Abstract
The rapid upscaling of wind turbines to multi-megawatt capacities has introduced significant structural flexibility challenges, rendering vibration mitigation a governing design constraint. This paper provides a critical review of state-of-the-art structural control strategies—passive, active, and semi-active—applied to turbine blades and towers. While passive systems remain the industry standard due to mechanical simplicity, this review highlights their susceptibility to frequency detuning under variable operational conditions. Conversely, active control offers superior vibration suppression but is constrained by high parasitic power consumption and reliability concerns. Consequently, the review identifies semi-active control as the optimal solution for next-generation turbines, balancing high-performance adaptability with fail-safe reliability. The paper concludes that future developments must focus on adaptive semi-active strategies for floating offshore and multi-hazard environments, advocating for a paradigm shift towards control co-design to reduce the levelized cost of energy.
Keywords
1. Introduction
The escalating global urgency regarding climate change has provided a strong impetus to reduce carbon dioxide emissions. With energy-related carbon emissions constituting nearly 66% of total greenhouse gas emissions, 1 there is a critical international paradigm shift towards renewable energy production. Within this context, wind energy has distinguished itself as a cornerstone of the global energy transition, offering a clean, sustainable solution that is compatible with existing land-use activities such as agriculture, while maintaining low pollutant emissions.
Over the past decade, the wind energy sector has witnessed unprecedented expansion. As illustrated in Figure 1, global cumulative wind power capacity surged from approximately 180 GW in 2010 to 910 GW in 2022.
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By 2023, the industry surpassed the landmark of 1 TW of installed capacity.
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In 2024 alone, the sector installed a record 117 GW, bringing the global total to approximately 1.14 TW.
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This capacity growth is mirrored in generation figures; wind electricity generation rose by nearly 10% in 2023 to reach over 2,330 TWh, representing the highest growth among all renewable technologies.
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Global cumulative wind power capacity growth
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To achieve these capacity targets, the industry has aggressively pursued a strategy of technological upscaling which is reflected in the early benchmark machines, such as the NREL 5-MW reference turbine. 6 In contrast, modern commercial turbines have vastly increased in size to capture wind at higher altitudes and maximise swept area. For instance, the Vestas V162-6.2 MW features a rotor diameter of 162m and hub heights up to 169m. 7 This trend is even more pronounced in the offshore sector. Forecasts suggest that annual offshore installations could grow from approximately 8 GW to over 34 GW by 2030. 3 Contemporary offshore prototypes, such as the Siemens Gamesa SG 14-222 DD (222m rotor) and the GE Haliade-X (14-MW, 220m rotor), are engineering colossi capable of powering tens of thousands of households individually.8,9 These advances underscore why wind energy is expected to constitute 95% of all renewable capacity additions through 2030. 5
The pursuit of larger rotor diameters has necessitated the use of highly flexible, lightweight blades and slender towers. While aerodynamically efficient, these structural characteristics significantly lower the inherent structural damping and natural frequencies of the system, rendering modern multi-megawatt turbines prone to severe vibration under complex loading conditions.10,11
Turbine blades predominantly deform in two primary modes: flapwise (out-of-plane) and edgewise (in-plane). Flapwise bending is induced by direct wind loading. Flapwise vibrations are typically stabilised by significant aerodynamic damping under normal operation. 12 However, under stall conditions or aeroelastic instabilities, resembling aircraft wing flutter for example, these oscillations can grow uncontrollably, risking tower strikes. On the other hand, edgewise bending represents a critical vulnerability. Unlike flapwise motion, edgewise vibrations occur within the rotor plane and possess very low, or near-zero, aerodynamic damping. In critical scenarios, such as stall or emergency shutdowns, aerodynamic forces can provide negative damping, effectively feeding energy into the system rather than dissipating it. 10 Documented failures, such as the APX-40 turbine incident, 13 highlight that divergent edgewise oscillations can lead to catastrophic blade failure even in parked or idling turbines.
The tower structure, however, is subjected to periodic excitation from the rotor, most notably the 3-per-rev (3P) blade passing frequency in three-bladed turbines. As each blade passes the tower, it induces a torque reduction of 2–3% and a pulse of aerodynamic loading. 14 Despite design standards (IEC 61400-1:2019) mandating frequency separation, the trend towards taller, softer towers makes avoiding resonance increasingly challenging. If the tower’s natural frequency aligns with the 1P or 3P harmonics, resonant amplification occurs, accelerating fatigue damage in welds and bolted connections. Post-mortem analyses have attributed several recent tower collapses specifically to excessive vibration during strong wind events. 15
Beyond wind loading, turbines installed in seismically active regions face a multi-hazard threat profile not fully addressed by traditional standards. While IEC 61400-1 provisions consider earthquake loads, they have historically treated them as secondary to wind loads. Recent literature suggests that this is a dangerous oversight for large flexible turbines. Studies indicate that the combined “aero-seismic” loading, simultaneous wind and ground shaking, can generate base shear and overturning moments that exceed the envelopes of conventional design cases.12,16 The interaction is complex; a flexible tower already excited by wind may experience amplified displacements during an earthquake, pushing the structure beyond its elastic limit.
The impact of these vibrations extends beyond immediate structural safety to significant economic losses. Fatigue damage with cyclic stresses drastically shortens the remaining useful life of components. Also, power curtailment where flexible blades undergo large deflections reduces the effective rotor swept area. Ahlström (2006) demonstrated that tip deflections exceeding 10% of blade length can result in power losses approaching 20%. 17 Moreover, reduced availability with severe vibrations frequently triggers protective shutdowns. In cold climates, the combination of icing and vibration has been shown to reduce power output by up to 40%. 18
In summary, vibration is no longer merely a serviceability issue but a governing design constraint. The susceptibility of modern turbines to edgewise instability, resonant tower coupling, and multi-hazard excitation necessitates the integration of advanced structural control systems to ensure long-term reliability.
While several existing review articles have documented the application of vibration mitigation devices in wind turbines, they frequently exhibit distinct limitations. Previous comprehensive reviews have primarily focused on classifying damper types or have evaluated passive, active, and semi-active systems in relative isolation. Furthermore, the existing literature often overlooks the compounded multi-hazard threats—such as combined aero-seismic loading and floating platform dynamics—that increasingly define contemporary offshore installations and exceed conventional design envelopes. Consequently, a critical research gap persists which is a lack of a holistic, comparative synthesis that evaluates all three structural control paradigms specifically against the stringent commercial and operational constraints of next-generation turbines.
To bridge this gap, this paper provides a comprehensive critical analysis of the state-of-the-art in structural control systems, systematically categorising methodologies into passive, active, and semi-active strategies applied to both blades and towers. Beyond a descriptive taxonomy, the unique contribution of this work is its rigorous comparative study. It evaluates each strategy against the competing metrics of vibration reduction efficiency, power consumption, and reliability to identify the optimal control paradigm for the next generation of utility-scale wind energy. By critically synthesising the most significant recent literature through this techno-economic lens, this review advocates for a necessary paradigm shift towards Control Co-Design (CCD) to reduce the levelized cost of energy.
The structural control strategies reviewed in this work are classified in Figure 2 according to their actuation mechanism and application domain. This taxonomy provides a conceptual framework that distinguishes passive, active, and semi-active approaches for both tower and blade systems, establishing the basis for the comparative analysis that follows. Wind turbines structural control strategies flowchart.
2. Passive control of wind turbines
Passive control strategies represent the established baseline for vibration mitigation in wind energy systems. Characterised by their capacity to dissipate energy without recourse to external power sources or sensor networks, these systems operate by transferring vibrational energy from the primary structure to a secondary oscillating system or through inherent material damping. Historically, such methods have been favoured in onshore applications due to their robustness and straightforward implementation at the tower or nacelle level and also blades. However, contemporary literature increasingly acknowledges that the reliance on single-mode tuning renders passive devices sensitive to ‘detuning’. In this context, long-term effectiveness may be eroded when the structural operating point shifts due to turbulence intensity, component ageing, or multi-hazard inputs. Consequently, recent scholarship has revisited classical Tuned Mass Damper (TMD) mechanics, developing nonlinear and spatially optimized configurations that facilitate integration within the nacelle for global tower vibration control and inside wind turbine blades for localized edgewise suppression, as depicted in Figures 3 and 4. Tmd inside of blade. Passive TMD mounted inside nacelle.
2.1. Turbine tower
The tower structure acts principally as a cantilever beam fixed at the foundation and carrying a heavy tip mass (the rotor-nacelle assembly). Consequently, the structural response is dominated by the first bending modes in the Fore-Aft (FA) and Side-to-Side (SS) directions. While traditional passive control relies on a single tuned mass damper (STMD) placed at the nacelle, recent literature indicates that this approach is increasingly insufficient for modern, large-scale turbines due to frequency sensitivity and spatial constraints.
A significant portion of recent research addresses the limitations of STMDs, particularly their sensitivity to frequency detuning caused by environmental factors. A critical variable often overlooked in earlier designs is Soil-Structure Interaction (SSI). Gaur et al. (2020) demonstrated that disregarding SSI leads to an overestimation of the tower’s fundamental frequency. 19 In their simulations, accounting for soil flexibility caused the fundamental frequency to drop by approximately 25%, rendering a fixed-base tuned STMD ineffective. To counter this, they proposed distributed multiple TMDs (d-MTMDs) placed at the top three nodes of the tower. Their parametric study recommended a mass ratio of 5% and a damping ratio of 7%, which maintained robust performance despite the frequency shift caused by SSI.
The placement strategy of these dampers is equally critical. While placing the damper at the location of maximum displacement (the nacelle) is the standard approach, McNamara et al. (2024) argued for distributing TMDs along the tower height based on mode shape analysis. In their investigation of a 5-MW monopile turbine, they optimized a system where TMDs were placed at the tower top (87.6 m), as well as at mid-span locations (55 m and 26 m). Their results highlighted a distinct trade-off: while a single tower-top TMD with a 1% mass ratio achieved a Root Mean Square (RMS) displacement reduction of 3.5% in the FA direction during operation, a higher mass ratio configuration (5% split across two dampers) was superior for load mitigation during parked or idling states. Notably, this configuration reduced the tower base moment by 43.6% in the SS direction and 20.8% in the FA direction. 20
Similarly, Colherinhas et al. (2021) utilised genetic algorithms to optimise a pendulum TMD for a 5-MW offshore turbine. Their optimisation indicated that while standard designs often focus on peak displacement, the critical parameter for offshore longevity is fatigue load reduction. Their optimised PTMD achieved a reduction of over 20% in response peaks for high wind velocities in the FA direction, further validating the need for precise tuning in offshore environments. 21
A recurring challenge with traditional pendulum TMDs is the physical space required to achieve the necessary natural frequency, particularly for low-frequency offshore towers
Furthermore, purely linear dampers may struggle with the broad frequency bandwidth of wind excitation. Pourjafar et al. (2025) introduced a nonlinear Magnetic Vibration Absorber (MVA) that utilises repulsive magnetic forces to provide nonlinear stiffness and eddy currents for damping. 23 This bi-directional device offers a distinct advantage in compactness; numerical simulations on a 10MW turbine demonstrated that the magnetic damper required 28–31% less stroke length than a traditional TMD to achieve comparable RMS reductions (approx. 0.64 m for both devices).
Moving beyond mass-spring systems, recent studies have investigated the efficacy of liquid and granular energy dissipation. Yusuf et al. (2024) explored the Tuned Liquid Damper (TLD) using a coupled fluid-structure interaction analysis. Their parametric study on a 5-MW turbine revealed a non-linear relationship between mass ratio and effectiveness. A single TLD with a 4% mass ratio reduced lateral deformation by 7.32% (fixed base). However, increasing the configuration to three TLDs with a cumulative 12% mass ratio resulted in a disproportionately higher reduction of 48.73% for fixed base conditions and up to 71.45% when soil stiffness was accounted for. Crucially, this configuration was estimated to extend the tower fatigue life by approximately 36%. 24 In the domain of granular mechanics, Prasad et al. (2022) investigated Honeycomb Damping Plates (HCDP) filled with granular materials (sand, stone powder, and tungsten). 25 Unlike TLDs which rely on sloshing resonance, these particle dampers dissipate energy through inter-particle friction and collision. Their experimental results on a scaled stator component indicated that a multi-unit HCDP configuration attached to the outer wall could achieve broadband damping with a vibration amplitude reduction of up to 12 dB, proving particularly effective for higher-frequency structural modes that TLDs or TMDs might miss.
2.2. Turbine blades
Controlling blade vibration presents a distinct set of challenges compared to towers. Blades are subject to complex aeroelastic coupling, significant centrifugal stiffening, and severe spatial constraints within the airfoil profile. While flapwise vibrations (out-of-plane) often benefit from high aerodynamic damping, edgewise vibrations (in-plane) possess very low—and occasionally negative—aerodynamic damping, rendering them a primary source of fatigue failure and instability. Consequently, recent literature has shifted focus from simple tuning to geometric optimisation and hybridisation to fit effective dampers within the limited blade interior.
While edgewise vibration control addresses fatigue, classical flutter (the coupling of torsional and flapwise modes) poses a risk of immediate structural failure at high rotational speeds. Chen et al. (2018) proposed a specialized electromagnetic linear viscous damper to suppress torsional rotation and increase the flutter critical rotational speed
Given the geometric constraints of the blade web and main girder, traditional pendulum dampers are often unfeasible due to the insufficient vertical clearance required for the pendulum arm. To address this, Li et al. (2023) proposed a track-TMD designed to fit within the looped frame of the blade structure. This mechanism utilises a mass block that slides along a track system guided by shafts and springs, allowing for precise tuning without the vertical space required for a pendulum. Through Euler-Lagrange modelling, the authors determined that the vibration reduction ratio is highly sensitive to the installation position
Wind turbine blades vibrate simultaneously in the edgewise and flapwise directions, yet most controllers are unidirectional. Jahangiri et al. (2024) criticised this limitation and proposed a Two-Dimensional Nonlinear Tuned Mass Damper Inerter (2d-NTMDI) which utilises a single mass connected to two sets of springs, dashpots, and inerters arranged orthogonally. The geometric configuration creates nonlinear restoring forces, allowing the device to suppress vibrations in both axes simultaneously. Using Particle Swarm Optimization (PSO), the authors simulated a 5-MW wind turbine blade under realistic wind conditions and found that a mass ratio of 6% was optimal for balancing performance against weight penalties. 28 Under a mean wind speed of 6 m/s, the 2d-NTMDI reduced the spectral peak response in the edgewise direction by 62.2% and in the flapwise direction by 19.2%. However, the authors noted that at higher wind speeds (9 m/s), the flapwise effectiveness dropped significantly to 3.7% due to the damper requiring larger stroke lengths than available. Most significantly, the study applied rain-flow cycle counting to demonstrate that this bi-directional control extended the fatigue life of the blade root by 24.6% compared to an uncontrolled blade.
Passive control strategies summary.
3. Active control of wind turbines
While passive control systems provide a robust and cost-effective baseline for vibration mitigation, their efficacy is inherently limited by fixed parameters and a narrow frequency bandwidth, rendering them less effective against the broadband and non-stationary excitation typical of the wind turbine environment. Active control systems represent the high-performance frontier in structural control, designed to overcome these limitations by employing external energy sources, sensors, and real-time feedback algorithms to generate control forces as depicted in Figure 5. Unlike passive devices that merely dissipate energy, active systems can inject energy into the structure to directly counteract multidirectional and multi-modal vibrations, offering superior adaptability to changing wind speeds and aerodynamic instabilities. However, this enhanced performance introduces significant trade-offs, particularly regarding system complexity, parasitic power consumption, and the risk of instability in the event of sensor or actuator failure. This section reviews the current state of active control technologies applied to wind turbine towers and blades, critically analysing whether the performance gains justify the increased operational costs and implementation challenges. Active structural control process schematic diagram.
3.1. Turbine tower
The application of active control to wind turbine towers primarily focuses on mitigating the first FA and SS bending modes, which are heavily excited by turbulent wind loading and wave frequencies in offshore environments. Unlike passive systems, which rely on fixed tuning, active tower control systems utilise actuators—typically electromechanical or hydraulic—to drive a reaction mass or tension cables based on real-time feedback.
The Active Tuned Mass Damper (ATMD) adds a control force to the standard spring-dashpot arrangement of a passive TMD. This architecture allows for significant performance improvements, particularly when the turbine structure undergoes parameter changes or damage. Research by Fitzgerald and Basu (2013) demonstrates the critical role of ATMDs in maintaining reliability when SSI is compromised. In simulations of a 5-MW turbine where a 20% loss in foundation stiffness led to a peak-to-peak vibration increase of approximately 45%, the deployment of an LQR-controlled ATMD effectively negated these effects. The study reported that the active control scheme achieved peak-to-peak vibration reductions of up to 52% compared to the uncontrolled damaged case, thereby restoring the structural reliability of the tower despite the foundation degradation. 30
Building on this, Fitzgerald et al. (2018) expanded the scope to probabilistic reliability assessment, using fragility curves to quantify performance under structural uncertainties (e.g., damping ratios and stiffness). Their study showed that an ATMD could reduce the peak FA displacement of the tower by 40% under turbulent aerodynamic loading. More significantly, the active system drastically improved structural reliability: at the rated wind speed (11.4 m/s), the probability of the tower exceeding a 1 m displacement threshold was reduced from 100% (uncontrolled) to 39% (controlled). This performance was achieved with a peak control force of 102 kN and an RMS force of 41 kN, demonstrating that active control can effectively “de-risk” flexible tower designs. 31
Furthermore, active systems offer superior handling of system uncertainties—such as variations in damping ratios or natural frequencies—which often render passive TMDs ineffective (detuning). Hu et al. (2017) proposed an adaptive sliding-mode control (SMC) law to address these uncertainties and the “hard constraint” of the damper’s working space (stroke). 32 In comparative simulations against a passive TMD and a static state feedback controller, the adaptive SMC demonstrated superior performance. For specific load cases, the SMC reduced the RMS displacement of the tower top to 1.60 cm, representing a 9.57% improvement over the passive system (1.76 cm), while strictly maintaining the damper stroke within a 10 cm limit, a constraint the passive system failed to honour in several scenarios.
The efficacy of active tower control is heavily dependent on the control algorithm employed. While Linear Quadratic Regulators (LQR) are standard, recent studies have explored nonlinear control strategies to maximise vibration suppression. Amer et al. (2024) investigated a combination of Cubic Negative Velocity Control (CNVC) and Linear Negative Acceleration Control (LNAC) to mitigate tower vibrations under multi-frequency excitations. Their numerical analysis indicated that this combined nonlinear approach significantly outperformed standard Proportional-Derivative (PD) or Linear Negative Velocity Control (LNVC) methods. Under resonance conditions, the CNVC+LNAC system reduced the vibration amplitude from an uncontrolled state of 3.788 m to approximately 0.51 m. This represents a reduction factor of roughly 7.4, illustrating the capacity of advanced active algorithms to suppress resonant amplitudes that would otherwise be catastrophic for the tower structure. 33
A limiting factor in conventional active mass dampers is the mechanical friction and complexity of the drive train (springs and lead screws). To address this, Basaran et al. (2022) proposed a novel Active Electromagnetic Mass Damper (AEMD) which utilises opposing electromagnets to drive a stabiliser mass without physical springs. Using an adaptive backstepping control algorithm, this non-contact actuation method allows for rapid response to varying wind loads. In scale model simulations, a stabiliser mass of just 7.35 kg (representing a mass ratio of only 1.75%) was sufficient to suppress flow-induced vibrations effectively. The system demonstrated the ability to adapt to unknown wind loads, reducing the settling time of the nacelle vibration significantly compared to the uncontrolled state, while avoiding the mechanical wear and non-linear friction associated with traditional mechanical actuators. 34
3.2. Turbine blades
While tower control focuses on global stability, blade control targets the reduction of local edgewise and flapwise vibrations which are the primary source of fatigue loads. As blades become longer and more flexible, aerodynamic damping alone—often negative in the edgewise direction—is insufficient to prevent instability. Active structural control in blades employs internal actuation to modify the blades’ dynamic response directly.
Active tendon systems utilise actuators to vary the tension in cables strung within the blade structure, thereby generating a control moment that opposes vibratory motion. Staino et al. (2012) proposed an active tendon configuration where linear actuators are mounted inside the blade root, driving tendons anchored near the tip. 35 Using an LQR, this system demonstrated exceptional efficacy in mitigating edgewise vibrations, which are critical due to their low aerodynamic damping. In simulations of a 5-MW turbine, the active tendon system achieved a 65% reduction in maximum tip displacement and an 85% reduction in RMS tip acceleration compared to the uncontrolled case. However, this performance required significant control authority, with peak forces reaching approximately 28% of the blade weight.
To improve force efficiency, Fitzgerald and Basu (2014) developed the Cable-Connected Active Tuned Mass Damper (CCATMD). 36 This hybrid system couples a traditional ATMD with a pre-tensioned cable. The cable introduces a geometric stiffness component that assists the inertial force of the damper. Numerical benchmarks indicate that the CCATMD can reduce peak-to-peak in-plane vibrations by 41% (with 200 kN cable tension), outperforming a standalone ATMD by approximately 25% while requiring lower actuator forces than the active tendon system proposed by Staino et al. (2012).
Placing active mass dampers directly within the blade structure offers a localised solution to vibration suppression. Fitzgerald et al. (2013) investigated the use of ATMDs tuned to the fundamental in-plane frequency of the blade. Unlike passive TMDs, which struggle with the time-varying frequencies of rotating blades, the active system can adapt its control force.
Under high turbulence intensities (30%), the ATMD system reduced peak-to-peak blade displacement by 53% and peak displacement by 24% relative to the uncontrolled baseline. The study highlighted that while passive TMDs provided modest reductions (≈22%), they failed to reduce peak responses effectively, whereas the active counterpart successfully mitigated both RMS and peak values, preventing potential tower clearance issues.
Emerging research focuses on embedding smart materials, such as Piezoelectric (PZT) layers, directly into the blade composite structure (e.g., spar caps and shear webs) to act as distributed sensors and actuators. Lee (2021) analysed a stiffened composite blade model integrated with PZT layers using a negative velocity feedback control algorithm. The study demonstrated that piezoelectric actuation could effectively suppress transient vibrations from extreme loads (e.g., blast or gust loads), with higher control gains resulting in significantly faster decay rates. 37 Crucially, the study found that the placement of the shear web significantly influences control authority; optimising the web location alongside the PZT layers is essential for maximising the stiffness-to-damping ratio for both bending and twisting modes.
It is valuable to contextually compare structural control with Individual Pitch Control (IPC), the current industry standard for asymmetric load mitigation. Tang et al. (2022) proposed a 2DoF Robust IPC using µ-synthesis to handle model uncertainties. Their results showed an 18.68% reduction in the Damage Equivalent Load (DEL) for blade flapwise moments. 38 While IPC is effective for fatigue reduction (DEL), the structural control methods reviewed above (Active Tendons/ATMDs) often demonstrate higher percentage reductions in displacement amplitude (40–60%). This suggests that while IPC handles fatigue, active structural add-ons may be superior for determining limit-state stability and preventing excessive deflection during extreme events.
3.3. Performance cost
While active control strategies offer superior vibration suppression compared to passive alternatives, their commercial viability hinges on two critical factors: the parasitic energy cost of operation and the system’s fail-safe behaviour during extreme events. A primary critique of active structural control is the requirement for external power. For a system to be economically viable, the energy consumed by the actuators must be significantly less than the energy gained through increased aerodynamic efficiency or the capital expenditure (CAPEX) saved by extending component fatigue life.
Research indicates that this balance is generally favourable for modern multi-megawatt turbines. For instance, simulation studies on monopile towers have shown that ATMDs can achieve a 15% improvement in fatigue load reduction over passive systems while consuming less than 0.24% of the turbine’s nominal power output. 30 Similarly, in the AEMD system proposed by Basaran et al. (2022), the control currents required to stabilize the nacelle remained within an achievable range for standard power supplies, suggesting that the parasitic loss is negligible compared to the multi-megawatt output of the generator.
However, the energy cost scales non-linearly with the control objective. As noted in Staino et al. (2012), achieving elite performance (e.g., 65% reduction in tip displacement) required actuator forces equivalent to 28% of the blade weight. Such high-force applications would demand larger power supplies and heavier cabling, potentially negating the weight-saving benefits of the control system. Thus, the “sweet spot” for active control likely lies in moderate vibration suppression (40–50%) rather than total elimination.
The most significant barrier to the adoption of active structural control is reliability during power grid failure. According to the IEC 61400-1:2019 design standard, utility-scale wind turbines must survive Extreme Wind Conditions (EWM)—such as 50-year gusts—often while assuming a simultaneous loss of the electrical grid.
In a grid-loss scenario, an active actuator (whether hydraulic, electromechanical, or piezoelectric) loses its power source. This creates two distinct risks: 1. Loss of Damping: The system reverts from an “active” damper to a “passive” mass. If the passive stiffness of the actuator (e.g., the de-energized stiffness of a tendon or spring) is not tuned to the tower’s frequency, the mass may become “detuned,” providing zero damping or, in worst-case scenarios, amplifying vibrations through phase lag. 2. Stroke Excursion: Without active control forces to centre the mass, extreme wind gusts can drive the damper mass to its physical limits. If the mass impacts the tower wall or blade interior, it can cause localized structural damage or even lead to resonant collapse.
To satisfy IEC safety standards, active systems must incorporate fail-safe mechanisms. These typically include: • Passive Reversion: Designing the actuator to default to a passive stiffness value that provides at least baseline damping when de-energized. • Mechanical Locking: A fail-safe brake that locks the mass in a neutral position during power loss to prevent uncontrolled oscillations. • Elastic End-Stops: High-stiffness bumpers installed at the stroke limits to absorb the kinetic energy of the mass during extreme events, preventing catastrophic impact with the primary structure.
Active control strategies summary.
4. Semi-active control strategies
While active control systems offer superior performance in mitigating wind turbine vibrations, their implementation is often hindered by high power consumption, system complexity, and the risk of instability during power failures. Semi-active control strategies have emerged as a compelling compromise, occupying the excellent balance between the reliability of passive devices and the adaptability of active systems. Unlike active controllers that inject significant energy into the structure to counteract motion, semi-active devices operate by modulating their mechanical properties, such as damping coefficients or stiffness, in real-time using only a nominal amount of external power (often battery-operable). This adaptive capability allows semi-active systems, such as Magnetorheological (MR) dampers and Semi-Active Tuned Mass Dampers (STMDs), to maintain optimal tuning across a broad range of wind speeds and loading frequencies, effectively addressing the “detuning” issues that plague passive systems without the parasitic energy costs of fully active actuation. This section reviews the efficacy of these adaptive technologies, evaluating their potential to provide high-performance vibration suppression with the robust fail-safe characteristics required for large wind turbines.
4.1. Technology overview
The most prominent semi-active device in wind turbine applications is the MR damper. As shown in Figure 6, these devices are filled with a smart fluid containing micron-sized magnetic particles suspended in a carrier oil. Under normal conditions, the fluid behaves as a Newtonian liquid; however, when subjected to a magnetic field, the particles align into chains, changing the fluid into a semi-solid state within milliseconds. This rheological transformation allows the damper to vary its yield stress and damping force continuously by adjusting the input current to an electromagnetic coil. The dynamic behaviour of MR dampers is highly non-linear and is frequently modelled using the modified Bouc-Wen model,
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which accurately captures the hysteretic force-velocity relationship. In practical applications, they act as variable friction dampers, capable of providing high damping forces with very low power requirements (e.g., currents of 0–3 A). Typical MR damper components
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While passive TMDs are effective only when tuned to a specific frequency, Semi-Active TMDs (STMDs) address the issue of mistuning caused by varying operational conditions, such as the centrifugal stiffening of rotating blades. STMDs utilize a variable stiffness or variable damping mechanism to retune the absorber frequency in real-time, ensuring it matches the dominant excitation frequency of the turbine. Recent advancements employ frequency-tracking algorithms, such as the Short-Time Fourier Transform (STFT), to identify the instantaneous dominant frequency of the blade or tower. The STMD then adjusts its parameters to resonate at this new frequency, maintaining optimal energy dissipation even as the rotor speed or structural stiffness changes.
The Tuned Liquid Column Damper (TLCD) dissipates energy through the oscillation of liquid in a U-shaped tube and head loss across an orifice. In its semi-active configuration, the damping characteristics are modulated to suit the loading intensity. Two primary methods exist: 1. Variable Orifice Control: An electro-valve is used to mechanically adjust the blocking ratio of the orifice in real-time, thereby altering the head loss coefficient and the damping force. 2. Magnetorheological TLCD (MR-TLCD): Instead of water, the column is filled with MR fluid. A magnetic field applied at the base of the U-tube alters the fluid’s yield stress, allowing for instantaneous control of the liquid’s motion without moving mechanical valves. This method provides a faster response time and higher density (reducing the required tank volume) compared to traditional water-based TLCDs.
4.2. Applications to towers and blades
The efficacy of semi-active control strategies is highly dependent on the specific structural dynamics of the component being controlled. Towers are dominated by low-frequency global bending modes (FA and SS) often excited by wave and wind coupling, whereas blades experience higher-frequency, complex multi-modal vibrations (flapwise and edgewise) driven by rotation and aerodynamic instability. Consequently, the application of semi-active devices must be tailored to these unique requirements. Figure 7 shows the installation of MR dampers inside of blades in the edgewise direction. MR dampers - blade installation.
4.2.1. Tower semi-active control
Research by Rahman et al. (2019) demonstrated the significant potential of smart semi-active vibration control for towers using MR dampers optimized with nature-inspired algorithms. 41 By employing a PID controller tuned via Ant Colony Optimization (ACO), the study achieved a 79% reduction in tower displacement under harmonic excitation at the first natural frequency (2Hz). Experimental validation on a lab-scaled model confirmed these findings, showing a 66% reduction in vibration amplitude. This performance was superior to traditional Ziegler-Nichols (Z-N) and PSO tuning methods.
Similarly, Caterino (2015) investigated a variable restraint system at the base of a tower using MR dampers. This configuration allowed the rotational stiffness of the tower base to be modified in real-time. Under extreme load cases like the “Extreme Operating Gust” (EOG), the semi-active system reduced the base bending stress by up to 67% compared to a fixed-base equivalent. Although this “relaxing” of the base restraint led to a slight increase in top displacement (approx. 15–28%), it remained within safe limits, proving that semi-active control can effectively trade off displacement for critical stress reduction during extreme events. 42
Expanding on the application of magnetorheological technology, Park et al. (2019) investigated semi-active pendulum TMDs applied to both fixed-bottom and floating wind turbines. Their research highlighted the necessity of tailoring control logic to specific operational limit states. They determined that the Inverse Velocity-based Groundhook (IVB-GH) algorithm was most effective for mitigating Ultimate Limit State (ULS) loads during extreme events, whereas the Displacement-based Groundhook (DB-GH) strategy offered superior performance for Fatigue Limit State (FLS) loads during normal operation.
In numerical simulations of a monopile structure, the semi-active system demonstrated exceptional efficacy compared to passive configurations. Under fatigue loading, the DB-GH controller achieved a 64% reduction in SS Damage Equivalent Loads (DEL). For extreme conditions (ULS), the IVB-GH controller reduced the tower top FA force by 44.8% (vs. 30.7% for passive) and the base moment by 31.7% (vs. 25.0% for passive). Furthermore, this enhanced performance was achieved with greater spatial efficiency, as the semi-active system reduced the damper’s 90th percentile stroke by 35% compared to the passive system, directly addressing the spatial constraints inherent in nacelle design. 43
For offshore applications where liquid dampers are advantageous, Mendes et al. (2025) proposed a semi-active TLCD using an on-off valve control strategy (Groundhook logic). 44 This system was tested on 5-MW, 10-MW, and 15-MW offshore turbines. The results showed that the semi-active TLCD could reduce the RMS FA displacement by approximately 30–33%, outperforming passive TLCDs by an average of 10%. Crucially, the study found that the semi-active system was more robust against the passive detuning issue, maintaining high performance even when the wind turbine operated at speeds far from the damper’s design point. Sarkar and Chakraborty (2018) further advanced this concept by integrating MR fluids directly into the TLCD (MR-TLCD). Using an LQR algorithm, they achieved a 40–60% reduction in peak tower response under turbulent wind loads. 45
4.2.2. Blades semi-active control
Arrigan et al. (2011) demonstrated the efficacy of STMDs employing a STFT algorithm for frequency tracking. 46 In simulations where the rotor speed decelerated linearly from 3.14 rad/s to 1.57 rad/s, the blade’s natural frequency shifted significantly; however, the STMD successfully tracked this variation using a 40 s moving window, maintaining resonance and energy dissipation throughout the event where a passive TMD became detuned and ceased to function. Similar adaptability was observed in floating offshore wind turbines, where Dinh et al. (2016) reported that STMDs maintained effectiveness even under severe structural changes, such as a 50% loss in blade stiffness or drops in mooring line tension, scenarios that rendered passive systems ineffective. 47
For edgewise vibrations, which are critical due to their negligible aerodynamic damping, the performance gains of semi-active control are even more pronounced. Fakhry et al. (2024) utilized a PSO algorithm to tune an LQR for MR dampers embedded within the blades. This optimized semi-active approach achieved an exceptional 81.0% reduction in peak edgewise displacement and a 77.0% reduction in peak-to-peak displacement, vastly outperforming the Passive-On (10% reduction) and Passive-Off (69% reduction) configurations.48,49
Semi-active control strategies summary.
5. Comparative synthesis
It is crucial to note that conducting a direct, one-to-one quantitative meta-analysis of the reviewed literature is precluded by the substantial heterogeneity across the studies. The research evaluated encompasses a wide spectrum of turbine ratings (ranging from 5-MW benchmark models to 15-MW+ offshore colossi), diverse environmental loading conditions (operational turbulence, parked extreme gusts, wave coupling, and seismic events), and fundamentally different performance metrics (e.g., RMS displacement, peak-to-peak acceleration, and DEL). Consequently, presenting a unified quantitative matrix risks oversimplifying these nuanced operational contexts and generating misleading cross-comparisons. Instead, this comparative synthesis provides a critical narrative framework. It contextualises the relative performance ranges, power consumption characteristics, and reliability implications of each control paradigm within their specific application domains, ensuring that performance outcomes are evaluated as relative indicators rather than absolute, universally equivalent metrics.
Having examined the individual mechanisms and theoretical foundations of passive, active, and semi-active control strategies in the preceding sections, this section synthesises the findings to offer a holistic comparison. The selection of an appropriate structural control system for wind turbines, particularly for large-scale wind turbine installations, is rarely a decision based solely on vibration reduction capacity. Rather, it is a multi-objective optimisation problem involving trade-offs between performance, power consumption, reliability, and CAPEX.
5.1. Performance and vibration reduction efficiency
The primary metric for any structural control system is its ability to mitigate loads and dampen resonance. Based on the literature reviewed in previous sections, a hierarchy of performance efficiency emerges. Active control systems demonstrate the highest theoretical performance ceiling. As illustrated in Table 2, active strategies such as AMD and active tendons can theoretically achieve vibration reductions in the range of 40–60% across a broad frequency band. Their ability to inject energy into the system allows them to dampen multi-modal vibrations effectively, addressing both the fundamental frequency (1P) and higher harmonics (3P) simultaneously.
However, passive systems, particularly TMDs and TLCDs, remain the industry baseline. While their peak reduction capability (typically 30–45% at the tuned frequency) is respectable, their performance degrades significantly when the structural frequency shifts due to factors such as SSI, ice accretion on blades, or component ageing. This “detuning” phenomenon is the critical weak point of passive strategies.
On the other hand, semi-active systems occupy the best trade-off as shown in Figure 8. By offering real-time retuning capabilities, modifying stiffness or damping coefficients via MR dampers or variable stiffness elements, they maintain high performance and sometimes surpass that of active systems (35–80% reduction) even as structural properties evolve. They effectively mitigate the detuning risks associated with passive systems without requiring the massive energy injection of active systems. Vibration reduction percentage vs. control efforts exerted.
5.2. Power consumption and parasitic energy loss
For a power-generating asset, the parasitic energy consumption of the control system is a decisive factor in the Levelized Cost of Energy (LCOE). For active systems, they require continuous external power sources to drive actuators. In extreme wind conditions, where control is most needed, the power demand peaks. Literature indicates that poor actuator sizing or control law design can result in a scenario where the energy consumed by the damper negates the efficiency gains from structural stability. Furthermore, in the event of grid loss (e.g., during a storm), active systems lose their functionality unless substantial battery backups are installed, adding weight and cost. However, passive systems operate at zero energy cost. Moreover, semi-active systems require nominal power, typically comparable to a light bulb (battery-operable), to adjust magnetic fields or valve positions. They do not drive the mass directly but rather manage energy dissipation. This low power requirement renders them compatible with small-scale energy harvesting solutions, making them theoretically self-sustaining.
5.3. Reliability, maintenance, and implementation feasibility
While the theoretical efficacy of advanced structural control is well documented, a critical evaluation of the literature reveals a stark disparity in Technology Readiness Levels (TRLs) across the three control paradigms. Passive systems represent the current industry standard and have undergone extensive field validation. Conversely, most active and semi-active strategies remain confined to numerical simulations or scaled laboratory testing. The transition to full-scale field implementation for these advanced systems is currently impeded by the financial and logistical risks associated with integrating unproven prototype actuators into multi-megawatt generating assets. Consequently, a pressing recommendation for future research is the execution of full-scale pilot programmes to empirically validate the complex aeroelastic interactions and scaling laws that cannot be fully captured in simulated environments.
The shift towards multi-megawatt wind turbines places a premium on reliability and low maintenance. Accessibility to the nacelle or tower top is severely restricted by weather windows and high operational costs especially for offshore and floating farms. For commercial deployment in these remote environments, long-term structural reliability is paramount. A critical distinction highlighted in this review is the fail-safe nature of the systems. Active systems introduce a risk of instability; specifically, “spillover effects” where control energy excites unmodelled high-frequency modes. Furthermore, a sensor or actuator failure in an active system can render the damper useless or, worse, destructive. The IEC 61400-1 design standard mandates that utility-scale turbines survive extreme wind conditions, often concurrent with electrical grid failure. Under such scenarios, the long-term reliability of fully active systems is highly compromised; a loss of power to hydraulic or electromechanical actuators not only ceases vibration mitigation but introduces severe risks of mechanical detuning or uncontrolled stroke excursions.
Conversely, semi-active systems generally possess a fail-safe property if the control electronics fail, the MR damper or variable stiffness device reverts to a passive state, still providing a baseline level of damping. For offshore applications where a maintenance crew may not visit a turbine for months, this fail-safe characteristic significantly favours semi-active solutions over fully active ones. Because devices such as MR dampers require minimal power and revert to a reliable, passive state upon electrical failure, they provide the necessary robust characteristics essential for commercial certification and extended operational lifespans. Ultimately, the commercial viability of any structural control system is dictated by its impact on the LCOE. The integration of an active or semi-active system introduces additional CAPEX and requires new maintenance protocols. For the technology to be commercially viable, these costs must be comprehensively offset by the economic benefits of extended fatigue life, reduced downtime, and enhanced power capture.
As the industry moves towards CCD, the commercial justification for semi-active systems becomes increasingly robust; by guaranteeing specific load reductions during the preliminary design phase, manufacturers can optimise blade and tower dimensions. This approach achieves substantial material savings that directly lower the initial structural CAPEX and, consequently, the LCOE.
5.4. Comparative discussion of control algorithms
Although previous sections reviewed a range of control methodologies—including LQR, SMC, adaptive and backstepping approaches, fuzzy logic, Groundhook strategies, and frequency-tracking algorithms—their relative merits, limitations, and suitability for different turbine configurations require deeper comparative analysis. The selection of a control algorithm in large-scale wind turbines is not governed solely by vibration reduction performance; it must also consider robustness to parameter uncertainty, computational burden, sensor requirements, fail-safe behaviour, and compliance with certification standards such as IEC 61400-1. In this context, algorithmic choice becomes a multi-criteria design decision rather than a purely theoretical optimization problem.
LQR remains the most widely implemented strategy in both active and semi-active wind turbine applications due to its mathematical transparency and systematic optimality framework. By minimizing a quadratic cost function that balances structural response and control effort, LQR provides predictable stability margins, moderate computational demand, and straightforward implementation in state-space form. However, its performance depends heavily on the accuracy of the underlying linearized model. In multi-megawatt turbines, structural properties vary with soil-structure interaction, centrifugal stiffening, blade icing, and aerodynamic nonlinearities. Such parameter variations can degrade the effectiveness of fixed-gain LQR controllers unless observers or gain-scheduling techniques are employed, which increases estimation complexity. Nevertheless, due to its stability guarantees and relatively low computational overhead, LQR remains highly suitable for onshore fixed-bottom turbines and semi-active MR damper implementations where system nonlinearities are moderate.
On the other hand, SMC offers enhanced robustness against parameter uncertainty and external disturbances by enforcing convergence to a predefined sliding surface. This robustness makes SMC attractive for offshore turbines subjected to severe turbulence or foundation variability. Unlike LQR, SMC can tolerate bounded modelling errors without significant performance degradation. However, its discontinuous switching nature introduces the well-known chattering phenomenon, which may excite unmodeled high-frequency structural modes in flexible blades and towers. In large-scale turbines, such high-frequency excitation can accelerate local fatigue damage or increase actuator wear. Mitigation strategies, such as boundary-layer smoothing or higher-order SMC formulations, reduce chattering but also partially compromise robustness. Consequently, while SMC is theoretically well suited for uncertain offshore environments, its practical implementation in blade-embedded systems requires careful smoothing to ensure long-term reliability.
Adaptive and nonlinear backstepping controllers attempt to address time-varying structural dynamics directly by updating control parameters in real time. These approaches are particularly appealing for floating offshore wind turbines, where low-frequency platform pitch, roll, and heave motions introduce strong dynamic coupling with tower and blade modes. By continuously estimating system parameters, adaptive control can maintain performance under stiffness degradation, mooring tension loss, or damage scenarios. However, such time-varying gain structures increase computational demand and sensitivity to measurement noise. Moreover, guaranteeing bounded stability under grid-loss or extreme wind events remains a non-trivial challenge. Certification authorities typically require deterministic stability margins, and the validation of adaptive controllers under all operational limit states is considerably more complex than for fixed-gain linear controllers. As a result, while adaptive strategies demonstrate high theoretical potential, large-scale industrial implementation remains limited and requires further experimental validation.
Fuzzy logic and other intelligent control approaches such as neuro controllers provide an alternative model-free framework capable of handling nonlinearities without requiring explicit mathematical representations. Their rule-based structure allows smooth control action without chattering, making them attractive for semi-active MR dampers and extreme-event mitigation. In particular, fuzzy controllers can switch effectively between different operational modes, such as fatigue mitigation during normal operation and stress reduction during ultimate limit states. However, their performance depends heavily on rule-base design and membership function tuning, which becomes increasingly complex for multi-modal and coupled blade-tower systems. Scaling such controllers to multi-megawatt turbines with distributed sensing introduces dimensionality challenges, and issues of interpretability and verification may hinder regulatory acceptance. Despite these limitations, fuzzy and neural based semi-active systems remain promising due to their low computational demand and inherent fail-safe compatibility.
Groundhook and clipped-optimal strategies are particularly well suited for semi-active devices because they do not inject energy into the structure and thus inherently preserve passive stability. These algorithms require minimal computation and guarantee that the damper behaves in a dissipative manner, even during power loss scenarios. This property is especially valuable for offshore or remote turbines where maintenance access is limited and reliability is paramount. While their vibration reduction capability may be slightly lower than fully active nonlinear controllers, their robustness and certification feasibility make them strong candidates for commercial deployment in MR dampers and TLCD systems.
Implementing nonlinear or adaptive control strategies in large-scale turbines introduces additional systemic challenges. High-fidelity state estimation demands distributed sensor networks, including accelerometers, strain gauges, and potentially LIDAR-assisted feedforward systems. Computational latency must remain significantly below dominant modal time scales to ensure effective control without excessive power consumption. Furthermore, any active or adaptive system must revert to a safe passive state during grid-loss events to comply with extreme wind survival requirements. Aggressive nonlinear control laws may also induce spillover effects, where energy is unintentionally transferred to higher structural modes not captured in reduced-order models. These considerations illustrate that algorithmic complexity does not automatically translate to superior practical performance.
In synthesis, LQR offers a mature and industrially viable balance between performance andapplication, SMC provides robustness at the expense of potential chattering-induced fatigue, adaptive and backstepping methods hold strong potential for floating and damage-sensitive configurations but require further validation, fuzzy logic and neuro controllers offer flexible nonlinear handling with manageable computational cost, and Groundhook-based semi-active strategies currently present the most commercially feasible solution for large utility-scale turbines. Therefore, while nonlinear and adaptive algorithms may achieve superior theoretical vibration suppression, semi-active architectures employing robust, yet computationally efficient control laws appear to represent the most practical pathway for next-generation multi-megawatt wind turbines.
6. Emerging Challenges and Recommendations
As the wind energy industry expands into deeper waters and seismically active regions, standard structural control strategies face new dynamic challenges that necessitate a departure from conventional design approaches. The commercialisation of Floating Offshore Wind Turbines (FOWT) introduces a fundamental shift in structural dynamics, as the floating platform possesses rigid-body vibration modes—pitch, roll, and heave—with very low frequencies, often below 0.1 Hz. 52 Traditional nacelle-based TMDs, which are typically tuned for the first tower bending mode, may prove ineffective or even detrimental in this context by exacerbating platform pitch motions due to dynamic coupling. 53 Because a nacelle-based damper is situated at the extremity of a 100-to-150-metre lever arm, its inertial forces can induce severe overturning moments that dynamically couple with and exacerbate the platform’s low-frequency pitch motions. Consequently, future research is increasingly directing attention towards hull-based damping strategies, where semi-active liquid column dampers are integrated directly into the floating substructure. 54 This architecture provides a superior technical solution for low-frequency mitigation: it counteracts wave-induced hydrodynamic excitation directly at the source, rather than hundreds of metres above it. Furthermore, it exploits the vast internal volume of the hull to achieve the massive fluid displacements required to tune to ultra-low frequencies (<0.1 Hz) without adding any destabilising top-mass to the tower. This approach serves the dual purpose of lowering the system’s centre of gravity to improve static stability while actively dissipating wave-induced motions.
Simultaneously, the expansion of wind farms into seismically active zones creates a complex “multi-hazard” threat profile that current design codes do not fully address. 16 A passive damper tuned precisely for wind-induced resonance may be rendered ineffective, or potentially amplify the structural response, during the broadband, high-frequency excitation characteristic of an earthquake. 55 To mitigate these concurrent risks, the future of structural control lies in the development of adaptive semi-active control laws, such as fuzzy logic and neuro controllers. These systems are capable of real-time load recognition, allowing them to instantly switch between a “wind-damping” mode and a “seismic-isolation” mode, thereby preventing catastrophic failures under combined loading scenarios. Highly effective multi-hazard systems achieve this through real-time frequency-tracking algorithms. For instance, Hemmati and Oterkus (2018) developed an adaptive STMD model that utilises a STFT to continuously monitor the instantaneous natural frequency of an offshore turbine. 56 Because earthquakes can cause rapid degradation of soil and tower stiffness, the fundamental frequency of the turbine drops significantly during a seismic event. The STFT algorithm instantly identifies these frequency shifts, allowing the STMD to modulate its stiffness to maintain resonance. Concurrently, the control law tracks the relative displacement of the damper; if the displacement increases, the damping coefficient is momentarily set to zero, thereby maximising the system’s ability to absorb sudden energy spikes.
Numerical simulations robustly validate the effectiveness of this adaptive logic under multi-hazard conditions. Under an extreme parked scenario facing concurrent stochastic wind, wave, and seismic excitations, this STFT-based semi-active system achieved a 47% reduction in peak nacelle displacement, lowering the peak excursion from 1.91m (uncontrolled) down to 1.01m. By contrast, standard passive mass dampers were shown to become highly off-tuned under identical damage development conditions, resulting in practically zero effectiveness in mitigating peak response values. This confirms that adaptive frequency-tracking logic is a strict prerequisite for structural survival in multi-hazard environments.
To fully realise the potential of structural control, the field is moving away from reactive, auxiliary systems towards intelligent, integrated solutions driven by digitalisation. A primary limitation of current control strategies is their reliance on reactive responses—measuring vibration after it has commenced. The integration of LIDAR-assisted feedforward control offers a transformative solution, employing Machine Learning algorithms to analyse incoming wind turbulence hundreds of metres upstream. 57 This predictive capability allows a semi-active damper to “pre-load” its stiffness or adjust damping coefficients seconds before a gust arrives, effectively mitigating the transient impact rather than merely damping the subsequent oscillation. 58 Furthermore, the adoption of Digital Twins, high-fidelity virtual models updated with real-time sensor data, will facilitate predictive maintenance of these control systems, ensuring high reliability even in remote offshore locations.59,60 For instance, a recent case study by Pastor-Sanchez et al. (2025) successfully demonstrated an operational digital twin for the structural health monitoring of a 5-MW floating offshore wind turbine. By embedding a physics-based hydro-elastic reduced-order model within a cloud-ready Internet of Things (IoT) architecture, the system was able to reconstruct full-field displacements, von Mises stresses, and fatigue metrics with near real-time responsiveness.
Crucially, this framework processed a three-hour complex load case in under four minutes—over two orders of magnitude faster than traditional finite-element models—enabling continuous structural monitoring, rapid ‘what-if’ assessments, and timely decision-making for adaptive control. Such empirical applications prove that digital twins can successfully transition from offline design tools to active operational supervisors, continuously evaluating the health of the turbine structure to mitigate vibrations and extend fatigue life.
Ultimately, the most significant trend identified in the literature is the shift towards Integrated Load Analysis (ILA) and CCD. 61 Historically, dampers were often retrofitted to solve vibration problems discovered late in the development cycle. In the future paradigm, turbines will be designed with the control system as a fundamental parameter from the outset. 62 By guaranteeing that a reliable semi-active system will cap ultimate loads by a specific margin, engineers can optimise structural components, such as reducing tower wall thickness or blade spar cap dimensions. In this context, structural control transitions from a safety add-on to a critical design enabler, directly contributing to weight reduction and a lower LCOE.
While active control demonstrates superior theoretical performance and maximum vibration reduction, the techno-economic constraints of wind farm operation currently hinder its widespread commercial adoption. Passive systems remain the industry standard, favoured for their mechanical simplicity and low CAPEX. However, as turbine capacities exceed 6-MW, resulting in taller towers and longer, more flexible blades, the narrow frequency bandwidth of passive systems is increasingly becoming a performance bottleneck.
Consequently, the literature indicates that a paradigm shift is underway. Semi-active control represents the most viable path forward for the next generation of multi-megawatt turbines, irrespective of their siting. It bridges the technological gap by offering the adaptability required to manage the complex dynamics of ultra-large flexible structures, specifically addressing variable aerodynamic and wave loading and operational frequency shifts, while adhering to the stringent reliability and low power consumption mandates of utility-scale energy infrastructure.
7. Conclusion
The rapid upscaling of wind turbines to multi-megawatt capacities and floating offshore architectures has transformed vibration mitigation from a secondary consideration into a governing design constraint. This review has evaluated the state-of-the-art in passive, active, and semi-active structural control strategies applied to turbine blades and towers. By critically analysing these technologies against the competing metrics of vibration reduction efficiency, power consumption, and reliability, it is evident that advanced control mechanisms are essential to ensure the longevity and stability of modern wind energy systems. While passive control strategies remain the established industry standard due to their mechanical simplicity and zero energy cost, they are increasingly inadequate for highly flexible modern turbines. Their reliance on fixed parameters renders them highly susceptible to frequency detuning when structural operating points shift due to variable operational conditions, soil-structure interaction, or multi-hazard input.
Conversely, active control systems offer superior vibration suppression and the ability to adapt to changing wind speeds by injecting energy into the structure. However, their widespread commercial viability is severely hindered by high parasitic power consumption, system complexity, and critical fail-safe vulnerabilities, particularly the risk of uncontrolled excursions or a dangerous loss of damping during mandated electrical grid failures. Consequently, this paper identifies semi-active control as the optimal and most viable solution for the next generation of utility-scale wind turbines. Technologies such as MR dampers and STMDs strike an excellent balance by offering the real-time retuning and adaptability of active systems while operating on merely nominal amounts of external power.
Crucially, semi-active systems possess inherent fail-safe characteristics; if control electronics fail, they revert to a reliable, passive damping state, thereby satisfying the stringent safety and reliability mandates necessary for remote offshore deployments. Looking ahead, the successful deployment of turbines in deeper waters and seismically active regions will require structural control to evolve from reactive retrofits into intelligent, predictive solutions.
The integration of LIDAR-assisted feedforward algorithms and operational digital twins will enable the real-time, adaptive mitigation of transient impacts and facilitate continuous structural health monitoring. Ultimately, the future commercial viability of advanced structural control hinges on a paradigm shift toward CCD. Historically, vibration dampers have been retrofitted as auxiliary solutions to resolve issues discovered late in development. However, CCD integrates the control system as a fundamental parameter during the preliminary design phase. By guaranteeing that a highly reliable semi-active system will cap ultimate loads and mitigate fatigue, engineers can confidently optimise the physical dimensions of the turbine. For example, safely reducing tower wall thickness or blade spar cap dimensions translates to substantial material savings, which directly lowers the initial structural CAPEX.
Furthermore, when supported by modern digitalisation tools—such as the real-time structural health monitoring digital twins to process complex multi-hazard load cases in minutes—CCD enables predictive maintenance that drastically reduces operational expenditure. Through these dual reductions in CAPEX and operational expenditure, integrated structural control transitions from a supplementary safety mechanism to a critical economic enabler, decisively lowering the LCOE and driving the continued global expansion of wind power.
Footnotes
Funding
The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the London South Bank University.
Declaration of conflicting interests
The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
AI Disclosure
During the preparation of this work, the authors utilised AI tools (ChatGPT/Google Gemini) to assist with language refinement, grammatical proofreading and figures production. We reaffirm that all technical analyses, comparative syntheses, literature selection, and final editing were solely conducted by us.