Commercial Wind Turbine Research Articles

Novel Fractional Order Differential and Integral Models for Wind Turbine Power–Velocity Characteristics

This work presents an improved modelling approach for wind turbine power curves (WTPCs) using fractional differential equations (FDE). Nine novel FDE-based models are presented for mathematically modelling commercial wind turbine modules’ power–velocity (P-V) characteristics. These models utilize Weibull and Gamma probability density functions to estimate the capacity factor (CF), where accuracy is measured using relative error (RE). Comparative analysis is performed for the WTPC mathematical models with a varying order of differentiation (α) from 0.5 to 1.5, utilizing the manufacturer data for 36 wind turbines with capacities ranging from 150 to 3400 kW. The shortcomings of conventional mathematical models in various meteorological scenarios can be overcome by applying the Riemann–Liouville fractional integral instead of the classical integer-order integrals. By altering the sequence of differentiation and comparing accuracy, the suggested model uses fractional derivatives to increase flexibility. By contrasting the model output with actual data obtained from the wind turbine datasheet and the historical data of a specific location, the models are validated. Their accuracy is assessed using the correlation coefficient (R) and the Mean Absolute Percentage Error (MAPE). The results demonstrate that the exponential model at α=0.9 gives the best accuracy of WTPCs, while the original linear model was the least accurate.

Machine Learning and Cointegration for Wind Turbine Monitoring and Fault Detection: From a Comparative Study to a Combined Approach

Data-driven models have become powerful tools for structural and condition monitoring of engineering systems, particularly wind turbines. This paper presents a comparative analysis of common machine learning (ML) algorithms (artificial neural networks, linear regression, random forests, and gradient boosting) and a cointegration-based approach for fault detection using Supervisory Control and Data Acquisition (SCADA) data. While ML models offer early fault prediction, the cointegration method is simpler, requires less training data, and has lower computational costs. However, it is less effective for early detection. To balance these trade-offs, we propose a cascading monitoring framework, where the ML model provides long-term predictions (outer monitoring process) and the cointegration model offers short-term verification (inner monitoring process). The cointegration model serves to confirm anomalies flagged by the ML model. By combining both models in a cascade structure, the system reduces the risk of false alarms triggered by uncertainties in the ML model alone. Furthermore, the short-term cointegration-based prediction model helps pinpoint immediate risks and mitigate the issue of prolonged downtime. This combination enhances both accuracy and reliability, as demonstrated through testing on a five-year SCADA dataset from a commercial wind turbine with a known gearbox fault.

Wind energy and hydrogen production: a new approach for green and sustainable greenhouse agriculture in Morocco

In response to the critical current energy demands and the imperative transition to green and sustainable agriculture, this study introduces an innovative approach by integrating wind energy into greenhouse agriculture. The sites of Dakhla, Agadir, Essaouira, and Kenitra, known for their intensive agricultural activities, were selected for a comprehensive evaluation. Utilising the Weibull distribution, a detailed wind potential analysis was conducted. The study's novelty lies in its combined technico-economic and environmental analysis, applied to four commercial wind turbines ranging from 1 kW to 10 kW. The findings reveal that Dakhla and Essaouira possess significant wind potential, sufficient to meet the electricity demands of agricultural greenhouses either partially or entirely. Specifically, the power densities recorded were 150.03 W/m2 and 62.29 W/m2 for Dakhla and Essaouira, respectively. The areas covered were 2 919.59 m2 and 1 120.26 m2, with a payback period of 1 year for Dakhla and 4.66 years for Essaouira using the T4 turbine. Additionally, the study explores the potential of integrating a wind/hydrogen conversion system, tested on a 100 kW turbine, which produced 6 648 kg of hydrogen in Dakhla and 4 443 kg in Essaouira. This approach allows for varied hydrogen uses, supporting sustainable agriculture.

Strategic analysis of wind energy potential and optimal turbine selection in Al-Jouf, Saudi Arabia

Wind power is considered one of the most environmentally friendly and rapidly growing form of renewable energy. This study aims at assessment of wind power potential for Al-Jouf region in Saudi Arabia. A long term historical wind speed data of 21 years (2000–2021) was analytically modeled using Weibull distribution function to determine the wind characteristics. The Weibull parameters and average wind speed for monthly and yearly were evaluated using MATLAB program. An online wind power calculator developed by Meteotest was used to evaluate the wind energy by selecting various turbines. Six types of commercial wind turbines of 3 MW capacity were selected for wind power assessment to optimize the turbine selection. Our analysis showed that average wind speed varies between 3.88 m/s and 4.99 m/s and an overall average wind speed is 4.38 m/s. The most frequent wind speed observed was 3.9 m/s with a probability of 20 % approximately. The Weibull distribution parameters, shape parameter “k” values ranged between 1.89 and 2.21 with an average of 1.98 and scale factor “c” values varied between 4.41 and 5.66 m/s with a mean value of 4.86 m/s. Performance evaluations of selected wind turbine models reveal that the Vestas V126 turbine outperforms others at the Al-Jouf site, generating an annual energy yield of 3779400 kWh at a capacity factor of 14.4 %. These results suggest that by constructing a wind farm consisting of 100 V126 turbines may compensate for the energy needs of 46000 individuals. These findings establish Al-Jouf as a viable location for utility-scale wind power projects, offering valuable insights for turbine manufacturers, developers, operators, and policymakers interested in deploying large-capacity turbines in the region. The method used in this study for wind power analysis and turbine selection is simple and helpful to provide an initial assessment about the site suitability and turbine selection without undergoing exhaustive efforts.

The Characteristics and Variation of the Golden Eagle Aquila chrysaetos Home Range

Satellite tracking allows for novel investigations into golden eagle home range characteristics. Understanding home range characteristics is important for conservation and for assessing the potential impact of landscape changes from forest planting, wind farms, etc. Small sample sizes, inconsistent definitions and methods restricted several previous studies. Our study involved 69 resident tagged eagles with over one year of data across five Scottish regions. Home range size was estimated from 95% isopleth contours extracted from Utilisation Distributions. Above a small threshold, estimated range size was not affected by the number of records but at least one year of data is required, largely because of the breeding and non-breeding seasonal differences. There were no significant range size differences between birds tagged as range holders and those previously tagged as nestlings. Across four regions, with considerable intra-regional variation, planar 95% isopleths did not differ (medians, km2): Argyll 58.9, Northwest Highlands 61.7, Northeast Highlands 89.3, South of Scotland 91.9. Ranges in the isolated Outer Hebrides region were exceptionally small, at 24.0 km2. Estimated range area was usually reduced to 70–80% of the planar area when restricted to usable habitat, as estimated by the Golden Eagle Topography (GET) model. Applying measures of known unsuitable habitat (closed-canopy commercial forest and wind turbines) further reduced usable open land. Loss of otherwise suitable habitat was substantially due to commercial forest. Larger ranges had larger extents of suitable habitat (according to GET), with no apparent optimum of preferred GET habitat. Range size was not different across a year between the sexes. Breeding ranges were smaller, and females’ breeding ranges were much smaller than those of males, but larger than males’ ranges in the non-breeding season. Breeding attempt duration was probably also influential. Our study provides novel insights into golden eagle home range characteristics and can guide further research and practical applications.

Identification and Localization of Wind Turbine Blade Faults Using Deep Learning

This study addresses the challenges inherent in the maintenance and inspection of wind turbines through the application of deep learning methodologies for fault detection on Wind Turbine Blades (WTBs). Specifically, this research focuses on defect detection on the blades of small-scale WTBs due to the unavailability of commercial wind turbines. This research compared popular object localization architectures, YOLO and Mask R-CNN, to identify the most effective model to detect common WTB defects, including cracks, holes, and erosion. YOLOv9 C emerged as the most effective model, with the highest scores of mAP50 and mAP50-95 of 0.849 and 0.539, respectively. Modifications to Mask R-CNN, specifically integrating a ResNet18-FPN network, reduced computational complexity by 32 layers and achieved a mAP50 of 0.8415. The findings highlight the potential of deep learning and computer vision in improving WTB fault analysis and inspection.

Assessment of wind energy resource in the western region of Somaliland

As the population of Somaliland continues to grow rapidly, the demand for electricity is anticipated to rise exponentially over the next few decades. The provision of reliable and cost-effective electricity service is at the core of the economic and social development of Somaliland. Wind energy might offer a sustainable solution to the exceptionally high electricity prices. In this study, a techno-economic assessment of the wind energy potential in some parts of the western region of Somaliland is performed. Measured data of wind speed and wind direction for three sites around the capital city of Hargeisa are utilized to characterize the resource using Weibull distribution functions. Technical and economic performances of several commercial wind turbines are examined. Out of the three sites, Xumba Weyne stands out as the most favorable site for wind energy harnessing with average annual power and energy densities at 80 m hub height of 317 kW/m2 and 2782 kWh/m2, respectively. Wind turbines installed in Xumba Weyne yielded the lowest levelized cost of electricity (LCOE) of not more than 0.07 $/kWh, shortest payback times (i.e., less than 7.2 years) with minimum return on investment (ROI) of approximately 150%.

Observer based pitch control for load mitigation and power regulation of floating offshore wind turbines

Abstract Most commercial wind turbines use proportional-integral (PI) collective blade-pitch control to regulate rotor speed in the above-rated wind speed regime. A significant drawback of this type of controller is that it assumes that the blades have identical structural properties and are subject to similar aerodynamic loads, which is seldom the case. Also, these controllers are designed to regulate the rotor speed and are not designed for structural vibration/load reduction. However, it is well known that blade pitch control can reduce structural loads on wind turbines. This opens up the possibility of designing controllers that use existing actuators and sensors like the blade pitch actuators to reduce structural loads/vibrations while maintaining the required rotor speed. Recent studies have investigated individual blade pitch control (IPC) to address these shortcomings. However, the vast majority of studies published in the literature depend on the availability of state measurement. Although sensors are commonly placed on all wind turbines, and some information is readily available, the measurement required by the typical state-feedback controllers is usually not available. Displacements and velocities of the blade, the tower and the floating platform are difficult to measure. This paper develops an observer-based individual blade pitch controller for load mitigation and power regulation of floating offshore wind turbines. We propose to use a Kalman filter to estimate the state from the accelerometer and strain gauge measurement for use in the state-feedback controller. The state-feedback controller was proposed previously by the authors that showed excellent performance. This paper extends the capability of the state-feedback controller by designing an observer (Kalman filter) to estimate the state from limited measurements. The proposed observer based controller is compared against a baseline proportional integral collective blade pitch controller and full state-feedback controllers to evaluate its performance. Numerical results show that the proposed output feedback controller offers performance improvements over the baseline controller, similar to the full state-feedback controller.

Investigation of Nonlinear Effects in the Tower Structure of Wind Turbines and Their Influence on System Behavior

In multibody simulation (MBS) software, large structural components such as the tower are often modeled as flexible bodies using the Craig-Bampton method. While being very computationally efficient, nonlinear effects cannot be modelled in this way. The flange connections between the segments of tubular steel towers are one such source of nonlinearity. The magnitude of this nonlinearity increases with damages at the flanges as flange gaping becomes more common. The nonlinearities in the flanges lead to a changing stiffness and hence changing eigenfrequencies of the structure as a function of its deformation. In this paper, a nonlinear model of a tower with and without damages is being developed and validated based on an FEM model. The model is assembled from linear flexible bodies linked with nonlinear rotatory springs. The damages considered are broken bolts as well as axially symmetric angular flange gaps. It is shown that the MBS model closely emulates the FEM model including all nonlinear effects. The nonlinear tower model is incorporated into an existing MBS model of a commercial wind turbine in the 4 MW range. The eigenfrequencies of the full turbine model are being calculated using the build-in eigenvalue solver in Simpack. The results show a small but measurable decrease in eigenfrequencies compared to the linearized model, which scales with the extend of the damage.

Computer Vision-Based Path Planning with Indoor Low-Cost Autonomous Drones: An Educational Surrogate Project for Autonomous Wind Farm Navigation

The application of computer vision in conjunction with GPS is essential for autonomous wind turbine inspection, particularly when the drone navigates through a wind farm to detect the turbine of interest. Although drones for such inspections use GPS, our study only focuses on the computer vision aspect of navigation that can be combined with GPS information for better navigation in a wind farm. Here, we employ an affordable, non-GPS-equipped drone within an indoor setting to serve educational needs, enhancing its accessibility. To address navigation without GPS, our solution leverages visual data captured by the drone’s front-facing and bottom-facing cameras. We utilize Hough transform, object detection, and QR codes to control drone positioning and calibration. This approach facilitates accurate navigation in a traveling salesman experiment, where the drone visits each wind turbine and returns to a designated launching point without relying on GPS. To perform experiments and investigate the performance of the proposed computer vision technique, the DJI Tello EDU drone and pedestal fans are used to represent commercial drones and wind turbines, respectively. Our detailed and timely experiments demonstrate the effectiveness of computer vision-based path planning in guiding the drone through a small-scale surrogate wind farm, ensuring energy-efficient paths, collision avoidance, and real-time adaptability. Although our efforts do not replicate the actual scenario of wind turbine inspection using drone technology, they provide valuable educational contributions for those willing to work in this area and educational institutions who are seeking to integrate projects like this into their courses, such as autonomous systems.

Growth in turbine size and technological development of modern commercial large scale wind turbines in Türkiye

In this study, in addition to the recent advances and tendencies in wind turbine technology around the world, the progress of commercial wind turbine technology installed in Turkey was also thoroughly examined. In this respect, several metrics for installed wind turbines including number of turbines, installed power capacity (MW), mean rated capacity (MW), mean rotor diameter (m), mean specific power capacity (W/m2), and mean hub height (m) have been obtained between the years of 2011 and 2019. According to the obtained results, the mean rated capacity of Turkey’s annual installed wind turbines advanced from 1.86 MW in 2011 to 3.52 MW in 2019. However, the mean specific power of yearly installed wind turbines declined from 423.7 W/m2 to 314.1 W/m2. Outcomes revealed that the growth in the size and the reduction in the specific power have contributed to the tendency of higher power outputs, and wind turbine capacity factor and power generation capacity have been on the rise in Turkey. In time, wind turbines with greater rotor diameters and hubs started to show up more observable on land. For that purpose, the proposed solution to regulate turbine visibility during site selection is the potential visibility model (PVM), which is meant to be used as an auxiliary variable.

Adaptive Robust Observer-Based Control for Structural Load Mitigation and Speed Regulation in Commercial Wind Turbines

Adaptive Robust Observer-Based Control for Structural Load Mitigation and Speed Regulation in Commercial Wind Turbines

Commercial wind turbines and residential home values: New evidence from the universe of land-based wind projects in the United States

We examine the impact of proximity to land-based commercial wind turbines on residential home values in the United States using data on the universe of commercial wind turbines and residential property transactions from 2005 to 2020. Using event study and difference-in-differences identification strategies we find that, on average, homes located within 1 mile of a commercial wind turbine experience approximately an 11% decline in value following the announcement of a new commercial wind energy project, relative to counterfactual homes located 3 to 5 miles away. Event study estimates also reveal important dynamics in the evolution of home values, with property values first declining following project announcement, and then recovering post project construction, with property value impacts becoming relatively small (∼2%) and statistically insignificant 9 years or more after project announcement (roughly 5 years after operation began). Homes located within 1–2 miles of a commercial wind turbine experience much smaller impacts and homes located farther than 2 miles away are unaffected. Our results are primarily driven by wind projects located in urban counties with populations greater than 250,000.

Wind vane correction during yaw misalignment for horizontal-axis wind turbines

Abstract. This paper investigates the accuracy of wind direction measurements for horizontal-axis wind turbines and their impact on yaw control. The yaw controller is crucial for aligning the rotor with the wind direction and optimizing energy extraction. Wind direction is conventionally measured by one or two wind vanes located on the nacelle, but the proximity of the rotor can interfere with these measurements. The authors show that the conventional corrections, including low-pass filters and calibrated offset correction, are inadequate to correct a systematic overestimation of the wind direction deviation caused by the rotor misalignment. This measurement error can lead to an overcorrection of the yaw controller and, thus, to an oscillating yaw behaviour, even if the wind direction is relatively steady. The authors present a theoretical basis and methods for quantifying the wind vane measurement error and validate their findings using computational fluid dynamics simulations and operational data from two commercial wind turbines. Additionally, the authors propose a correction function that improves the wind vane measurements and demonstrate its effectiveness in two free-field experiments. Overall, the paper provides new insights into the accuracy of wind direction measurements and proposes solutions to improve the yaw control for horizontal-axis wind turbines.

Data‐driven models for predicting remaining useful life of high‐speed shaft bearings in wind turbines using vibration signal analysis and sparrow search algorithm

AbstractWind turbine bearings play a crucial role in ensuring the safe and efficient operation of wind turbines. Accurate estimation of the remaining useful life (RUL) of bearings can significantly reduce operating and maintenance costs. In this paper, we propose three advanced data‐driven models to predict the RUL of high‐speed shaft bearings in wind turbines. These models combine the sparrow search algorithm (SSA) with three different regression models, namely support vector machine, random forest (RF) regression and Gaussian process regression. The models are based on features extracted from the vibration signal analysis, and the features are selected based on their monotonicity to evaluate bearing degradation. To optimize the performance of the regression models, all model parameters are tuned using the SSA algorithm. The proposed models are validated using vibration data collected from a real 2 MW commercial wind turbine. Our results demonstrate that the proposed models are effective in predicting the RUL of wind turbine bearings, and the SSA algorithm improves the accuracy of the predictions.

Aerodynamic load evaluation of leading edge and trailing edge windward states of large-scale wind turbine blade under parked condition

The safety and reliability of wind turbine blades are increasingly challenged by extreme wind conditions such as typhoons, as wind turbines tend to become larger. Under these conditions, most units will be shut down and the blades will be pitched to around 90° to minimize the loads. This paper aims to compare a new strategy for the parked condition, i.e., the trailing edge windward state, with the traditional leading edge windward state to verify its technical feasibility. The aerodynamic loads of a 30%-thickness airfoil and a commercial wind turbine blade are comprehensively evaluated by wind tunnel experiment, CFD simulation and engineering analytical model. The two-dimensional airfoil cases indicate that the airfoil resultant force is lower in the trailing edge windward state than in the leading edge windward state for a wide range of angles of attack. Consequently, the three-dimensional blade cases shows that the low load region of the trailing edge windward state is relatively wider than that of the leading edge windward state. The averaged blade root load is reduced by 41.4% ~ 57.8% through trailing edge windward state with prescribed error bounds of windward angular. Besides, it is suggested that the traditional engineering analytical model should improve the precision of the extrapolated airfoil data around AOA = 180 deg. to ensure the accurate load evaluation under the trailing edge windward state. This study suggests a new control strategy for wind turbine blades under the parked condition, which offers significant benefits for load reduction and has a good potential for future applications.

Technological and dimensional improvements in onshore commercial large-scale wind turbines in the world and Turkey

Technological and dimensional improvements in onshore commercial large-scale wind turbines in the world and Turkey

Research on the Rheological Characteristics of Wind Power Grease Based on Rheological Parameters

Our research scrutinizes the impact of grease rheological properties on the lubrication performance of wind turbine spindle bearings. The rheological behavior of three distinct commercial wind turbine greases was examined with a rotational rheometer. Investigations into the viscoelastic, flow, and viscosity–temperature attributes of the grease under varying temperatures were conducted, and the rheological parameters were fitted utilizing the Herschel–Bulkley (H–B) model. Constitutive equations of the grease derived from fitting the H–B model can efficaciously predict its rheological properties and viscosity–temperature behavior for wind power spindle bearings at disparate temperatures.

A Three-Phase Phase-Modular Single-Ended Primary-Inductance Converter Rectifier Operating in Discontinuous Conduction Mode for Small-Scale Wind Turbine Applications

Small-scale wind turbines play an important role in distributed generation since customers can use their houses, farms, and business to produce electric energy. The development of the power electronics system that processes the electric energy from small-scale wind turbines is a concern due to cost, simplicity, efficiency, and performance trade-offs. This paper presents the results of applying a three-phase phase-modular single-ended primary-inductance converter rectifier to processing the energy of a small-scale wind turbine system. The rectifier was designed according to the specifications of a commercial small-scale wind turbine system and tested in an emulator workbench, providing experimental data on the operation of the rectifier in this application. The rectifier can process the energy of a non-sinusoidal three-phase system since the permanent magnet synchronous generator has trapezoidal waveforms. The results show that the rectifier has the advantages of (i) using the inductance of the generator as the input filter inductor of the rectifier, (ii) providing input currents with the same shape as the voltages and in phase without the use of a current control system, (iii) simplicity of control of the DC output voltage and PWM modulation, and (iv) phase-modular characteristics that allow operating with phase fault without any additional control techniques. Due to the operation in discontinuous conduction mode, low efficiency in high power and/or low input voltage specifications are disadvantages.

A Grey Wolf Optimization Algorithm-Based Optimal Reactive Power Dispatch with Wind-Integrated Power Systems

Keeping the bus voltage within acceptable limits depends on dispatching reactive power. Power quality improves as a result of creating an effective power flow system, which also helps to reduce power loss. Therefore, optimal reactive power dispatch (ORPD) studies aim at designing appropriate system configurations to enable a reliable operation of power systems. Establishment of such a configuration is handled through control variables in power systems. Various control variables, such as adjusting generator bus voltages, transformer tap locations, and switchable shunt capacitor sizes, are utilized to achieve this objective. Additionally, the integration of wind power can greatly impact power quality and mitigate power loss. In this study, the Grey Wolf Optimization (GWO) approach was applied to the ORPD issue for the first time to discover the best placement of newly installed wind power in the power system while taking into account tap changer settings, shunt capacitor sizes, and generated power levels. The main objective was to determine optimal wind placement to minimize power loss and voltage deviation, while maintaining control variables within specified limits. On the basis of IEEE 30-bus and IEEE 118-bus systems, the performance of the proposed method was investigated. The results demonstrated the superiority of GWO in multiple scenarios. In IEEE-30, GWO outperformed the PSO, GA, ABC, OGSA, HBMO, and HFA methods, reducing total loss by 10.36%, 18.03%, 9.19%, 7.13%, 5.23%, and 7.73%, respectively, and voltage deviation by 68.00%, 1.59%, 36.34%, 41.97%, 46.29%, and 71.08%, respectively. In wind integration scenarios, GWO achieved the simultaneous reduction of power loss and voltage deviation. In IEEE-118, GWO outperformed the ABC, PSO, GSA, and CFA methods, reducing power loss by approximately 19.91%, 16.83%, 14.09%, and 4.36%, respectively, and voltage deviation by 8.50%, 14.15%, 16.19%, and 7.17%, respectively. These promising results highlighted the potential of the GWO algorithm to facilitate the integration of renewable energy sources, and its role in promoting sustainable energy solutions. In addition, this study conducted an analysis to investigate site-specific wind placement by using the Weibull distribution function and commercial wind turbines.