# Aerodynamic Performance and Impact Analysis of a MEMS-Based Non-Invasive Monitoring System for Wind Turbine Blades

Nicolas Schärer, Denis Mikhaylov, Cédric Sievi, Badoui Hanna, Caroline Braud, Julien Deparday, Sarah Barber, Tommaso Polonelli, Michele Magno

► To cite this version:

Nicolas Schärer, Denis Mikhaylov, Cédric Sievi, Badoui Hanna, Caroline Braud, et al.. Aerodynamic Performance and Impact Analysis of a MEMS-Based Non-Invasive Monitoring System for Wind Turbine Blades. IEEE Access, 2025, 13, pp.187081-187091. 10.1109/ACCESS.2025.3624624. hal-05404762

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Distributed under a Creative Commons Attribution 4.0 International License
Received 8 October 2025, accepted 17 October 2025, date of publication 22 October 2025, date of current version 5 November 2025.

Digital Object Identifier 10.1109/ACCESS.2025.3624624

RESEARCH ARTICLE

# Aerodynamic Performance and Impact Analysis of a MEMS-Based Non-Invasive Monitoring System for Wind Turbine Blades

NICOLAS SCHÄRER<sup>1</sup>, (Graduate Student Member, IEEE), DENIS MIKHAYLOV<sup>1</sup>, CÉDRIC SIEVI<sup>1</sup>, BADOUI HANNA<sup>2</sup>, CAROLINE BRAUD<sup>2</sup>, JULIEN DEPARDAY<sup>1,3</sup>, SARAH BARBER<sup>1,3</sup>, TOMMASO POLONELLI<sup>1,1</sup>, (Senior Member, IEEE), AND MICHELE MAGNO<sup>1,1</sup>, (Senior Member, IEEE)

Corresponding author: Nicolas Schärer (nschae@ethz.ch)

This work was supported in part by French-Swiss Project MISTERY through French National Research Agency (ANR PRCI) under Grant 266157, and in part by Swiss National Science Foundation under Grant 200021L 21271.

**ABSTRACT** Wind power generation plays a key role in transitioning away from fossil fuel-dependent energy sources, contributing significantly to the mitigation of climate change. Monitoring and evaluating the aerodynamics of large wind turbine rotors is crucial to enable more wind energy deployment. This is necessary to achieve the European climate goal of a reduction in net greenhouse gas emissions by at least 55% by 2030, compared to 1990 levels. This paper presents a comparison between two measurement systems for evaluating the aerodynamic performance of wind turbine rotor blades on a full-scale wind tunnel test. One system uses an array of ten commercial compact ultra-low power micro-electromechanical systems (MEMS) pressure sensors placed on the blade surface, while the other employs high-accuracy lab-based pressure scanners embedded in the airfoil. The tests are conducted at a Reynolds number of $3.5 \times 10^6$, which represents typical operating conditions for wind turbines. MEMS sensors are of particular interest, as they can enable real-time monitoring which would be impossible with the ground truth system. This work provides an accurate quantification of the impact of the MEMS system on the blade aerodynamics and its measurement accuracy. Our results indicate that MEMS sensors, with a total sensing power below 1.6 mW, can measure key aerodynamic parameters like Angle of Attack (AoA) and flow separation with a precision of 1°. Although there are minor differences in measurements due to sensor encapsulation, the MEMS system does not significantly compromise blade aerodynamics, with a maximum shift in the angle of attack for flow separation of only 1°. These findings indicate that surface and low-power MEMS sensor systems are a promising approach for efficient and sustainable wind turbine monitoring using self-sustaining Internet of Things devices and wireless sensor networks.

**INDEX TERMS** Wind turbine, aerodynamics, pressure measurement, MEMS, pressure sensors, pressure scanners, IoT.

## I. INTRODUCTION

Wind energy is a key tool for reducing CO2 emissions and meeting global warming targets [1], [2]. To support

The associate editor coordinating the review of this manuscript and approving it for publication was Ahmed A. Zaki Diab.

goals like the EU's target, wind power capacity must expand significantly, from 204 GW in 2022 to over 500 GW by 2030 [3]. This expansion requires optimizing the design and efficiency of wind turbines [4], which starts with a deeper understanding of blade aerodynamics [5]. Real-time monitoring of aerodynamic performance and structural

<footnote n="1">¹PBL, ETH Zürich, 8006 Zürich, Switzerland</footnote>

<footnote n="2">²LHEAA, CSTB, 44323 Nantes, France</footnote>

<footnote n="3">³IET, OST, 8640 Rapperswil-Jona, Switzerland</footnote>
health is critical, particularly for offshore wind farms where
maintenance can make up 20-35% of a turbine's total life
cycle cost [6].

Advanced control strategies like Individual Pitch Control (IPC) [7] and Machine Learning (ML) models for power forecasting [8] are being developed to improve efficiency. However, these strategies are limited by their reliance on indirect data from SCADA or strain gauges, which only measure the blade's structural response, not the aerodynamic forces causing it [6], [9]. This forces controllers to be reactive rather than proactive. Directly measuring the pressure distribution along the blade would provide the high-fidelity data needed to create a truly 'aerodynamically-aware' turbine, but this requires a sensor system that is robust, low-power, and provides critical on-blade data.

Wireless Sensor Networks (WSN) using low-power Micro-
Electro Mechanical Systems (MEMS) such as absolute
pressure sensors or inertial measurement units (IMU) offer
a promising solution [10], [11], [12]. Recent works, namely
the Aerosense system [13], have proven the feasibility of
installing self-sustaining wireless sensor nodes on turbine
blades, demonstrating long-term operation in harsh field
conditions [14]. While these studies establish the techno-
logical feasibility, a rigorous scientific validation against a
high-accuracy ground truth is still missing. The aerodynamic
impact of such a surface-mounted system, as well as its
precise measurement accuracy under realistic conditions,
remain critical, unaddressed questions. To fill these gaps, this
paper provides the first systematic validation and addresses
the following key research objectives: (i) Accuracy and
Performance: To evaluate how accurately a low-power,
surface-mounted MEMS sensor array can measure blade
surface pressure and detect key aerodynamic features (such
as flow separation and Angle of Attack (AoA) with a
precision of ~1°) when compared to a high-accuracy,
invasive pressure scanner system under realistic wind tunnel
conditions (Re = 3.5 × 10⁶). (ii) Aerodynamic Impact: To
quantify the aerodynamic impact of installing a 2.4 mm thick
sensor system on the airfoil surface, and determine whether
it significantly compromises the blade's performance by
altering the flow. (iii) Practical Viability: To assess the
practical benefits and limitations of this MEMS-based
approach, considering the trade-offs between its ultra-low
power consumption (1.6 mW), low cost, and measurement
fidelity compared to conventional lab-based systems. The
main findings of this paper demonstrate that the performance
of surface measurement sensors is comparable to that of
high-accuracy lab-based pressure scanners, with a difference
of 2.3%. The system's low power consumption of 1.6 mW
and minimal aerodynamic impact (a maximum shift in
the separation point of only 1°) confirm its suitability for
effective, long-term wind turbine monitoring.

## II. RELATED WORKS

MEMS pressure sensors have long been identified as a
promising low-cost solution for aerodynamic monitoring.

<figure><img src="image_2.png" /><figcaption>**FIGURE 1.** Architecture example of a self-sustaining node system for wind turbine blade aerodynamic monitoring. The sensor node integrates photovoltaic panels and a battery for energy harvesting, the selected sensors (IMU, absolute pressure sensors), and a SoC for data processing and wireless communication (BLE). The base stations acquire the data from the sensors over a wireless connection and send the data to a server running a digital twin.</figcaption></figure>

Their application has been demonstrated in similar fields
such as aviation, where sensor arrays have been tested on
gliders [15], and in the automotive sector for duct aerody-
namics [16]. Alongside these, other technologies like high-
accuracy fiber-optic sensors [17] and MEMS microphone
arrays for aeroacoustic studies [18], [19] have also been
investigated.

Within the specific domain of wind turbine monitoring,
the state-of-the-art is the field-proven Aerosense system [13].
Its suitability for long-term offshore deployment, where
accessibility and durability are critical, has been demon-
strated by combining a self-sustaining design based on energy
harvesting with long-range wireless data transmission [13].
The detailed architecture of such a system is described in
Figure 1. Building on this, recent work has also focused
on improving the power efficiency of such monitoring
systems. The study by Odermatt et al. [20] explored a hybrid
approach using passive Polyvinylidene Fluoride (PVDF)
piezoelectric sensors as event-driven triggers to activate
MEMS barometers. This strategy demonstrated a significant
reduction in power consumption by avoiding continuous
sampling. A comprehensive summary of these key works is
presented in Table 1.

As highlighted in the table, a critical gap remains: the
fundamental validation of MEMS-based pressure sensor
arrays against a high-accuracy ground truth under realistic
aerodynamic conditions, such as high Reynolds numbers
(typically 3 × 10⁶ to 15 × 10⁶), characteristic of large-scale
wind turbines [22]. This paper directly addresses these critical
gaps. We perform the first systematic comparison of a
surface-mounted MEMS pressure sensor array against a high-
accuracy, invasive pressure scanner on a full-scale airfoil
section at a realistic Reynolds number of 3.5 × 10⁶. The focus
is on quantifying both the measurement performance and the
aerodynamic influence of the non-invasive system, thereby
<table><caption>TABLE 1. Summary of sensor technologies for aerodynamic monitoring in related literature.</caption><thead><tr><td>References</td><td>Sensor Type & Method</td><td>Application</td><td>Key Advantages</td><td>Limitations</td></tr></thead><tbody><tr><td>[15]</td><td>MEMS pressure sensors (64 sensors in a 'wing glove')</td><td>Dynamic flight tests on a glider</td><td>Qualitatively matched strain gauges and XFOIL simulations.</td><td>Bulky housing increased cross-section, uneven sensor placement</td></tr><tr><td>[17]</td><td>Fiber-optic pressure sensors</td><td>Flight test application</td><td>Thin form factor (1.6 mm), high accuracy (&lt;5 hPa difference).</td><td>Higher cost and greater system complexity compared to MEMS</td></tr><tr><td>[16]</td><td>MEMS pressure sensors (12-sensor strip)</td><td>Automotive aerodynamics (S-duct)</td><td>Demonstrated performance comparable to a pressure scanner</td><td>Not validated on an airfoil, sensors were not waterproof for field use</td></tr><tr><td>[18], [19]</td><td>MEMS microphone arrays</td><td>Aeroacoustic studies in wind tunnels</td><td>Enabled high-density noise source identification.</td><td>Measures acoustic pressure fluctuations, not static pressure for lift and aerodynamic performance</td></tr><tr><td>[13], [21]</td><td>Multi-MEMS "Aerosense" system (pressure, IMU)</td><td>In-field monitoring on operating wind turbines</td><td>Proven robustness and self-sustainability in harsh field conditions</td><td>Required tedious sensor calibration, no quantitative validation of measurement accuracy or aerodynamic impact against a ground truth</td></tr><tr><td>[20]</td><td>Hybrid system: PVDF sensors as triggers for MEMS barometers</td><td>Event-driven monitoring on a scaled wind turbine blade</td><td>Ultra-low power consumption via a zero-power wake-up strategy</td><td>Focus on power efficiency and PVDF performance; still lacks a quantitative validation of the MEMS sensors against a ground-truth system</td></tr></tbody></table>

<figure><img src="image_3.png" /><figcaption>FIGURE 2. MEMS sensor and pressure tap distribution on the blade profile. This includes 10 evenly distributed absolute pressure MEMS sensors and 8 pressure taps. The chord length of the blade c is 1.25 m.</figcaption></figure>

providing the essential ground-truth validation missing from prior work, including [20].

### III. EXPERIMENTAL SETUP

The experiments described in this paper were conducted in the Jules Verne wind tunnel owned by the Scientific and Technical Center for Building (CSTB) and located in Nantes, France. The test section, measuring 6 m in width and 5 m in height, accommodates the airfoil with a 1.25 m chord and a span of 5 m. The maximum height is 25 cm (20% of the chord). It occurs at 33% of the chord length from the leading edge. The shape of the airfoil corresponds to a 1:1 scale

of the section at 80% of the span of an operating 2 MW wind turbine blade [23]. The wind speed can reach up to 60 m s⁻¹, reproducing the field conditions at a 1:1 scale. The AoA is varied using two jacks located at the extremities of the blade to be able to sustain 2 t of generated aerodynamic loading and maintain an angular precision below 1°. The blade's structure has different hatches on the pressure side to install the acquisition system and wires inside the blade. The setup has a Reynolds number of 3.5 × 10⁶ and a Mach number of 0.12 at 40 m/s. The free stream wind can reach a speed of 70 m s⁻¹ and is controlled to keep a constant and uniform velocity, taking into account the air density
<figure><img src="image_4.png" /><figcaption>FIGURE 3. Top-down view of the encapsulated absolute pressure MEMS sensor array (orange) and pressure scanner taps (blue) on the blade surface. The encapsulation is the black rubber around the MEMS sensors.</figcaption></figure>

variations. The turbulence intensity is less than 1.5% in the empty test section [23]. On the blade surface, to measure the pressure distribution, a total of 350 high-quality pressure taps are distributed on the blade. This work focuses on a subset of these, placed in the vicinity of the MEMS sensor array, as seen on Figure 3 and Figure 2. The MEMS acquisition system is described in more detail in Section IV.

A total of 18 tests were conducted, each consisting of two-minute acquisitions at a constant wind speed of 40 m s⁻¹ with the blade setup in Figure 2 and Figure 3. Each acquisition has a varying AoA, from -10° to 28°, as described in [24]. In addition, between each AoA, short 10 s acquisitions are performed under stationary flow conditions. These experiments were also repeated without the MEMS sensor array present, allowing an evaluation of the aerodynamic effect of the monitoring system.

## IV. MEMS SENSING SYSTEM DESIGN AND GROUND TRUTH

This section describes the design of the MEMS absolute pressure sensor array system and its placement on the airfoil, as well as the configuration of the pressure scanner. Key sensor characteristics are described in Table 2.

### A. MEMS ABSOLUTE PRESSURE SENSOR ARRAY

The MEMS absolute pressure sensor array is designed to be placed on a key aerodynamic region of the blade’s suction side, between 28% and 55% of the chord, where flow detachment typically occurs and pressure gradients are most significant. The choice of a ten-sensor array for this region was an empirical one, based on a trade-off analysis from preliminary experiments. These earlier tests with a higher sensor density showed significant data redundancy, indicating that a more moderate number of sensors could still capture the essential aerodynamic features. Therefore, ten sensors were selected to provide sufficient spatial resolution without the unnecessary data overhead, power consumption, and electronic complexity that a denser array would entail. This results in a spacing of 37 mm between the sensors.

The MEMS sensor selected is the LPS28DFW from STMicroelectronics, an evolution of the sensor used on

Aerosense (LPS27HHW) [13], providing 1.7 × better relative accuracy and a power consumption reduction of 28%. The improved relative accuracy reduces the need for the tedious calibration process previously required [21]. The sensor is also waterproof, enabling real-world deployment in the future.

To accommodate the sensor array, a Printed Circuit Board (PCB) was designed and deployed on the blade surface with a maximum component thickness of 1.95 mm. The substrate is 0.2 mm Polyimide coupled with a 0.2 mm stiffener, featuring complete flexibility to adhere to the blade surface. The PCB is based on the STM32L452, and supports an array of ten LPS28DFW. The board is encapsulated using custom 3D-printed flexible rubber, see Figure 3. This is done to smoothen the surface to limit the impact of sharp edges of the PCB and electronic components on the airflow, which could skew the measurements. The encapsulation process is identical to Aerosense’s, making the system robust against rain and snow if deployed on the field [14]. The maximum thickness of the MEMS pressure measurement system is 2.4 mm, i.e., 0.96% of the blade height. The power consumption for an array of ten LPS28DFW sampling at 100 Hz is 1.6 mW. The STM32L452 adds 20 mW for computation and serial transmission. Although the LPS28DFW sensor supports a maximum sampling frequency of 200 Hz, the choice was made to retain the sampling frequency from the Aerosense system. This ensures that the results remain relevant to field installations. In the application scenario this work targets, increasing data throughput leads to a greater volume of data that must be processed and transmitted. It, in turn, results in higher power consumption and reduces system flexibility. The power consumption in a power-optimized setup would be lower than that of the Aerosense system, as it utilizes fewer sensors, each consuming less power. Therefore, the 32 W h lithium-ion battery of the Aerosense system meets the requirements [13]. A detailed description of the acquisition system, including the communication protocols used is available in the paper this work is based on [24]. In summary, the sensors are read by a microcontroller on the surface of the blade. This microcontroller communicates to a gateway inside the blade using Low Voltage Differential Signaling (LVDS). This gateway relays the data to an acquisition computer using UDP over Ethernet.

### B. PRESSURE SCANNERS

Small holes (pressure taps) of 0.8 mm in diameter are drilled through the 1:1 scale wind turbine blade to measure the surface pressure generated by the flow passing over the airfoil, which generates the lift force. These pressure taps are connected to multiplexed ESP pressure scanners from TE Connectivity, using 1.5 m long vinyl tubes with an internal diameter of 0.8 mm. They measure differences of pressure between the airfoil surface ($P_i$) and the free airstream in the wind tunnel ($P_\infty$), as defined in Equation (1).

$$ \Delta P_{\text{scanner}} = P_i - P_{\infty} \quad (1) $$
<table><caption>TABLE 2. Sensors selected for the experiments reported in Section VI.</caption><thead><tr><th>Type</th><th>Model</th><th>Quantity</th><th>Sampling Rate [Hz]</th><th>R/A Accuracy<sup>*</sup>[Pa]</th><th>Height<sup>**</sup> [mm]</th></tr></thead><tbody><tr><td>MEMS absolute pressure sensors</td><td>ST LPS28DFW</td><td>10</td><td>100</td><td>1.5 / 50</td><td>1.95</td></tr><tr><td>Pressure scanners</td><td>TE ESP</td><td>8</td><td>512</td><td>- / 7.5</td><td>0</td></tr></tbody></table>

* Relative/Absolute accuracy, ** Extra height added on top of the blade surface

<figure><img src="image_5.png" /><figcaption>FIGURE 4. Flowchart of data processing steps.</figcaption></figure>

A pressure scanner consists of an array of silicon piezoresistive pressure sensors, designed to maintain long-term stability and provide accurate measurements within ±0.03% of static errors over the full-scale pressure range. The sensors operate effectively from 0 kPa to 7 kPa near the leading edge suction peak and from 0 kPa to 2.5 kPa in other regions.

The transfer function of the whole system, i.e., tubes and sensor cavity, has been characterized offline at a sampling frequency of 1024 Hz and has been taken into account in the processing of the pressure data. During the measurements, the sampling frequency was 512 Hz. The signal acquisition was performed using two National Instrument acquisition boards synchronized using a dedicated 1 Hz signal.

## V. DATA PROCESSING AND NORMALIZATION

Prior to comparing the acquired measurements, two data processing steps are necessary as described in Figure 4. First, the data must be synchronized to ensure that the measurements are time-aligned, avoiding comparisons between different time points for the two sensors. Then, the measurements must be adapted to enable comparison between absolute (MEMS) and differential (pressure scanners) pressure sensors. The previously mentioned 1 Hz hardware signal - which is also connected to the MEMS system - is used for synchronization. Multiple steps are required to compare both types of sensors. The atmospheric pressure ($P_{atm}$) is removed from the MEMS absolute pressure sensor acquisitions in Equation (2). This is done by subtracting the mean of ten-second measurements taken under stationary flow conditions.

$$ \Delta P_{MEMS} = P_i - P_{atm} \quad (2) $$

As described in Section IV-B, the measurements from the pressure scanner are given by Equation (1), where $P_i$ is the pressure on the blade surface and $P_\infty$ is the freestream static pressure. Following the Bernoulli formula along a streamline, Equation (1) formulates as Equation (3).

$$ P_{atm} + 0 = P_{\infty} + q_{\infty} \quad (3) $$

Therefore the pressure measured by the MEMS sensors can also be written as Equation (4), where $q_\infty = 0.5\rho U_\infty^2$ is the so-called dynamic pressure, $\rho$ is the air density, and $U_\infty$ the upstream wind velocity.

$$ \Delta P_{MEMS} = P_i - (P_\infty + q_\infty) \quad (4) $$

Although the pressure scanner and the MEMS sensors measure slightly different quantities, it is possible to compare them by adding the dynamic pressure $q_\infty$ as described in Equation (5). To remove this variation, an opening is typically created in the closed airflow circuit of the wind tunnel, forcing the pressure to be in equilibrium with the outside air. This was not possible in the CSTB wind tunnel for safety reasons. Furthermore, the static pressure is measured in a dedicated box near the airfoil. Because the airfoil takes up space in the test section, it slightly reduces the area available for airflow (by up to 10% at an AoA of 24°), causing a slight increase in wind speed around the airfoil. As a result, the measured static pressure in the test section differs slightly from the undisturbed free-stream pressure upstream.

The calibration coefficient $\alpha$ accounts for systematic differences in how the reference pressure is defined in the two measurement systems. The MEMS sensors measure absolute pressure relative to vacuum, whereas the pressure scanner measures differential pressure with respect to a static reference pressure box located upstream of the blade. These different reference conditions lead to small, consistent offsets caused by factors such as temperature-induced density changes, non-uniform static pressure within the closed wind tunnel, and the location of the reference pressure box. To correct for these effects, $\alpha$ is introduced as a global calibration factor. Its value ($\alpha$ = 0.9) is derived from measurements at high angles of attack (24°), where the flow is fully separated and the surface pressure should be nearly uniform, providing a robust reference for calibration. This procedure ensures consistency between both systems and allows reproducible comparison rather than empirical curve fitting.

$$ \Delta P_{\text{scanner, from MEMS}} := \Delta P_{MEMS} + q_{\infty} \cdot \alpha \quad (5) $$

In the following results and figures, the MEMS absolute pressure sensor array results are shown with the pressure as defined here throughout.

## VI. EXPERIMENTAL RESULTS

This section presents the results in three stages. First, it details the data obtained from the MEMS sensor system. Second, it provides a comparison with the pressure scanner data.
<figure><img src="image_6.png" /><figcaption>FIGURE 5. Inverted mean and standard deviation over time of the absolute pressure sensors (in Pa) depending on the blade AoA.</figcaption></figure>

<figure><img src="image_7.png" /><figcaption>FIGURE 6. Normalized raw sensor data for pressure scanners and MEMS sensors for $x/c = 0.28$ (top) and $x/c = 0.55$ (bottom).</figcaption></figure>

Finally, it analyzes the impact of the MEMS system on the aerodynamics of the blade by comparing the acquisition process with and without the MEMS system attached to the blade surface.

## A. MEMS SENSORS SYSTEM DATA ANALYSIS

The mean and standard deviation over time of the MEMS pressure sensor array for different angles of attack is shown in Figure 5. The aerodynamic force comes mainly from a generated suction on the upper side of the airfoil (hereafter called the suction side), which means the sensors measure negative pressure relative to the atmosphere. An inversion of the y-axis on the mean measurements allows for a more direct interpretation of the generated suction and therefore of the lift forces acting on the airfoil. That way, the maximum lift generated is represented when the value reaches its maximum peak (i.e. the minimum pressure). When the AoA is increased, the suction increases (i.e., the pressure decreases) linearly with the AoA, an effect clearly visible in Figure 5 between -10° and 8°. This result is confirmed by previous experiments in the same conditions and on the same airfoil [25], where a maximum suction is found at 10° before flow separation. Above 10°, the flow on the suction side starts to separate from the trailing edge which reduces the overall suction generated on the airfoil. The flow starts to separate at the trailing edge, and as the AoA is increased, the limit (called the separation point) between the attached

flow region near the leading edge and the separated flow region near the trailing edge, moves upstream. This effect is visible from the data collected by the array of ten absolute pressure sensors, depicted in Figure 5, where the suction drops down to a local plateau at about -500 Pa, starting with the pressure sensors closer to the trailing edge. Moreover, the effect of flow separation intrinsically generates increased local fluctuations [25], which are visible with the rise in standard deviation around 12° of Figure 5. Notably, the suction decreases more slowly for the pressure sensors closer to the leading edge, e.g., $x/c = 0.28$ and $x/c = 0.34$, than for the ones to the trailing edge side, e.g., $x/c = 0.49$ and $x/c = 0.55$, between 10° and 26°. For AoA larger than 26°, the flow becomes fully separated from the airfoil and reaches another state, which can be seen by another peak of the standard deviation.

## B. COMPARISON OF MEMS SENSORS AND HIGH-ACCURACY PRESSURE SENSORS

Figure 6 compares synchronized and normalized raw signals for the pressure scanners and the MEMS sensors at $x/c = 0.28$ and $x/c = 0.55$ for AoA = 12°. These are the sensors closest to the leading edge and closest to the trailing edge respectively. The AoA was chosen because the flow is separated at the trailing edge and at the pressure tap $x/c = 0.55$, but attached at the leading edge and at the pressure tap $x/c = 0.28$. This plot provides information
<figure><img src="image_8.png" /><figcaption>**FIGURE 7.** Inverted mean pressure readings (top) and standard deviation (bottom) over time for different angles of attack for the two furthest apart sensor pairs. Dotted lines represent pressure scanner data and plain lines represent MEMS sensors.</figcaption></figure>

about the dynamic response of the sensors. The MEMS sensors follow the pressure scanner accurately at lower frequencies, i.e., when the flow is attached. However, when higher frequencies arise, i.e., when the flow is separated, the lower sampling frequency of the MEMS sensors can be observed. Nevertheless, the filtered signal from the pressure scanners is similar to the MEMS time series. While increasing the sampling frequency of the MEMS sensor could potentially enhance the correlation between the two sensor types, a sampling frequency of 100 Hz appears sufficient for capturing data when the flow is attached to the airfoil, which is the region of interest, whereas doubling the frequency would cause increased costs in terms of processing power and energy consumption.

Figure 7 presents a comparison of the sensors for time-averaged behavior analysis, it shows the mean and standard deviation for the same pair of MEMS sensors and pressure taps as Figure 6, i.e., $x/c = 0.28$ and $x/c = 0.55$ across all the AoA averaged over time. For both sensor locations at $x/c = 0.28$ and $x/c = 0.55$ the mean follows the expected linear trend for low angles of attack –10° to 8°, however, a slight offset is present. While the sensors close to the trailing edge, i.e., $x/c = 0.55$, show an offset between 35 to 70 Pa, the sensors close to the leading edge, i.e., $x/c = 0.28$, show an increasing offset from 110 to 210 Pa. The offset in the standard deviation for low AoA remains below 19 Pa.

<figure><img src="image_9.png" /><figcaption>**FIGURE 8.** Inverted mean pressure readings (top) and standard deviation (bottom) over time along the chord length for measured angles of attack between 10° and 16°. Dotted lines represent pressure scanner data and plain lines represent MEMS sensors.</figcaption></figure>

At the AoA of 10° the sensors closer to the trailing edge, at $x/c = 0.55$, show the expected decrease in the suction alongside a sudden increase in standard deviation due to the flow separation, as previously discussed. The sensors at $x/c = 0.28$ are not yet affected at 10°, showing the same expected behavior at 12°, due to the flow separation point moving towards the leading edge with increasing AoA. The largest offset between the pressure scanner and the MEMS pressure sensors at $x/c = 0.55$ with 220 Pa occurs at the same AoA as the suction peak at 10°. For larger AoA, i.e., more than 12°, the absolute suction offset between the MEMS sensors and the pressure scanner for both sensor pairs steadily decreases and only increases again after the flow fully separates at 26°. Comparing the standard deviation for larger AoA, the offset stays low around 5 Pa for $x/c = 0.55$ while fluctuating between 5 Pa and 22 Pa for $x/c = 0.28$. For the fully separated flow after 26° the offset in the standard deviation increases again. Summarizing the presented plot on the static behavior analysis of the MEMS and pressure scanner sensors, the MEMS sensors follow the behavioral trends of the pressure scanners overall, however a slight offset is present. The offset affects the sensors near the leading edge more strongly than the sensors near the trailing edge and demonstrates changing behavior for different AoA-regions: low AoA from –10° to 8° show a fluctuating or increasing offset, high AoA 12° to 24° show a decreasing offset, very high AoA above 26° show an increasing offset again.
Figure 8 presents the mean and standard deviation over the chord length for the angles of attack between 10° and 16° for the MEMS sensors and pressure scanner. The markers show the exact positions of the MEMS sensors and the pressure taps along the chord. It can be seen that for an $AoA = 10°$ and $AoA = 12°$ the pressure gradient is constant for each angle and follows the same slope for both the MEMS array and the pressure scanner. For all the AoA, there is a plateau around -500 Pa. However, it starts closer to the leading edge as the AoA increases. This is produced by the region of separated flow.

Comparing the presented mean and standard deviation of the MEMS and pressure scanner sensors, it is observed that an increase of the AoA within the presented selection leads to a reduction of the offset, as well as that differences appear worse near the leading edge than near the trailing edge, just as deduced from Figure 7. However, it is more complex to estimate a clear difference between the measurements along the chord due to the different positions of the MEMS sensors and pressure taps. For example, around the position $x/c = 0.31$ some of the biggest differences seem to occur between the measurements. However, this blade region is covered by a limited number of pressure taps, only allowing a limited comparison of the linear spatial approximations. On the bottom plot, the standard deviation graph indicates that both systems equally capture the separation point moving from the trailing edge towards the leading edge for higher AoA during the flow separation, shown by a gradual shift of a bumpy peak in the distribution. Already for 10° an increase of the standard deviation towards the trailing edge can be observed, hinting at a separation point slightly outside of the chord scope. The intermittent separation point, defined as the location of the peak fluctuation maximum, can be estimated at roughly the same locations for the MEMS and pressure scanner sensors, despite the pressure scanners featuring more extreme peaks. Due to the sparse and different distribution of the sensors, a more detailed comparison of the intermittent separation point locations is restricted. However, similar trends and relations are evident.

As explained in Section IV, not all the MEMS sensors have a pressure tap in their vicinity. However, a clear matching trend between the two types of sensors can be observed. The behavior is similar to the one observed in [25]. These plots show that it is possible to rely on low-power compact MEMS sensor data for steady pressure analysis.

### C. IMPACT OF THE MEMS MEASUREMENT SYSTEM ON THE BLADE AERODYNAMICS

To evaluate the relevance of adding sensor nodes on wind turbine blades, it is important to estimate the impact of such devices on the aerodynamics in their vicinity. This provides an idea of the changes in the generated lift caused by such a monitoring system installed on an airfoil. The encapsulation of the system, described in Section IV, slowly increases in thickness from its edge until it reaches the thickness of

<figure><img src="image_10.png" /><figcaption>**FIGURE 9.** Inverted mean (top) and standard deviation (bottom) of pressure scanners between $x/c = 0.28$ and $x/c = 0.55$ for AoA between -10° and 28°. Full lines represent data gathered before mounting the MEMS sensor system and dotted lines represent the data when the MEMS system is added on the surface of the blade, next to the pressure taps as described in Figure 3.</figcaption></figure>

the MEMS pressure sensors, i.e., 1.95 mm. The housing is 48.5 cm long, 8.5 cm wide, and 2.4 mm thick.

Figure 9 describes a comparison between data acquisition from the pressure scanner before and after the additional installation of the MEMS sensor array on the blade. The pressure taps of the pressure scanner are located parallel to the encapsulation of the system, at 8.5 mm from the edge. It shows the mean and standard deviation for the pressure over different angles of attack for a selection of locations along the chord between $x/c = 0.28$ and $x/c = 0.55$. Looking at the upper plot, one can observe that the two acquisitions share very similar results before the start of flow separation at the trailing edge around ($AoA = 10°$). With the addition of the MEMS sensors on the blade, during the transition from a fully attached flow ($AoA < 10°$) to a fully separated flow ($AoA > 26°$), the decreasing slope of the pressure seems more abrupt, while the AoA where the maximum of suction occurs stay identical.

Observing the standard deviation, for low angles of attack and an attached flow ($AoA < 8°$) the measurements match closely. However, then the acquisition including the addition of the MEMS sensor system appears to show earlier symptoms of flow separation, presenting earlier peaks than the acquisition without the added system. This offset goes from 0° for $x/c = 0.49$ to 1° for $x/c = 0.44$. Towards
<table><caption>TABLE 3. Average error for mean and standard deviation over all angles of attack for $x/c = 0.28$ and $x/c = 0.55$ as described in Figure 7.</caption><thead><tr><td>Sensor position</td><td>Mean error (%)</td><td>Standard deviation error (%)</td></tr></thead><tbody><tr><td>x/c = 0.28</td><td>7.5</td><td>5.5</td></tr><tr><td>x/c = 0.55</td><td>2.3</td><td>3.3</td></tr></tbody></table>

high angles of attack both standard deviation measurements peak again at the AoA = 26° where the flow becomes fully separated. The acquisition with the MEMS system on top exhibits higher peaks of fluctuations than the acquisition without them.

## VII. DISCUSSION

From the MEMS sensors alone, it can be seen that it may be possible to infer the AoA and the flow separation of an operating wind turbine with low-cost and low-power pressure sensors measuring directly from the blade surface. Moreover, Figure 5 suggests that there is a strong correlation among the sensors, indicating a potential redundancy in the measurements. Results are detailed in Table 3.

The comparison between the MEMS sensor array and the pressure scanner shows a small but varying offset between the two sensing approaches. Table 3 shows that for $x/c = 0.55$, the average error over the AoA is 2.3% for mean and 3.3% for standard deviation. However, the error is higher for the sensor at $x/c = 0.28$. There are a variety of possible causes for this. Aside from the slight differences in sensor positions and potentially slightly lower accuracy of the MEMS sensors compared to the pressure scanner, aerodynamic causes are also not unlikely. On one side, the housing and the encapsulation of the MEMS sensor array may be affecting their readings or the aerodynamics of the blade. It is also possible that the assumptions made in Section V to enable the comparison between the MEMS sensors and the pressure scanner may not entirely hold. In particular, small inaccuracies in the measured wind velocity and calculated air density may cause the offset observed. Alternatively, it is possible that the MEMS sensors are not impacting the aerodynamics of the blade itself, but rather affecting the readings recorded by the pressure scanner due to their proximity, while the blade aerodynamics in itself remains largely unchanged.

The results of the comparison between the pressure scanner measurements with and without the presence of the MEMS sensors indicate that flow separation may be triggered slightly faster when the MEMS sensor array is installed than for a clean blade. However, when the flow starts to separate, the flow becomes more three-dimensional, and the flow is no longer entirely uniform along the span. Therefore the local extra thickness due to the encapsulation of the sensor may trigger 3D structures in the flow, which may or may not exist without the extra thickness. The influence of the sensors' housing may be reduced with a system that fully covers the blade and does not create a local extra thickness, therefore increasing its cross-section. However, this increase in cross-section is in itself undesirable.

It is therefore possible that part of the observed offset between the MEMS and pressure scanner measurements originates from local aerodynamic interference caused by the encapsulation. The encapsulation slightly alters the surface profile and may influence the flow in its vicinity. Ideally, the MEMS sensors would be fully integrated into the blade surface to eliminate this effect, but such an approach would significantly increase complexity and cost. The present work instead evaluates the complete MEMS-based measurement system as a whole, demonstrating the feasibility and measurement quality of using compact surface-mounted patches for aerodynamic monitoring.

Overall, the results suggest that MEMS pressure sensor arrays are a promising approach to monitoring wind turbine blades, and provide comparable results to high-accuracy pressure scanners in a wind tunnel environment.

## VIII. CONCLUSION

Monitoring wind flow and operational parameters of wind turbines directly in the field is a fundamental prerequisite for optimizing sustainable electrical generation efficiency. The aerodynamics of the rotating blades is of particular interest. However, many technical and scientific challenges still exist due to the complexity of measurement system installations. This study successfully demonstrated that a non-invasive, surface-mounted MEMS sensor array can provide accurate and reliable aerodynamic data on a full-scale wind turbine blade under realistic operational conditions. Our results show that this low-cost system achieves a measurement error as low as 2.3% compared to high-accuracy pressure scanners, while having a minimal aerodynamic impact that shifts the onset of flow separation by only 1°, confirming that the 2.4 mm encapsulation thickness is a viable trade-off between component protection and aerodynamic efficiency for long-term deployment. Critically, the system's ultra-low power consumption of 1.6 mW confirms its suitability for the long-term, self-sustaining deployments required for effective in-field monitoring. These findings have significant implications for the wind energy industry. By validating a practical and affordable monitoring solution, this work helps bridge the gap between controlled wind tunnel experiments and the demands of real-world operation. The ability to deploy dense sensor arrays directly on blades opens the door for advanced, data-driven control strategies that adapt to real-time flow conditions. This could lead to improved predictive maintenance by detecting aerodynamic anomalies early, ultimately increasing the annual energy production and operational lifetime of wind farms. While this work provides a strong validation, it also highlights clear directions for future research. A key area for future investigation is the optimization of the sensor array itself, exploring the trade-offs between sensor density and placement to identify the minimal configuration required for accurate aerodynamic state estimation. Also, the system's robustness must be tested under more complex, unsteady aerodynamic conditions, such as turbulent gusts and dynamic stall, which are characteristic
of field operations. Finally, long-term field trials are essential to assess the system's durability against environmental factors like icing and erosion and to integrate its data stream into machine learning models for real-time AoA estimation and structural health monitoring.

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<figure><img src="image_11.png" /></figure>

**NICOLAS SCHÄRER** (Graduate Student Member, IEEE) received the M.Sc. degree in electrical engineering and information technology from INSA Rennes, France, in 2022. He is currently pursuing the Ph.D. degree with the D-ITET Center for Project-Based Learning (PBL), ETH Zürich, Switzerland. From 2019 to 2022, he was an Application Support Engineer Intern with STMicroelectronics. His research interests include hardware-software co-design, ultra-low power embedded systems, and their application in demanding environments.

<figure><img src="image_12.png" /></figure>

**DENIS MIKHAYLOV** received the B.Sc. and M.Sc. degrees in electrical engineering and information technology from ETH Zürich, Zürich, Switzerland, in 2021 and 2022, respectively. He is currently an Embedded Systems Engineer with RTDT Laboratories, Zürich, Switzerland. His professional interests include embedded systems design, hardware-software co-design, sensor systems, and structural health monitoring (SHM).
<figure><img src="image_13.png" /></figure>

**CÉDRIC SIEVI** received the B.Sc. and M.Sc. degrees in electrical engineering and information technology from ETH Zürich, Zürich, Switzerland, in 2022 and 2024, respectively. He was a DevOps Engineering Internship with Swisscom, where he worked on network validation using deep packet inspection. His professional interests include computer networks, DevOps, and embedded systems for industrial and energy applications. For his master's thesis on exploring novel sensors for wind turbine blade monitoring, he was awarded the Best PBL Master Thesis of 2024.

<figure><img src="image_14.png" /></figure>

**SARAH BARBER** received the M.Eng. degree in mechanical engineering from the University of Cambridge, Cambridge, U.K., in 2004, which included a year of study in aeronautical and astrophysical engineering with Massachusetts Institute of Technology (MIT), Cambridge, MA, USA, and the Ph.D. degree in mechanical engineering from The University of Sheffield, Sheffield, U.K. She is currently the Head of the Wind Energy Innovation Division with the Eastern Switzerland University of Applied Sciences (OST), Rapperswil-Jona, Switzerland. Her research interests include wind energy technology, renewable energy systems, and aerodynamics.

<figure><img src="image_15.png" /></figure>

**BADOUI HANNA** received the B.Eng. degree in mechanical engineering from the Lebanese University, Tripoli, Lebanon, and the M.Sc. degree in mechanical engineering from the Université Grenoble Alpes, Grenoble, France, in 2022. He is currently pursuing the Ph.D. degree with the Scientific and Technical Center for Building (CSTB), Nantes, France. His research interests include aerodynamics, fluid dynamics, wind energy, and stall phenomena in wind turbine blades.

<figure><img src="image_16.png" /></figure>

**TOMMASO POLONELLI** (Senior Member, IEEE) received the M.Sc. and Ph.D. degrees in electronic engineering from the University of Bologna, Bologna, Italy, in 2017 and 2020, respectively. He is currently a Postdoctoral Researcher with ETH Zürich, Zürich, Switzerland. He has collaborated with several universities and research centers, such as University College Cork, Cork, Ireland, and Imperial College London, London, U.K. He has authored over 40 papers in international journals and conferences. His research interests include wireless sensor networks, the IoT, autonomous unmanned vehicles, power management techniques, structural health monitoring, and the design of ultra-low power battery-supplied devices with onboard intelligence.

<figure><img src="image_17.png" /></figure>

**CAROLINE BRAUD** received the Ph.D. degree in fluid dynamics from University of Poitiers, Poitiers, France, in 2003. She was an Associate Researcher with Syracuse University, Syracuse, NY, USA, for two years. She joined CNRS in 2005 and was assigned to the LML Laboratory, Lille, France. In 2015, she moved to the LHEEA Laboratory, Ecole Centrale Nantes, Nantes, France, to work on wind energy. Along that, she started working with CSTB, Nantes, where she is responsible for wind energy research development. She is currently a Senior CNRS Researcher. Her research interests include aerodynamics, turbulent shear flows, turbulent flow control, fluidic actuators, and wind turbine applications.

<figure><img src="image_18.png" /></figure>

**MICHELE MAGNO** (Senior Member, IEEE) received the master's and Ph.D. degrees in electronic engineering from the University of Bologna, Bologna, Italy, in 2004 and 2010, respectively. Currently, he is a Privatdozent with ETH Zürich, Zürich, Switzerland, where he is the Head of the Project-Based Learning Center. He has collaborated with several universities and research centers, such as Mid University Sweden, where he is a Guest Full Professor. He has published more than 150 articles in international journals and conferences, in which he got multiple best paper and best poster awards. The key topics of his research are wireless sensor networks, wearable devices, machine learning at the edge, energy harvesting, power management techniques, and extended lifetime of battery-operated devices.

<figure><img src="image_19.png" /></figure>

**JULIEN DEPARDAY** received the M.Sc. degree in mechanics from ENSTA Bretagne, Brest, France, in 2010, and the Ph.D. degree in fluid mechanics from the Université de Bretagne Occidentale, Brest, in 2016. He was a Senior Scientist with the Institute for Energy Technology, OST–Eastern Switzerland University of Applied Sciences. He is currently the CTO of RTDT Laboratories. He is specialized in experimental fluid dynamics, with a strong emphasis on field-measurements and unsteady aerodynamics. He developed several in-field measurement systems, implying interdisciplinary projects with electronics experts. His research interests include fluid-structure interactions, flow separation, and inviscid flow theory for naval engineering, wind energy, and aeronautics.