Experimental Comparative Analysis of Energy Production in HAWT with Bio-Inspired Active Oscillating Vortex Generators

Abstract

This study presents a comparative analysis of horizontal-axis wind turbines (HAWTs) equipped with and without bio-inspired active oscillating vortex generators (VGs). The experimental investigation examines key aspects of mechanical integration and the resulting variations in aerodynamic behavior, demonstrating measurable improvements in electrical power output. The VGs were designed and implemented using servomechanisms and embedded control systems to enable oscillatory motion during operation. Experimental findings were validated against CFD simulations, indicating that the use of VGs increases annual energy production efficiency by 16.7%, primarily due to the stabilization of wake turbulence. While a reduction in output voltage was observed at wind speeds below 5 m/s, the VGs exhibited enhanced performance under variable wind conditions. These results highlight the potential of combining biomimetic design principles with electronically actuated flow-control devices to advance HAWT technology, improving energy efficiency and contributing to operational sustainability.

1. Introduction

The development and optimization of wind turbines has long been a central priority in the advancement of renewable energy technologies [1,2]. In this context, horizontal-axis wind turbines (HAWTs) have demonstrated high efficiency in converting the wind’s kinetic energy into electrical power [3,4]. However, these turbines operate under variable conditions, being exposed to fluctuations, gusts, and changes in wind direction. Such conditions not only induce structural vibrations but also lead to loss of lift on the blades and a gradual decline in energy performance over time, ultimately compromising their operational lifespan [5,6].

One of the most widely employed strategies to mitigate these effects is the implementation of vortex generators (VGs) [7,8], installed on the blade surface to control the boundary layer and prevent its separation. However, commercial VGs are passive devices that are highly effective under steady-flow conditions [9], but are limited in their performance under variable wind conditions as they lack the capability to adapt to dynamic and fluctuating environments.

To address this limitation, this study proposes an innovative solution through the design, implementation, and testing of active oscillating vortex generators (VGs) [7]. These devices are bio-inspired [10,11,12,13] by the study of the dorsal feathers of the peregrine falcon, the fastest bird in the world, whose morphology enables it to stabilize its flight during controlled dives at high velocities [14,15].

The potential of vortex generators (VGs) to enhance wind turbine blade performance by delaying flow separation and improving aerodynamic efficiency. Analyzed passive VGs on airfoils undergoing dynamic pitch oscillations, confirming their effectiveness in suppressing stalls and increasing overall energy capture [16]. Building on this, developed an integrated aerodynamic design framework for airfoils with VGs using optimization techniques, achieving notable improvements in maximum lift and lift-to-drag ratio [17]. A study in Engineering Structures reported that the aerodynamic benefits of VGs are strongly dependent on their radial position along the blade span, reinforcing the importance of installation strategies [18]. Further compared different VG geometries on a research wind turbine, highlighting that chordwise placement on the suction side significantly influences stall delay and power output. Together, these studies provide a robust foundation for the design and installation strategy developed in this work, seeking to maximize aerodynamic benefits while minimizing additional drag.

The active VGs replicate the principle identified in the torque analysis [7], also considering the study of programmed oscillation frequencies, modifying the inclination angle in real time through embedded systems [19,20], where it was determined that the optimal oscillation frequency for the active VGs is 1 Hz, with inclination angles from 0° to 90°.

For experimental implementation, it was necessary to redesign the HAWT blades to accommodate the active VGs, which require a system for varying both frequency and inclination angle. To conduct these experimental tests, a wireless power supply system was developed, and mechanical adaptations were made to the turbine’s transmission system. These aspects are detailed in the design section using CAD-CAE tools and are supported by the results, which demonstrate that the modifications do not compromise the stability or structural integrity of the wind turbine.

This paper focuses on the mechanical design implementation and the experimental study demonstrating that the integration of active VGs, when compared with systems without VGs and with previous experimental wind tunnel studies [19], improves electrical power generation efficiency [21,22] and, in turn, open new research avenues related to vibrational response [19,23] and mechanical efficiency studies that will drive an increase in wind turbine durability, particularly for units installed in remote areas where maintenance and system reliability is critical [24].

2. Materials and Methods

To analyze the behavior of vortex generators (VGs) (VGs) [25], implemented in HAWT wind turbines, a set of blades adapted with servomotors was constructed for conducting experimental tests. The research methodology developed consists of four phases, as shown in Figure 1.

The methodology employed in this study is based on the recommendations derived from three-dimensional computational fluid dynamics (3D CFD) simulations [7,26] and wind tunnel tests [19,27]. The experimental tests aim to compare the aerodynamic performance of a commercial HAWT without VGs to that of one with blades equipped with active vortex generators. In the first step, a mechanical design was developed for the implementation of the VGs, including the angular motion activation system and oscillation frequency control. This process required modifying the rotor shaft coupling to distance the magnetic system from the aluminum housing, with the aim of reducing electric field interference in the coil of the wireless power transmission module. The mechanical design includes an adaptation stage to extend the shaft, positioning the blades 64.65 mm away from the turbine’s original housing.

The design was validated through CAD–CAE simulations to ensure proper operation of the wind turbine. Based on this design, a system was developed with a wireless power supply for the servomotors and a miniESP32 embedded system, allowing free rotation of the blades along the horizontal axis.

For the HAWT body, slip rings were incorporated into the vertical shaft without affecting its normal operation. The design of the bio-inspired vortex generators was carried out using Solidworks 2023 (CAD software) and manufactured via 3D printing with Gray V4 photo-curable Formlabs resin (Formlabs Twin City, 22 McGrath Highway, Suite 201, Somerville, MA, USA). They were mounted on 2 mm stainless steel shafts, actuated by servomotors and the miniESP32 embedded system. This configuration enabled the generators to oscillate between 0° and 15° at a constant frequency of 1 Hz. The two wind turbines were installed on the campus of the Universidad Militar Nueva Granada, located in Cajicá, Cundinamarca, Colombia, as shown in Figure 2. The average wind speed measured in Cajicá is approximately 4.1 m/s, with a deviation of ±0.3 m/s, and its wind power density is low, which makes commercial wind energy generation unfeasible. However, one-hour intervals with wind speeds between 5 and 7 m/s can be observed on some days, mainly during the windy seasons. The signals obtained made it possible to analyze the electrical power generation performance of each HAWT according to the actual wind variations in the area where they were installed.

Gray Formlabs Grey Resin V4 is a versatile SLA (Stereolithography) resin for prototyping with strong mechanical properties, including an Ultimate Tensile Strength of 65 MPa and a Tensile Modulus of 2.8 GPa. It also offers a Flexural Modulus of 2.2 GPa, Elongation at Break of 6%, and a Notched Izod impact strength of 25 J/m. The post-cured resin features a matte, opaque finish, neutral undertone, and high feature detail, making it suitable for detailed models, presentation pieces, and as a base for painted parts.

The VGs were inspired by the dorsal feathers of the peregrine falcon, which have active microstructures for flow control. Their geometry was designed to be slender and oscillating, imitating the shape and motion of feathers that emerge under critical flow conditions. The final length of each VGs was set to 11.2 mm, corresponding to a VG-to-chord ratio of 0.16 relative to the S822 airfoil section.

The location of the VGs considers that the HAWT blades used in this study have a variable chord, with the airfoil sections aligned at the quarter-chord, a position where the distance between the suction and pressure sides is larger, maintaining a nearly constant turbulence level. In addition, the VGs were installed on the suction side of the blade, between 20% and 40% of the span, which is the region carrying the highest aerodynamic load and where flow separation is most likely to occur. This arrangement allows the generated vortices to re-energize the boundary layer, delay stall, and improve lift. The installation criterion was defined based on CFD simulations and wind tunnel experimental data, which indicate that positioning VGs near the quarter-chord region enhances flow stability and the overall aerodynamic efficiency of the turbine.

The area where the HAWTs are installed experiences wind speed variations ranging from 2 to 10 m/s, with typical speeds between 5 and 7 m/s. These fluctuations produce noticeable variations in the generated voltage, which will be explained in detail in the results section. HAWT wind turbines are the most widely used in the market [28] and are commercially well-developed due to their ease of installation and mechanical robustness. The arrangement of the vortex generator devices is proposed in a linear configuration along the extrados of the blades, aiming for the greatest similarity to the dorsal feathers of the peregrine falcon (bio-inspired design) [14].

3. Results

3.1. Biomimetic Design and Development of Active VGs

The bio-inspired vortex generators (VGs) can be compared to the feathers of the peregrine falcon, which deploy covert feathers during high-speed dives to generate controlled vortices, delay boundary layer separation, and enhance lift. In a similar way, the VGs induce streamwise vortices that energize the boundary layer, mitigating flow detachment and improving aerodynamic stability and efficiency of the turbine blades. This comparison underlines the relevance of biological flow-control mechanisms as models for advanced engineering design. Figure 3 shows the assembly of the HAWT equipped with bio-inspired active vortex generators. To begin this results section, a comparison of the two wind turbines is presented, highlighting the impact on reducing boundary layer separation when the VGs are installed at an inclination angle of 15 degrees, as determined in the 3D CFD study [7], in which the simulation parameters are detailed.

Figure 4 presents a complete schematic of the new configuration, including the additional components that make up the experimental setup used to perform voltage and frequency measurements on the three-phase RL load in a Y configuration.

Figure 4 shows the wireless power supply modules for the embedded system located in the turbine, which is responsible for generating the PWM signal for the servomotors that oscillate the linear array of vortex generators mounted on a 2 mm diameter stainless steel shaft.

3.2. Structural Adaptation of the HAWT

The following section presents the shaft extension calculations performed through CAD–CAE simulation, along with details of the adjustment, demonstrating its functionality and operation for the experimental tests. The simulation considers the weight of the blades, 12.75 N, displaced 64.65 mm outward from the original position on the wind turbine shaft. The equivalent axial load corresponding to the thrust generated by the maximum wind speed was calculated as 72 N, Equation (1).

The maximum torsional load generated by rotation is 4.2 Nm. For the simulation, the shaft extension design is modeled using the threaded tip of the original shaft as the anchoring point, validating the loads present in the new configuration, Figure 4.

$$F_A={\displaystyle \frac{1}{2}} \ast \rho \ast A \ast V^2 \ast C_T$$

(1)

For the calculation of the axial thrust forces in

Table 1. Axial Force and Torque using CFD simulation vs. Wind velocity.

Wind Velocity (m/s)	Axial Wind Force (N)	Torque (Nm)
3	5.43	0.3
7	29.56	2.0
11	72.03	4.2

, the following parameters were used: ρ = 0.98 kg/m³, Lp = 0.7 m, A = 1.54 m², and CT = 0.8. For the simulation, these loads are applied to the centerline of the rotor shaft. This CT value is appropriate as it represents a conservative parameter for the structural sizing of the shaft extension. Typical CT values for low-power, three-bladed HAWT generators range between 0.45 and 0.85. Using a higher value ensures that the structural verification of the shaft extension remains on the safe side without overestimating the operational loads. The calculation of F_A is important for simulating the loads on the shaft extension required for the implementation of active VGs. This type of table is not typically used in conventional wind turbine analyses; however, in this study it is applied to assess whether the shaft extension is technically suitable.

The parameters established for the combined shaft loads take fatigue into account [29] due to rotating bending from the weight of the blades, fluctuating torsional loads within the operational limits ranging approximately from 1 Nm to 4.2 Nm, and axial loads due to thrust forces ranging from 5.43 N to 72.03 N. The shaft is made of stainless steel SS316, and the mechanical properties of the material are detailed in

Table 2. Mechanical Properties SS316 Stainless Steel.

Property	Value (MPa)
Sy	138
Sut	550
Se	192
Se′	96
E	200,000

. For the analysis, studies of combined static loads, fatigue for each type of load, and frequency analysis were carried out to determine the critical rotational harmonics of the shaft. For the fatigue studies, a standard operational lifespan for wind turbines of 20 to 25 years was considered, which results in approximately 10 Hz rotation, equivalent to 1.6 billion cycles for bending and torsional loads, and 1 billion cycles for the axial thrust load.

The calculations were carried out using maximum limit reference values, representing continuous operation above normal requirements, but allowing verification that the addition of the shaft for the implementation of the VGs in the HAWT will ensure reliable and safe operation. The static simulation was analyzed using the Von Mises model [30], Equation (2), in SolidWorks^®, and for the fatigue loads, the Maximum Shear Stress model was used, including corrections based on the Gerber method [31], Equation (3).

$${\sigma }_e=\sqrt{{\displaystyle \frac{1}{2}}\left[{({\sigma }_1-{\sigma }_2)}^2+{({\sigma }_2-{\sigma }_3)}^2+{({\sigma }_3-{\sigma }_1)}^2\right]}\le {\displaystyle \frac{S_y}{FDS}}$$

(2)

This model is suitable for ductile materials and uses quadratic relations. The standard fatigue strength factor, Se, was adjusted with a factor of 0.5 for torsional and axial loads, and a factor of 0.6 for bending loads, considering correction factors for surface finish during manufacturing, dimensional factors, and reliability factors for the design [32].

$${\displaystyle \frac{n{\mathit{\sigma }}_a}{{S_e}^{\prime }}}+{\left({\displaystyle \frac{n{\mathit{\sigma }}_m}{S_{ut}}}\right)}^2=1$$

(3)

3.3. Structural Evaluation by CAD–CAE Simulations of Static Loads, Fatigue, and Critical Frequencies

Figure 5 shows the results of the static analysis, showing a minimum safety factor of 6.77. This value is in the high-stress concentration area due to the diameter change for the blade coupling. This factor is adequate for the design and normal operation of the equipment.

The results of the static analysis of the shaft extension are presented in

Table 3. Static Simulation Results.

Property	Value (Unit)
σMax	20.37 (MPa)
FDS	6.77
δMax	0.003 (mm)
εMax	0.000088

below.

Figure 6 shows the fatigue study simulations for bending, axial, and torsional loads. These simulations were performed with significantly higher loads, with a magnitude of 50 for bending, 100 for axial loads, and 10 for torsion with respect to the nominal working value. The loads were progressively increased until the shaft failure limit was reached. Assuming real operational loads, the results indicate that the shaft will not fail due to fatigue.

Figure 7 shows the four main frequencies (Equation (4)) at which the shaft may experience resonance. Of these frequencies, only the first, at 14.42 Hz, approaches the maximum operating range of the wind turbine in its installed location, which corresponds to 10 Hz. It shows the four main frequencies (Equation (4)) at which the shaft may experience resonance.

$$f_c={\displaystyle \frac{\sqrt{g \ast \left(\frac{\sum _{i=1}^n{w}_i \ast {\mathit{\delta }}_i}{\sum _{i=1}^n{w}_i \ast {{\mathit{\delta }}_i}^2}\right)}}{2\pi }}$$

(4)

Of these frequencies, only the first, at 14.42 Hz, approaches the maximum operating range of the wind turbine at its installation site, which corresponds to 10 Hz. This indicates that the equipment will not experience operational issues at maximum speeds, as the other frequencies are unattainable during normal operation [33].

The mechanical redesign and experimental configuration of the HAWT wind turbine utilize VGs featuring the morphological characteristics of the peregrine falcon’s dorsal feathers, as analyzed in previous studies [14]. It was established that the feathers have a natural inclination capable of adapting to the wind flow, allowing stabilization of the body during high-speed maneuvers.

Based on this behavior, mobile devices measuring 11.2 mm in length with a VG/chord ratio of 0.16 were designed, tailored to the S822 airfoil profile. The objective of the new design, focused on implementing active VGs, arose from the need to power the servomotors and the miniESP32 embedded system without affecting the rotational operation of the HAWT on the vertical and horizontal axes, respectively. This requirement was met by modifying the wind turbine body, extending the shaft that connects the rotor to the blades and adapting the hub, Figure 8.

The proposed biomimetic vortex generator (VG) is inspired by the morphology of the wing feathers of the peregrine falcon, particularly the secondary feathers that exhibit protrusions on the trailing edge. Based on images and morphometric measurements, key geometric parameters such as height, length, and inclination angle of these structures were extracted. These parameters were then adapted into an optimized three-dimensional design using CAD software, ensuring a geometry feasible for fabrication through 3D printing (SLA). The result is an elongated-profile VG with a smooth curvature at the base, designed to induce the generation of controlled vortices under turbulent flow conditions.

The VG oscillation at 1 Hz was selected based on the spectral energy density measured in the wake of the falcon’s real feather, which was observed between 0 and 2 Hz. A frequency of 1 Hz was chosen as the central frequency of the magnitude spectrum with FFT (Fast Fourier Transform) function in Matlab 2022-b.

3.4. Experimental Data and Acquisition System

The data acquisition system consists of multiple Raspberry Pi embedded systems, interconnected with various sensors such as the 3D ultrasonic anemometer Wind Master Gill^® (Gill Instruments Limited, Saltmarsh Park, 67 Gosport Street, Lymington, Hampshire, UK) and two Pikasola^® 400 W Wind Turbines (707 SW Washington St #1100, Portland, OR, USA). The sensor signal data is transmitted via IoT modules and subsequently stored on an AWS server. Figure 9 shows the data visualization interface.

To monitor the information stored in the database, Grafana with MySQL was used. Data processing was performed in Linux Bash Shell, where a script was created to execute the data acquisition process. The execution frequency was set using Crontab, with data updates to the station occurring every 10 min.

The measured data are uploaded to two platforms: Thingspeak^® and The things network^®, which receive the output voltage and frequency data from the two wind turbines. For the operation of the physical system (HAWT and HAWT + VGs), a minimum wind speed is established to overcome inertia and achieve energy production. A threshold value of 2 m/s is set for HAWT without VGs, while a threshold of 3.5 m/s is defined for HAWT + VGs.

At low wind speeds, the vortices generated do not supply sufficient energy to the boundary layer to compensate for the induced drag, which increases the starting torque required. This effect results in a higher cut-in threshold and a reduction in energy capture, where the rotor remains inactive for longer periods. However, at medium and high wind speeds, the VGs effectively fulfill their aerodynamic role: they stabilize the boundary layer, creating a low-turbulence wake regime, which significantly improves annual energy efficiency in the face of sudden changes in wind direction.

Table 4. Correlation between generated RMS voltages and wind speed.

Threshold ABS (Wind) (m/s)	Correlation V_G1	Correlation V_G2
0	0.58	0.25
1	0.53	0.23
2	0.55	0.22
3	0.66	0.26
3.4	0.69	0.28
3.5	0.69	0.28
3.6	0.49	0.34
4	0.49	0.37
4.9	0.46	0.41
5	0.45	0.42
5.1	0.46	0.41
5.5	0.39	0.41
6	0.33	0.39

below shows the correlation coefficient Pearson between the RMS voltage measured in the two wind turbines and the magnitudes of the wind using Absolute values (ABS).

3.5. Performance Comparison: HAWT vs. HAWT with VGs

Based on the monitored RMS AC voltages and balanced three-phase star-connected resistive loads of 100 Ω–100 W for each wind turbine, calculations were performed, yielding the curves shown in Figure 10. The annual energy production for HAWT and HAWT + VGs wind turbines is presented in

Table 5. Average values V_{AC RMS} (V), P_{AC RMS} (W) y F_ELEC.

Turbine	VAC RMS (AVG) (V)	PAC RMS (AVG) (W) RL 3ϕ-Y = 100 Ω	FELEC (AVG) (Hz)
HAWT	3.63	0.96	30
HAWT + VGS	3.08	0.821	22.4

.

Figure 10 shows the average measured values of turbines 1 and 2 in a gauge-type diagram, where the maximum and minimum values can also be quickly observed. The orange post signal represents samples of RMS voltage taken approximately every 2 min, while the purple posts indicate its electrical frequency; these are digital signals. The absence of posts indicates data not received in the database due to loss of connection.

Table 6. Comparison $E_{\left(PROMEDIO\right)}$ (Wh) (AVG).

Turbine	${\mathit{E}}_{\left(\mathit{A}\mathit{V}\mathit{G}\right)}$ Day (Wh)	${\mathit{E}}_{\left(\mathit{A}\mathit{V}\mathit{G}\right)}$ Month (Wh)	${\mathit{E}}_{\left(\mathit{A}\mathit{V}\mathit{G}\right)}$ Year (Wh)
HAWT	23.040	691	8292
HAWT + VGS	19.704	591.12	7093

shows the average energy production data calculated (Equation (5)), and

Table 7. Maximum values of turbines HAWT vs. HAWT + VG’s.

Turbine	VAC RMS (MAX) (V)	PAC RMS (MAX) (W) RL3ϕ-Y = 100 Ω	FELEC (MAX) (Hz)
HAWT	16.5	8.36	122
HAWT + VGS	18	9.75	98

presents the maximum values of voltage, power, and frequency.

$${\mathrm{E}}_{\left(\mathrm{A}\mathrm{V}\mathrm{G}\right)}={\mathrm{P}}_{\mathrm{A}\mathrm{C} \mathrm{R}\mathrm{M}\mathrm{S}\left(\mathrm{W}\right)} \ast \mathrm{h}=\mid \mathrm{P}\mid \ast \mid \mathrm{h}\mid \left(\mathrm{W}\mathrm{h}\right)$$

(5)

The reduction in the daily, monthly, and annual average production in Table 5 and Table 6 is explained by the increase in the cut-in threshold of the turbine with active VGs. At low wind speeds (<5 m/s), the additional drag generated by the VGs reduces the output compared to the conventional HAWT. However, at medium and high wind speeds (>5 m/s), the turbine equipped with VGs outperforms the baseline configuration, achieving a 16.7% increase in annual energy production under variable operating conditions. The measurements stored on the monitoring platform enable the comparison of the maximum energy produced by the wind turbines HAWT and HAWT + VGs, under conditions of maximum wind velocity resource.

$${\mathrm{E}}_{(\mathrm{P}\mathrm{R}\mathrm{O}\mathrm{M}\mathrm{E}\mathrm{D}\mathrm{I}\mathrm{O} \mathrm{i}\mathrm{d}\mathrm{e}\mathrm{a}\mathrm{l})}={\mathrm{P}}_{\mathrm{A}\mathrm{C} \mathrm{R}\mathrm{M}\mathrm{S} \left(\mathrm{M}\mathrm{A}\mathrm{X}\right)} \ast \mathrm{h}=\mid \mathrm{P}\mid \ast \mid \mathrm{h}\mid \left(\mathrm{W}\mathrm{h}\right)$$

(6)

The monitored voltage values for Turbine 1 (HAWT) are higher at wind speeds below 3 m/s; however, Turbine 2 (HAWT + VGs) exceeds the voltage values at wind speeds above 5 m/s sustained by a minimum without change in direction. When the wind direction changes rapidly, Turbine 2 also shows better preservation of rotational speed.

Table 8. Energy comparison using PAC RMS (MAX).

Turbine	${\mathit{E}}_{\left(\mathit{P}\mathit{R}\mathit{O}\mathit{M}\mathit{E}\mathit{D}\mathit{I}\mathit{O}\right)}$ Day (Wh)	${\mathit{E}}_{\left(\mathit{P}\mathit{R}\mathit{O}\mathit{M}\mathit{E}\mathit{D}\mathit{I}\mathit{O}\right)}$ Month (Wh)	${\mathit{E}}_{\left(\mathit{P}\mathit{R}\mathit{O}\mathit{M}\mathit{E}\mathit{D}\mathit{I}\mathit{O}\right)}$ Year (Wh)
HAWT	200.4	6012	72,144
HAWT + VGS	234	7020	84,240

shows the average maximum energy production data calculated using Equation (6).

Turbine 1 exhibits greater sensitivity to rapid changes in wind direction, reducing its rotational speed and generating a peak voltage of approximately 3 to 4 V, values that are lower compared to Turbine 2.

Figure 11 shows the measurements taken during the hours of the day with the highest recorded wind speed. In this time window, it is possible to observe the increase in the RMS Power measured for turbine 2 compared to turbine 1.

The comparison highlights that, although the turbine with VGs presents a higher cut-in threshold at low wind velocities, it achieves superior performance at medium and high wind speeds, resulting in a net increase in annual energy production under variable operating conditions.

4. Discussion

The installation principle of the biomimetic vortex generators (VGs) was defined by considering the aerodynamic behavior of the boundary layer along the variable-chord blades of the HAWT. The devices were installed on the suction side, in the region between 20% and 40% of the airfoil (upper surface) and near the quarter chord, where the risk of flow separation is highest and the effectiveness of vortex generation is maximized.

The oscillating VGs induce controlled streamwise vortices that re-energize the boundary layer, delay flow separation, and stabilize the aerodynamic load distribution. This strategy reduces large-scale flow separation, improves wake recovery, and increases the lift-to-drag ratio at medium and high wind speeds.

The adaptive nature of biomimetic design also provides the possibility of selectively activating the VGs, which can mitigate the increase in drag at low velocities.

The active VGs, modeled after the dorsal feathers of the peregrine falcon and installed on the HAWT turbine, demonstrated clear advantages over the standard HAWT turbine. They showed improvement in the dynamic response capability to variations in wind direction at speeds above 5 m/s. At wind speeds below 3 m/s, a slight reduction in electrical power output was observed, where the absence of VGs proved more favorable.

This finding opens the possibility of expanding these research lines toward future designs of activation systems that allow the adjustment and modulation of the inclination angle of the active VGs according to the operating conditions of wind turbines, thereby increasing overall efficiency within their operating range and improving wake flow stability.

The developed methodology strengthens the creation of hybrid approaches that enable the analysis of various parameters, which in turn validate different phenomena impacting electrical energy production through HAWT generators. Furthermore, 3D CFD simulations allowed the identification of significant operational differences in the reduction in kinetic energy in the turbulent wake, which are evidenced by the increase in power generation at high wind speeds. This demonstrated a relevant correlation between the CFD models, and the results obtained in the experimental tests.

From a technological perspective, the integration of the miniESP32 embedded system and Raspberry Pi 4^® as activation, data acquisition, and wireless communication platforms proved to be functional, with low power consumption and easy scalability. The connection with the IoT platform (Grafana^®) enables the design of intelligent wind systems with adaptive activation, capable of operating in changing conditions without supervision.

The use of additive manufacturing techniques (3D printing) and affordable electronic components allows this technology to be applied in low-power contexts, such as non-interconnected rural areas. Although the study was conducted at a laboratory scale and focused on a single aerodynamic profile (S822), the modular design and consistent results suggest the feasibility of scaling and adapting it to other environments, such as wind farms.

Among the observed limitations, the need for long-term validation under real operating conditions stands out, as well as the exploration of its behavior in other aerodynamic profiles commonly used in higher-power turbines. The potential for improving the VG activation system was also identified, through the incorporation of wind sensors and machine learning algorithms that would allow for even more precise and efficient management.

Finally, this research contributes to the evolution of turbine design toward adaptive models, where biomimetic concepts are integrated with digital technologies and activation strategies using embedded systems. The results demonstrate that inspiration from natural mechanisms can be translated into energy solutions with technical, economic, and social impact, aimed at sustainability and the decentralization of wind power generation.

5. Conclusions

To analyze the applicability of this technology in real environments, an atmospheric monitoring system was implemented in a rural area of Cundinamarca, enabling the characterization of 3D wind conditions and the evaluation of related environmental variables. This analysis revealed a strong correlation between wind magnitude and the average RMS voltage generated by HAWT wind turbines, allowing the establishment of specific operating thresholds for different designs. It was determined that the conventional HAWT starts generating from 3.5 m/s, while the HAWT modified with bio-inspired vortex generators requires exceeding 5 m/s to produce energy efficiently. This higher threshold is due to the initial aerodynamic behavior of the active VGs, but is offset at wind speeds above 5 m/s.

These results suggest that the penalty in the low-wind-speed range can be mitigated through selective or adaptive activation of the VGs, that is, keeping them retracted or inactive when the wind velocity is insufficient to compensate for the additional drag, and deploying them when the turbine operates within its most efficient range. This adaptive operating principle represents an opportunity for future developments in intelligent VG control, integrating wind sensors and machine learning algorithms to optimize the overall performance of the turbine under variable wind conditions.

When comparing both wind turbines under the same technical and electrical conditions, the differences in the power curve were found to be mainly due to the presence of the active VGs. At constant wind speeds above 5 m/s, the turbine with active VGs generated 16.7% more energy annually than the conventional one. However, for wind speeds between 2 m/s and 5 m/s, the conventional turbine was more efficient, generating 16.9% more energy than the modified version. These results confirm that VGs are effective in environments with strong and variable winds, typical of open rural areas. By integrating data analysis, experimental testing, and bio-inspired design, this study proposes a robust and adaptable solution to expand the use of clean energy in territories with high wind fluctuations.