged as a promising strategy for enhancing wind turbine recovery, but accurately modelling its underlying fluid mechanisms remains challenging. This study presents computationally efficient model that provides end-to-end prediction capability from rotor actuation to recovery enhancement by capturing the coupled dynamics of meandering and mean flow modification, requiring only two inputs: reference without user-defined AWC strategy. The combines physics-based resolvent large-scale coherent structures an eddy viscosity small-scale turbulence. A Reynolds stress is introduced account influence both incoherent fluctuations, so time-averaged enhanced can be quantitatively predicted. Validation against large-eddy simulations (LES) across various approaches actuating frequencies demonstrates models predictive capability, AWC-specific frequency-dependent with less than 8 % error LES while reducing computational time thousands central-processing-unit hours minutes. efficiency accuracy makes it tool practical design optimization farms.

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Controlling wind turbines using a predictive controller that improves load handling and avoids component failures by accurately modeling loads and constraints. The method involves using a wind turbine model with a predictive aeroelastic module to estimate future loads based on current operating data. A strength calculation module calculates secondary load parameters from the primary loads. Optimization over a finite period subjects to constraints on the secondary loads. Commands are generated to control the wind turbine actuators. This allows optimal actions to capture more power while avoiding overloads. The predictive modeling allows accounting for true limiting factors rather than conservative assumptions.

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Optimizing power output of wind farms with multiple wind turbines by dynamically adjusting the yaw angle of upwind turbines to mitigate wake effects on downwind turbines. The yaw steer is determined based on a net energy gain calculation that balances increased power from reduced wake with the cost of yaw adjustment. Clustering of turbines affected by wake is ranked based on net energy gain and only the best clusters receive yaw steers above a threshold. This prevents excessive yaw adjustment wear while maximizing overall farm power.

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The increasing integration of wind turbines into the power grid has reduced system frequency stability, necessitating energy storage systems in primary regulation. This paper proposes an MPC-based control method to optimize response a combined windstorage system. An evaluation is also developed characterize stability and guide dispatch. First, model state-space equations for MPC are established. Then, strategy proposed achieve objective minimizing variation deviation. Finally, assessed using MATLAB/Simulink case studies confirm effectiveness enhancing regulation performance. results show that this not only accelerates speed but reduces its fluctuations, thereby improving

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In this work, the optimization of wind turbines is considered by introducing a cylindrical blade with an activedeflector. The use metal (aluminum) deflector, compared plastic (polypropylene), significantly increasedthe aerodynamic efficiency blade. It shown that aluminum deflector reduces dragforce 1820 % and increases lifting force 2.7 times. maximum reached 2.16 N at awind speed 15 m/s deflector. addition, achieveda higher rotation up to 1100 rpm, which 10 polypropylenedeflector. improved performance due high rigidity minimal deformation aluminummaterial under influence air flow. active eliminates need for additionaltriggers, simplifying design reducing operating costs. results obtained indicate useof Magnus contributes developmentof renewable energy technologies.

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Distributed wind farm control using consensus estimates of local wind conditions to improve performance and resiliency. The technique involves having wind turbines in a farm communicate their measured wind data to nearby turbines. By comparing and averaging the local wind estimates, a consensus wind estimate is determined for each turbine. This consensus wind is then used as input to the turbine's own controller instead of its local measurement. It allows leveraging nearby turbine data to make better wind direction estimates and reduce unnecessary yaw motions.

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Adaptive wind farm control that optimizes power production and grid stability by dynamically adjusting the wind turbine control strategies based on the wind farm conditions. The control system has a wind farm state input to receive data like number of turbines, wind speeds, availability, etc. This farm state is used to modify the wind turbine controller parameters and structure to better suit the current operating conditions. This allows the turbines to adapt their control settings for optimal performance when factors like turbine count, wind variability, and grid stability change.

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Estimating when a component of a wind turbine needs replacement based on the remaining producible energy until failure rather than just remaining useful lifetime. This provides a more useful metric for optimizing turbine operation and maintenance scheduling because it relates directly to power yield. The method involves estimating the remaining energy until component replacement using machine learning with trained neural networks. The neural network is trained using data from multiple turbines to predict the remaining energy based on operational and environmental parameters. If the estimated remaining energy is below a threshold, it indicates the component needs replacement.

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