Wind Turbine Research Articles

Controlling the energies of the single-rotor large wind turbine system using a new controller.

In wind energy generation systems, ensuring high energy quality is critical but is often compromised due to the limited performance and durability of conventional regulators. To address this, this work presents a novel controller for managing the machine-side inverter of a single-rotor large wind turbine system using an induction machine-type generator. The proposed controller is designed using proportional, integral, and derivative error-based mechanisms, which fundamentally differ from traditional proportional-integral (PI) regulators. Key features of the proposed regulator include its simplicity, cost-effectiveness, ease of implementation, reduced number of gains, and rapid dynamic response.This regulator enhances the direct power control (DPC) approach, as it integrates two tailored controllers alongside a pulse width modulation strategy to manage the machine inverter. The DPC strategy incorporating the proposed controller was implemented and tested using MATLAB, with various simulations to evaluate its performance and effectiveness. The proposed regulator demonstrated a significant improvement over the PI regulator, with reductions in active power ripples of 69%, 61.70%, and 59.14% across different tests. Additionally, the steady-state error of reactive power was reduced by 54.84%, 85.23%, and 62.68%, and the total harmonic distortion of current decreased by 48.12%, 50.55%, and 56.05%. These results underscore the high efficiency, robustness, and effectiveness of the proposed controller in improving system performance compared to conventional PI regulators. The controller's outstanding performance makes it a promising solution for broader industrial applications.

A Combined High and Low Cycle Fatigue Life Prediction Model for Wind Turbine Blades

A Combined High and Low Cycle Fatigue Life Prediction Model for Wind Turbine Blades

Model predictive control for risk-based inspection and maintenance planning of offshore wind turbines

ABSTRACT Offshore wind turbines (OWTs) are highly susceptible to fatigue deterioration under extreme environmental conditions, making optimal inspection and maintenance (I&M) planning critical for their safe and sustainable operation. The dynamic nature of weather and complex deterioration processes challenge the long-term effectiveness of maintenance strategies. Moreover, I&M policies often become outdated when deterioration models are updated. Unlike traditional approaches that rely on a fixed life-span horizon, effective I&M planning must adapt to evolving conditions and varying planning horizons. This paper introduces a model predictive control (MPC) method for adaptive, risk-based I&M planning of OWTs. The closed-loop MPC approach provides flexibility by dynamically adjusting inspection and maintenance strategies based on evolving fatigue risks. We propose an augmented state-space model that captures the impact of probabilistic I&M actions on fatigue failure risks over time. A finite-horizon optimal control problem is then formulated to derive the MPC controller, which balances fatigue risk and I&M costs over the planning horizon. The proposed method is demonstrated using a fatigue-prone OWT component. Results show that the MPC controller effectively manages the trade-off between fatigue risk and maintenance costs while remaining adaptable to different planning horizons and decision-making preferences.

Study on the Effect of Mixed-Phase Icing on the Aerodynamic Characteristics of Wind Turbine Airfoil

Study on the Effect of Mixed-Phase Icing on the Aerodynamic Characteristics of Wind Turbine Airfoil

Recovery of rare earths from end-of-life NdFeB permanent magnets from wind turbines.

This work aims to recover rare earths from wind turbines NdFeB magnets through pyrometallurgical and hydrometallurgical techniques. First, a NdFeB hydride powder is obtained by decrepitation with hydrogen. Subsequently, this powder was subjected to a chlorination roasting process and successive leaching with water to bring the metals into solution. This was followed by a liquid-liquid extraction to remove the iron and purify the rare earth solution. For this purpose, Aliquat 336 diluted in Solvesso was selected as the iron selective extraction agent. As a single extraction was not enough for complete iron removal, a second Fe extraction step was carried out. This second extraction step was performed using the restored organic phase. This restoration was achieved by treating the organic phase with Na2SO3 and then washing it with a 3M HCl solution. In this way, the process was achieved more sustainably. Finally, the rare earths contained in the final solution were precipitated using oxalic acid to obtain mixed rare earth oxalates.

Geometric description of a gliding grey-headed albatross (Thalassarche chrysostoma) for computer-aided design.

Albatrosses are increasingly drawing attention from the scientific community due to their remarkable flight capabilities. Recent studies suggest that grey-headed albatrosses may be the fastest and most energy-efficient of the albatross species, yet no attempts have been made to replicate their wing design. A key factor in aircraft design is the airfoil, which remains uncharacterized for the grey-headed albatross. Other critical aspects, such as wing twist and dihedral/anhedral, also remain unquantified for any albatross species. This study aimed to fill this gap in the current knowledge by extracting detailed morphological data from a grey-headed albatross wing to recreate digitally. A well-preserved dried grey-headed albatross wing was scanned in the presence of airflow in a wind tunnel, at conditions that represent a grey-headed albatross in gliding flight. Wing cross-sections were extracted and smoothed to produce a series of airfoils along the wing span. The 3D properties such as wing dihedral/anhedral, sweep and twist were also extracted and used to build a CAD model of the wing. Variations in airfoil shape were observed along the wing span, with thicker, more cambered airfoils near the wing base. The model wing's camber was slightly higher, particularly in the arm section, but overall matched flight photographs. The body, tail, and bill were modelled based on available photographs and known dimensions from literature and merged with the wing to form the final bill-body-wing-tail model. This model is based on real grey-headed albatross morphology under aerodynamic pressure, in gliding flight. Although geometric changes due to scanner interference remain a limitation of this method, the extracted geometric data still provide valuable insights into wing performance under varying conditions. The geometry can also be fully parameterized for complex simulations, aiding studies of grey-headed albatross aerodynamics and engineering design, such as in aircraft or wind turbines at similar Reynolds numbers.

Numerical Simulation of Ice Crystal Accretion and Aerodynamic Impacts on Wind Turbine Blades in Cold Climates

Numerical Simulation of Ice Crystal Accretion and Aerodynamic Impacts on Wind Turbine Blades in Cold Climates

A Review of Experiment Methods, Simulation Approaches and Wake Characteristics of Floating Offshore Wind Turbines

A Review of Experiment Methods, Simulation Approaches and Wake Characteristics of Floating Offshore Wind Turbines

Experimental and Numerical Analysis of Carbon/Glass Fiber Reinforced Thermoplastic Wind Turbine Blade Prepared by VARIM

Experimental and Numerical Analysis of Carbon/Glass Fiber Reinforced Thermoplastic Wind Turbine Blade Prepared by VARIM

Typhoon Eye-Induced Misalignment Effects on the Serviceability of Floating Offshore Wind Turbines: Insights Typhoon SOULIK

Typhoon Eye-Induced Misalignment Effects on the Serviceability of Floating Offshore Wind Turbines: Insights Typhoon SOULIK

Eoldist, a Web Application for Estimating Cautionary Detection Distance of Birds by Automatic Detection Systems to Reduce Collisions With Wind Turbines

<i>Eoldist</i>, a Web Application for Estimating Cautionary Detection Distance of Birds by Automatic Detection Systems to Reduce Collisions With Wind Turbines

Waste Management of Wind Turbine Blades—A Review of Recycling Methods and Applications in Cementitious Composites

Waste Management of Wind Turbine Blades—A Review of Recycling Methods and Applications in Cementitious Composites

Dual Input Interleaved Three-Phase Rectifier In Discontinuous Conduction Mode For Application In Small Wind Turbines

Dual Input Interleaved Three-Phase Rectifier In Discontinuous Conduction Mode For Application In Small Wind Turbines

Research and Application of Microwave Microstrip Transmission Line-Based Icing Detection Methods for Wind Turbine Blades

Research and Application of Microwave Microstrip Transmission Line-Based Icing Detection Methods for Wind Turbine Blades

Treatment and Valorization of Waste Wind Turbines: Component Identification and Analysis.

Recycling end-of-life wind turbines poses a significant challenge due to the increasing number of turbines going out of use. After many years of operation, turbines lose their functional properties, generating a substantial amount of composite waste that requires efficient and environmentally friendly processing methods. Wind turbine blades, in particular, are a problematic component in the recycling process due to their complex material composition. They are primarily made of composites containing glass and carbon fibers embedded in polymer matrices such as epoxies and polyester resins. This study presents an innovative approach to analyzing and valorizing these composite wastes. The research methodology incorporates integrated processing and analysis techniques, including mechanical waste treatment using a novel compression milling process, instead of traditional knife mills, which reduces wear on the milling tools. Based on the differences in the structure and colors of the materials, 15 different kinds of samples named WT1-WT15 were distinguished from crushed wind turbines, enabling a detailed analysis of their physicochemical properties and the identification of the constituent components. Fourier transform infrared spectroscopy (FTIR) identified key functional groups, confirming the presence of thermoplastic polymers (PET, PE, and PP), epoxy and polyester resins, wood, and fillers such as glass fibers. Thermogravimetric analysis (TGA) provided insights into thermal stability, degradation behavior, and the heterogeneity of the samples, indicating a mix of organic and inorganic constituents. Differential scanning calorimetry (DSC) further characterized phase transitions in polymers, revealing variations in thermal properties among samples. The fractionation process was carried out using both wet and dry methods, allowing for a more effective separation of components. Based on the wet separation process, three fractions-GF1, GF2, and GF3-along with other components were obtained. For instance, in the case of the GF1 < 40 µm fraction, thermogravimetric analysis (TGA) revealed that the residual mass is as high as 89.7%, indicating a predominance of glass fibers. This result highlights the effectiveness of the proposed methods in facilitating the efficient recovery of high-value materials.

ANALYSIS OF LOAD ON PERMANENT MAGNET SYNCHRONOUS GENERATOR (PMSG) 12S8P WITH SPEED VARIATION

Abstract. This study investigates the impact of speed variation on the performance of a Permanent Magnet Synchronous Generator (PMSG) configured with 12 slots and 8 poles (12S8P) for wind energy conversion systems. PMSG has gained attention due to its high efficiency, simple design, and ability to operate effectively at low speeds, making it particularly suitable for renewable energy applications like wind turbines. Using MagNet simulation software, the performance of the PMSG was analyzed over a speed range of 1000–6000 rpm and load variations of 10–50 ohms. The results showed a maximum magnetic flux of 0.000927 Wb, while the no-load average voltage was 17.78 V at a speed of 1000 rpm. Furthermore, it was observed that voltage and current increased proportionally with speed. Optimal torque performance was achieved at a 30-ohm load at a speed of 5000 rpm, highlighting the importance of load optimization for effective energy conversion. Efficiency analysis revealed that the generator achieved its highest efficiency of 92.96% at a 10-ohm load and a speed of 5000 rpm. This study also emphasizes the influence of material properties, slot-pole design, and load conditions on generator performance. These findings provide critical insights for the design and optimization of wind turbines, particularly in regions with varying wind speeds. Moreover, this research contributes to advancing the utilization of renewable energy technologies by offering data-driven strategies for enhancing system efficiency and reliability..

Optimizing FACTS Device Placement Using the Fata Morgana Algorithm: A Cost and Power Loss Minimization Approach in Uncertain Load Scenario-Based Systems

Today, reliable power delivery and increasing demand are important issues in modern power systems. Flexible Alternating Current Transmission Systems (FACTS) devices are used to control transmission line parameters to increase power transfer and stability. Nevertheless, the problem of determining the optimal placement and sizing of these devices is still challenging, as the placement and sizing of the devices affects generation costs, power losses, voltage stability, and system reliability. This study proposes the Fata Morgana Algorithm (FATA), an optimization algorithm inspired by the natural process of mirage formation to optimize placement and sizing of FACTS devices in an IEEE 30 bus system with wind turbine integration. The FATA algorithm is evaluated against recently developed and improved optimization techniques, such as rime-ice formation phenomenon based Improved RIME (IRIME) Algorithm, Newton–Raphson-Based Optimization (NRBO), Resistance Capacitance Algorithm (RCA), Krill Optimization Algorithm (KOA), and Grey Wolf Optimizer (GWO), across multiple optimization objectives: reduction in generation cost, reduction in power loss and combined generation cost plus power loss, termed as Gross cost function. Results obtained show that FATA consistently outperforms the other algorithms in terms of convergence and solution quality, offering a robust approach to solving single objective optimization problems. FATA theoretically provides a good balance between exploration and exploitation, and produces better global solutions. It practically increases power system efficiency by lowering operational costs and losses and improving stability. Results indicate that the FATA algorithm produced minimum generation cost of 807.0405 $/h, which is 0.088–0.426% less than the competing algorithms. It also reduced power losses to 5.5917 MW, which is 1.095–6.781% less than other methods. For gross cost minimization, the FATA algorithm achieved a minimum gross cost of 1366.3727 $/h, which is 0.4799% better than the next best algorithm and 3.2261% better than the worst. The results also show that FATA is robust in solving complex optimization problems in power systems, and it provides significant improvements in run time and convergence efficiency. The main advantage for readers is that FATA provides a reliable and efficient way to optimize power systems. Future work could also investigate the application of FATA in real time, as well as in larger power networks with more renewable energy sources.

Estimation of Wind Farm Losses Using a Jensen Model Based on Actual Wind Turbine Characteristics for an Offshore Wind Farm in the Baltic Sea

Estimation of Wind Farm Losses Using a Jensen Model Based on Actual Wind Turbine Characteristics for an Offshore Wind Farm in the Baltic Sea

Tidal Current with Sediment Transport Analysis and Wind Turbine Foundation Pile Scour Trend Studies on the Central Bohai Sea

Tidal Current with Sediment Transport Analysis and Wind Turbine Foundation Pile Scour Trend Studies on the Central Bohai Sea

Multi-objective optimization of hybrid energy systems using gravitational search algorithm

The depletion of fossil fuel reserves, increasing environmental concerns, and energy demands of remote communities have increased the acceptance of using hybrid renewable energy systems (HRES). However, choosing an optimal HRES from economic, environmental, reliability, and sustainability aspects is still challenging. To solve this challenge, this study introduces a novel multi-objective optimization approach using the Gravitational Search Algorithm (GSA) and non-dominated sorting techniques. The proposed framework addresses four objectives: minimizing the loss of power supply probability, reducing total costs, increasing renewable energy fraction, and lowering CO2 emissions. A carbon tax sensitivity analysis evaluates the system’s economic performance under varying scenarios. Also, the amount of damage caused by the release of carbon dioxide on human health and the ecosystem is examined. In this way, an optimal configuration consisting of wind turbines, photovoltaic panels, and diesel generators is introduced to satisfy the above objectives. Results demonstrate that the GSA outperforms established methods, such as multi-objective particle swarm optimization and non-dominated sorting genetic algorithm II in Pareto front diversity and convergence. In this work, the optimal system achieves an 18.4% increase in renewable energy share, reducing ecosystem and human health damage by 14.2%. Notably, with a 20% increase in the carbon tax, system costs increased by 3%. These findings underscore the potential of multi-objective optimization combined with carbon tax policies to enhance energy system sustainability and affordability.