The stakes are enormous. Ireland’s 2030 goal of 80 percent renewable electricity depends on wind turbines, but aging infrastructure and harsh climates cause blades and towers to develop structural problems that produce fluctuating loads and diverse operational stresses. Early-stage damage is often subtle, making detection challenging, and maintenance costs can climb to 40 percent of operational costs. Conventional condition-monitoring techniques such as strain gauges, acoustic emission sensors, and fiber optics face problems including difficult accessibility, especially in offshore farms, sensors detaching due to bad weather, and high costs. Wireless sensor networks combining transducers, signal conditioning circuits, and processing software offer an alternative, but they demand high sampling rates and synchronization, which can be mitigated using advanced digital signal processing.

Direct detection methods such as visual inspections, X-rays, or ultrasonic testing are considered very reliable and accurate, but they are costly, require large numbers of expensive sensor arrays, and produce complicated data management. Indirect detection is cheaper and can detect small deflections such as changes in stiffness or damping with fewer sensors, but it is not as reliable. Structural health monitoring has to find a compromise between cost and performance to minimize downtime. RF-based radars with different antenna types and frequency bands promise high-accuracy detection while avoiding these problems entirely, because the radar never touches the blade.

The researchers examined a progression of antenna technologies for radar-based monitoring. In one study, eight 24 GHz mmWave sensors mounted on the tower continuously measured the tower-blade-tip clearance. From each sensor’s input into the nacelle rotation coordinate system and least-squares fitting, the researchers computed the minimum clearance and utilized it in a feedforward pitch-control loop to increase clearance by 30 percent in field tests. This demonstrated that mmWave radar can follow blade deflection in real-world settings. Another team developed a wireless IoT acoustic monitoring system for wind turbine blades, handling blade rotation, cabling, remote deployment, and antenna performance. Their field studies showed that omnidirectional antennas perform better since directional antennas lose communication when the blade moves, though sporadic signal loss highlighted the need for more reliable high-gain wireless connectivity.

A series of studies explored ultrawideband (UWB) sensing in the lower frequency bands of 2.82 to 4.97 GHz, 3.1 to 5.3 GHz, and 3.1 to 5 GHz, pairing a tip antenna with root antennas to estimate blade tip-root distances and deflection. UWB is a low-power radio technology that utilizes a broad spectrum, generating extremely brief temporal pulses because of its extensive bandwidth. These short pulses provide precise determination of the initial signal’s Time of Arrival, enabling localization within centimeters. The lower band was chosen because RF components and pulse modules are cheaper, less lossy, and commercially available. One configuration used two horn root antennas as receivers and one internal corner-reflected monopole tip antenna as transmitter, powered by a pulse generator, with deflections estimated via time-domain pulse measurements using delay-based distance estimation and triangulation. A later version replaced the monopole with an external Vivaldi tip antenna, and simulations optimized tip antenna polarization and root antenna positions to enhance signal strength and limit multipath interference in the lossy fiberglass blade.

Practical deployment positions all UWB electronics, including the pulse generator and RF circuits, at the blade root, where power is available. The tip antenna receives signal from an internal connection. To protect the in-blade tip antenna, an external diverter strip sends lightning strike energy to a grounded receptor without affecting the radio link. Cross-turbine signal overlap is negligible due to highly directed tip and root antennas, low UWB transmit power, far-dispersed turbines, and blade rotation and yawing. Researchers evaluated in-blade UWB with dielectric lens tip antennas for multipath interference from surface and leaky waves, using absorber material to reduce multipath and improve pulse-front fidelity and tip-root distance precision. In models and full-blade experiments with restricted in-blade space, lightning protection, ice, water, and temperature fluctuations, optimal absorber placement and thickness increased direct-pulse amplitude, stretch ratio, and multipath reduction. Later studies recommended high-gain tip antennas for UWB sensing accuracy and early detection of significant deflections to prevent tower strike, and proposed a vertically polarized tip antenna with metal directors to improve endfire gain and lightning protection.

Researchers used the finite-difference time-domain (FDTD) numerical method to analyze UWB signal propagation along a 37.3-meter wind turbine blade, comparing simulations with real measurements to see how well FDTD predicts pulse magnitude and delay. FDTD simulation showed that multipath makes signal propagation over blades more complicated than ordinary wireless links due to directed waves and reflections. Recently, a microwave SHM method was developed for grouted connections of monopile-based offshore wind turbines, which are prone to cracking, particle washout, and long-term erosion. Guided electromagnetic waves propagating along the grout and steel interface were monitored with a 100 MHz to 2 GHz stepped-frequency CW UWB radar with permanently attached antennas to detect crack formation and erosion during large-scale fatigue testing. Although useful, UWB approaches have drawbacks: their longer wavelengths of 28 to 96 mm limit angular resolution, making them less effective at detecting small surface defects like cracks or corrosion, and their low power spectral density limits range to under 50 meters, far below the 1 to 5 km needed to monitor Ireland’s offshore wind farms.

Previous SHM investigations used continuous wave and Doppler radar at low or fixed frequencies to quantify vibration, requiring considerable offset calibration. These methods lacked imaging, range resolution, and internal defect localization. Researchers conceptualized, simulated, and detected wind turbine Doppler fingerprints using C-band and K-band radars, finding Doppler flashes, tip halos, and curved spectrogram features that proved higher radar frequencies improve Doppler resolution. But short-range, fixed-position Doppler radars measure velocity signatures only, and since they cannot provide range, deflection, deformation, or structural-localization information, they are unsuitable for continuous remote monitoring. Frequency Modulated Continuous Wave (FMCW) radar is preferred among Doppler radars due to its enhanced spatial resolution, phase-based displacement sensitivity, and accurate fault localization, detecting range, velocity, and vibration with high penetration and resolution.

Researchers designed a sparse mmWave antenna array operating at 33.4 to 36 GHz with a Y-shaped transmit-receive configuration for radar-based SHM. Sparse arrays localized faults cheaper and with less data than fully populated arrays using stepped-frequency CW radar and back-projection photography, though the method involves powered on-blade antennas and demands strong understanding of the blade’s heterogeneous permittivity, which can cause large localization errors. For the first time, mmWave imaging FMCW-based radar was used for SHM of wind turbine blades using synthetic aperture imaging, with horn antennas along the tower projecting electromagnetic waves at the rotor blades. This provides non-contact glass-fiber blade inspection and penetration at 24 to 25.6 GHz and 24 to 24.25 GHz. Differential signal processing revealed corrosion and water in rotor tips. Larger bandwidth detects multipath reflections inside the blade, improving radar resolution over a narrow one that produces a single merged peak. A 24 to 25.6 GHz FMCW radar with 24 dBi horn antennas detected faults in a laboratory demonstrator utilizing spinning glass-fiber samples, with vertical scanning possible while modifying rotor pitch and speed. Typical surface faults may be remotely identified, though ambient elements like temperature change range profiles and require future compensatory measures.

The field then progressed from proof-of-concept imaging to more practical SHM methods, introducing permanent 24.05 to 24.55 GHz and 33.4 to 36.0 GHz FMCW radars with time-of-flight range profiles and a simple root-mean-square damage indicator for laboratory SHM with real structural damage. Both studies were lab-validated, used commercial horn antennas, and lacked field-level challenges like environmental changes, blade pitch variation, long-range deflection tracking, or continuous in-service monitoring. Researchers then achieved the first in-field inverse synthetic aperture radar (ISAR) imaging of whirling blades by deploying two high-power FMCW radars at 24 GHz and 33 to 36 GHz on a 2-MW turbine tower 95 meters high. They obtained radar pictures with front- and back-wall echoes using I/Q extraction and range-profile creation, indicating full blade penetration throughout operation. Tower-mounted radars see blades at large standoff distances, so rotation, pitch, and environmental fluctuation substantially affect their signals. The researchers automated blade-passage detection using RMS curves and a moving-average filter to smooth the RMS signal and detect each blade as it crosses the radar beam, guaranteeing that only synchronized pictures are used for imaging. This work shows important viability, but the tower-mounted ISAR technique is noisy and geometrically unstable, requiring complicated synchronization filters and generating inconsistent findings across rotations. Embedded 57 to 64 GHz RadCom sensors in blade laminates enabled direct quality inspection and material-level SHM, with 3D simulations optimizing sensor placement and defect detection including delamination and fiber failure, though full-scale and long-term deployment remains untested. A 77 GHz MIMO-SAR system was proposed to improve tower deformation measurements, and a 79 GHz circularly polarized microstrip array was utilized for tower collision detection, improving signal-to-noise ratio and reducing interference via polarization.

Advanced digital signal processing along with machine learning is an important tool for improving detection by reducing noise and correcting displacement in low-SNR environments. Machine learning enhances defect detection, with k-medoid clustering achieving 81.21 percent accuracy for micro-cracks in GFRP blades. Gaussian filters and k-medoids clustering improve delamination detection, while phase-based algorithms address high-frequency clutter and weather interference. Despite these advances, mmWave radars face challenges such as atmospheric attenuation, multipath interference, and dynamic load effects in composites. Internal antenna placement increases signal loss, and MIMO configurations raise costs. Environmental factors such as yaw misalignment, ice accumulation, and harsh weather, as well as drone inspection limitations, further challenge scalability.

The researchers conclude that RF-based radar systems, particularly FMCW radars in UWB and mmWave bands, offer a promising non-contact technique for wind turbine blade deflection monitoring. Vivaldi, horn, and microstrip array antennas have been tested to improve spatial resolution, beam focusing, and detection accuracy. UWB systems have long-range coverage but low spatial resolution, while mmWave systems have higher resolution but are more attenuated. Selecting and optimizing antenna types, combinations, and positioning is essential for precise detection at low cost and complexity. Future research may focus on real-time analysis of high-frequency radar data requiring excellent DSP and machine learning algorithms, especially in dynamic operational contexts, hybrid radar systems using UWB and mmWave bands or MIMO to improve detection accuracy and robustness, compact high-gain antennas, hybrid FMCW-MIMO radars, and low-power modular systems for scalable, reliable SHM of wind turbines. To be used in large-scale applications, environmental robustness, antenna optimization, and signal processing development must continue.

Subject of Research: Overview of RF-based structural health monitoring of wind turbine blades

Article Title: Overview of RF-based structural health monitoring of wind turbine blades

Article References: Saleh, S., Saeidi, T., Timmons, N., Razzaz, F., & Hamad, Q. S. (2026). Overview of RF-based structural health monitoring of wind turbine blades. Frontiers in Antennas and Propagation, 3, Article 1755037. https://doi.org/10.3389/fanpr.2025.1755037

Image Credits: AI Generated

DOI: 10.3389/fanpr.2025.1755037

Keywords: Overview, RF-based, structural, health, monitoring, wind, turbine, blades, scientific research