Search Results (1,814)

Offshore wind-farm performance remains constrained by persistent wake deficits and turbulence that compound across intra-farm, intra-cluster, and inter-cluster scales, particularly under atmospheric neutral–stable stratification. A concept is advanced whereby offshore wind-farm yield may be augmented by pairing conventional horizontal-axis wind turbines (HAWTs) with lighter-than-air parafoil systems that entrain higher-momentum air and re-energise wakes, complementing yaw/induction-based wake control and enabling higher array energy density. A concise synthesis of wake physics and associated challenges motivates opportunities for active momentum re-injection, while a review of kite technologies frames design choices for lift generation and spatial keeping. Stability and control, spanning static and dynamic behaviours, tether dynamics, and response to extreme meteorological conditions, are identified as key challenges. System-integration pathways are outlined, including alignment and mounting options relative to turbine rows and prevailing shear. A staged validation programme is proposed, combining high-fidelity numerical simulation with wave-tank testing of coupled mooring–tether dynamics and wind-tunnel experiments on scaled arrays. Evaluation metrics emphasise net energy gain, fatigue loading, availability, and Levelized Cost of Energy (LCOE). The paper concludes with research directions and recommendations to guide standards and investment, and with a quantitative assessment of the techno-economic significance of kite–HAWT integration at scale. Full article

To improve the dynamic response and steady-state frequency quality of a wind–storage coordinated system during primary frequency regulation, and to address the secondary frequency dip caused by rotor kinetic energy recovery when a doubly fed induction generator (DFIG)-based wind turbine (DFIG-WT) participates in frequency support, this paper proposes a coordinated wind–storage primary frequency regulation strategy. This strategy synergistically controls the wind turbine’s rotor kinetic energy recovery and exploits the advantages of hybrid energy storage system (HESS). During the DFIG-WT control stage, an adaptive weighted model is developed for the inertial and droop power contributions of the DFIG-WT based on the available rotor kinetic energy, enabling a rational distribution of primary frequency regulation power. In the control segment of HESS, an adaptive complementary filtering frequency division strategy is proposed. This approach integrates an adaptive adjustment method based on state of charge (SOC) to control both the battery energy storage system (BESS) and supercapacitor (SC). Additionally, the BESS assists in completing the rotor kinetic energy recovery process. Through simulation experiments, the results demonstrate that under operating conditions of 9 m/s wind speed and a 30 MW step disturbance, the proposed adaptive weight integrated inertia control elevates the frequency nadir to 49.84 Hz and reduces the secondary frequency dip to 0.0035 Hz. Under the control strategy where wind and storage coordinated participate in frequency regulation and BESS assist in rotor kinetic energy recovery, secondary frequency dips were eliminated, with steady-state frequency rising to 49.941 Hz. The applicability of this strategy was further validated under higher wind speeds and larger disturbance conditions. Full article

This paper presents a systematic review conducted under the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) framework, analyzing 286 scientific articles focused on vibration-based predictive maintenance strategies for wind turbines within the context of advanced Prognostics and Health Management (PHM). The review combines international standards (ISO 10816, ISO 13373, and IEC 61400) with recent developments in sensing technologies, including piezoelectric accelerometers, microelectromechanical systems (MEMS), and fiber Bragg grating (FBG) sensors. Classical signal processing techniques, such as the Fast Fourier Transform (FFT) and wavelet-based methods, are identified as key preprocessing tools for feature extraction prior to the application of machine-learning-based diagnostic algorithms. Special emphasis is placed on machine learning and deep learning techniques, including Support Vector Machines (SVM), Random Forest (RF), Convolutional Neural Networks (CNN), Long Short-Term Memory networks (LSTM), and autoencoders, as well as on hybrid digital twin architectures that enable accurate Remaining Useful Life (RUL) estimation and support autonomous decision-making processes. The bibliometric and case study analysis covering the period 2020–2025 reveals a strong shift toward multisource data fusion—integrating vibration, acoustic, temperature, and Supervisory Control and Data Acquisition (SCADA) data—and the adoption of cloud-based platforms for real-time monitoring, particularly in offshore wind farms where physical accessibility is constrained. The results indicate that vibration-based predictive maintenance strategies can reduce operation and maintenance costs by more than 20%, extend component service life by up to threefold, and achieve turbine availability levels between 95% and 98%. These outcomes confirm that vibration-driven PHM frameworks represent a fundamental pillar for the development of smart, sustainable, and resilient next-generation wind energy systems. Full article

This study proposes a hybrid forecast-enabled adaptive crowbar coordination strategy to enhance low-voltage ride-through (LVRT) performance of doubly fed induction generator (DFIG) wind turbines. A unified electro-mechanical model in the αβ/dq frames with dual closed-loop control for rotor- and grid-side converters is built in MATLAB/Simulink (R2018b), and LVRT constraints on current safety and DC-link energy are explicitly formulated, yielding an engineering crowbar-resistance range of 0.4–0.8 p.u. On the forecasting side, a CEEMDAN-based decomposition–modeling–reconstruction pipeline is adopted: high- and mid-frequency components are predicted by a dual-stream Informer–LSTM, while low-frequency components are modeled by XGBoost. Using six months of wind-farm data, the hybrid forecaster achieves best or tied-best MSE, RMSE, MAE, and R² compared with five representative baselines. Forecasted power, ramp rate, and residual-based uncertainty are mapped to overcurrent and DC-link overvoltage risk indices, which adapt crowbar triggering, holding, and release in coordination with converter control. In a 9 MW three-phase deep-sag scenario, the strategy confines DC-link voltage within ±3% of nominal, shortens re-synchronization from ≈0.35 s to ≈0.15 s, reduces rotor-current peaks by ≈5.1%, and raises the reactive-support peak to 1.7 Mvar, thereby improving LVRT safety margins and grid-friendliness without hardware modification. Full article

This paper examines the application of an Adaptive Neuro-Fuzzy Inference System (ANFIS) for voltage regulation in a small-scale wind turbine (SWT) system intended for off-grid rural electrification in Uzbekistan. The proposed architecture consists of a wind turbine, a permanent-magnet DC generator, and a buck converter supplying a regulated 48 V DC load. While ANFIS-based control has been reported previously for wind energy systems, the novelty of this work lies in its focused application to a DC-generator-based SWT topology using real wind data from the Bukhara region, together with a rigorous quantitative comparison against a conventional PI controller under both constant- and reconstructed variable-wind conditions. Dynamic performance was evaluated through MATLAB/Simulink simulations incorporating IEC-compliant wind turbulence modeling. Quantitative results show that the ANFIS controller achieves faster settling, reduced voltage ripple, and improved disturbance rejection compared to PI control. The findings demonstrate the technical feasibility of ANFIS-based voltage regulation for decentralized DC wind energy systems, while recognizing that economic viability and environmental benefits require further system-level and experimental assessment. Full article

This study establishes a direct and quantitative link between field-scale monopile behavior, three-dimensional finite element (FE) modeling, and practical p-y curve formulations for large-diameter offshore monopiles. A validated three-dimensional FE model, benchmarked against a full-scale monopile field test, was employed to derive depth-dependent p-y curves under monotonic lateral loading and to evaluate the applicability of classical formulations proposed by Matlock and Reese. A systematic parametric analysis was performed to investigate the influence of pile diameter, embedment depth, and undrained shear strength of the surrounding soil. The results demonstrate that pile diameter and soil shear strength exert a dominant control on lateral stiffness and ultimate soil reaction, whereas embedment depth has only a minor influence on near-surface p-y behavior within the deep embedment range considered. Increasing the pile diameter leads to a transition from bending-dominated response to rigid-body rotation accompanied by three-dimensional soil wedge formation. Quantitative comparisons show that, at depths of 1–4 m and for working displacement levels of approximately 5–10 mm, FE-derived soil reactions are typically 3.0–4.8 times higher than those predicted by the Matlock formulation, as well as Reese curves. These findings demonstrate that classical p-y methods can significantly underestimate lateral soil resistance for modern large-diameter monopiles and highlight the necessity of calibrated three-dimensional FE analyses or FE-informed p-y modifications for reliable offshore wind turbine foundation design. Full article

The integration of high-penetration renewable energy sources (RESs) and electric vehicles (EVs) increases the risk of voltage fluctuations in distribution networks. Traditional static partitioning strategies struggle to handle the intermittency of wind turbine (WT) and photovoltaic (PV) generation, as well as the spatiotemporal randomness of EV loads. Furthermore, existing scheduling methods typically optimize EV active power or reactive compensation independently, missing opportunities for synergistic regulation. The main novelty of this paper lies in proposing a spatiotemporally coupled voltage-stability optimization framework. This framework, based on an hourly updated electrical distance matrix that accounts for RES uncertainty and EV spatiotemporal transfer characteristics, enables hourly dynamic network partitioning. Simultaneously, coordinated active–reactive optimization control of EVs is achieved by regulating the power factor angle of three-phase six-pulse bidirectional chargers. The framework is embedded within a hierarchical model predictive control (MPC) architecture, where the upper layer performs hourly dynamic partition updates and the lower layer executes a five-minute rolling dispatch for EVs. Simulations conducted on a modified IEEE 33-bus system demonstrate that, compared to uncoordinated charging, the proposed method reduces total daily network losses by 4991.3 kW, corresponding to a decrease of 3.9%. Furthermore, it markedly shrinks the low-voltage area and generally raises node voltages throughout the day. The method effectively enhances voltage uniformity, reduces network losses, and improves renewable energy accommodation capability. Full article

With the increasing penetration of wind power, the transient synchronization stability of doubly fed induction generator (DFIG)-based wind turbines during grid faults has become a critical issue. While conventional fault ride-through methods like Crowbar protection can ensure safety, they compromise system controllability and worsen grid voltage conditions. Virtual resistance demagnetization control has emerged as a promising alternative due to its simple structure and effective flux damping. However, its impact on transient synchronization stability has not been revealed in existing studies. To fill this gap, this paper presents a comprehensive analysis of the transient synchronization stability of DFIG systems under virtual resistance control, introducing a novel fourth-order transient synchronization model that explicitly captures the coupling between the virtual resistance demagnetization control and phase-locked loop (PLL) dynamics. The model reveals the emergence of transient power and positive damping terms induced by the virtual resistance, which play a pivotal role in stabilizing the system. Furthermore, this work theoretically investigates how the virtual resistance and current loop’s proportional-integral (PI) parameters jointly influence transient stability, demonstrating that increasing the virtual resistance while reducing the integral gain of the current loop significantly enhances synchronization stability. Simulation results validate the accuracy of the model and the effectiveness of the proposed analysis. The findings provide a theoretical foundation for optimizing control parameters and improving the stability of DFIG-based wind turbines during grid faults. Full article

The capability of wind turbines (WTs) to provide frequency response is crucial for future power systems with high wind power penetration. Existing strategies primarily focus on mitigating initial and secondary frequency drop (SFD), often overlooking the adverse effects of rotor speed recovery on turbine safety and sustained grid support. Moreover, the lack of a dynamic linkage between the frequency support stage (FSS) and speed recovery stage (SRS) impedes multi-objective coordination encompassing initial drop suppression, SFD mitigation, and rapid rotor speed recovery. To address these gaps, this paper proposes a two-stage coordinated control strategy. In the FSS, the frequency regulation coefficients (K_p and K_d) are adaptively adjusted based on available kinetic energy, ensuring its rational release. Subsequently, the switching timing from FSS to SRS is optimized using these coefficients to suppress the SFD and accelerate recovery. Finally, a fuzzy logic-based PI controller dynamically governs the SRS to restore rotor speed efficiently while further alleviating the SFD. Simulation results confirm the effectiveness of the proposed strategy under two wind speeds. It improves the initial frequency nadir by up to 0.197 Hz over no frequency control, reduces the secondary frequency drop by as much as 0.106 Hz compared to the stepwise method, and accelerates rotor speed recovery by over 30%, quantitatively validating its superior coordinated performance. Full article

Driven by global initiatives to mitigate climate change, the offshore wind power industry is experiencing rapid growth. Personnel transfer between service operation vessels (SOVs) and offshore wind turbines under complex sea conditions remains a critical factor governing the safety and efficiency of operation and maintenance (O&M) activities. This study establishes a fully coupled dynamic response and control simulation framework for an SOV equipped with an active motion-compensated gangway. A numerical model of the SOV is first developed using potential flow theory and frequency-domain multi-body hydrodynamics to predict realistic vessel motions, which serve as excitation inputs to a co-simulation environment (MATLAB/Simulink coupled with MSC Adams) representing the Stewart platform-based gangway. To address system nonlinearity and coupling, a composite control strategy integrating velocity and dynamic feedforward with three-loop PID feedback is proposed. Simulation results demonstrate that the composite strategy achieves an average disturbance isolation degree of 21.81 dB, significantly outperforming traditional PID control. Validation is conducted using a ship motion simulation platform and a combined wind–wave basin with a 1:10 scaled prototype. Experimental results confirm high compensation accuracy, with heave variation maintained within 1.6 cm and a relative error between simulation and experiment of approximately 18.2%. These findings demonstrate the framework’s capability to ensure safe personnel transfer by effectively isolating complex vessel motions and validate the reliability of the coupled dynamic model for offshore operational forecasting. Full article

The increasing penetration of wind energy has led to reduced system inertia and heightened sensitivity to dynamic disturbances in modern power systems. This paper proposes a Newton–Raphson-Based Optimizer (NRBO) for tuning proportional, integral, and feedforward gains of a grid-forming converter applied to a wind energy conversion system operating in a low-inertia environment. The study considers an aggregated wind farm modeled as a single equivalent DFIG-based wind turbine connected to an infinite bus, with detailed dynamic representations of the converter control loops, synchronous generator dynamics, and network interactions formulated in the dq reference frame. The grid-forming converter operates in a grid-connected mode, regulating voltage and active–reactive power exchange. The NRBO algorithm is employed to optimize a composite objective function defined in terms of voltage deviation and active–reactive power mismatches. Performance is evaluated under two representative scenarios: small-signal disturbances induced by wind torque variations and short-duration symmetrical voltage disturbances of 20 ms. Comparative results demonstrate that NRBO achieves lower objective values, faster transient recovery, and reduced oscillatory behavior compared with Differential Evolution, Particle Swarm Optimization, Philosophical Proposition Optimizer, and Exponential Distribution Optimization. Statistical analyses over multiple independent runs confirm the robustness and consistency of NRBO through significantly reduced performance dispersion. The findings indicate that the proposed optimization framework provides an effective simulation-based approach for enhancing the transient performance of grid-forming wind energy converters in low-inertia systems, with potential relevance for supporting stable operation under increased renewable penetration. Improving the reliability and controllability of wind-dominated power grids enhances the delivery of cost-effective, cleaner, and more resilient energy systems, aiding in expanding sustainable electricity access in alignment with SDG7. Full article

High penetration of wind farms into the power grid lowers system inertia and compromises stability. This paper proposes a grid-forming control strategy for Permanent Magnet Synchronous Generator (PMSG) wind turbines based on DC-link voltage matching and virtual inertia. First, a relationship between grid frequency and DC-link voltage is established, replacing the need for a phase-locked loop. Then, DC voltage dynamics are utilized to trigger a real-time switching of the power tracking curve, releasing the rotor’s kinetic energy for inertia response. This is further coordinated with a de-loading control that maintains active power reserves through over-speeding or pitch control. Finally, the MATLAB/Simulink simulation results and RT-LAB hardware-in-the-loop experiments demonstrate the capability of the proposed control strategy to provide rapid active power support during grid disturbances. Full article

To address the complex surface curvature, massive dimensions, and variable pitch angles of wind turbine blades, this paper proposes a climbing robot design based on a variable-cell mechanism. By dynamically adjusting the support span and body posture, the robot adapts to the geometric features of different blade regions, enabling stable and efficient non-destructive inspection operations. Two reconfigurable configurations—a planar quadrilateral and a regular hexagon—are proposed based on the geometric characteristics of different blade regions. The configuration switching conditions and multi-leg cooperative control mechanisms are investigated. Through static stability margin analysis, the stable gait space and maximum stride length for each configuration are determined, optimizing the robot’s motion performance on surfaces with varying curvature. Simulation and experimental results demonstrate that the proposed multi-configuration gait planning strategy exhibits excellent adaptability and climbing stability across segments of varying curvature. This provides a theoretical foundation and methodological support for the engineering application of robots in wind turbine blade maintenance. Full article

This paper addresses Region-3 control of a 2.5 MW three-bladed HAWT using a data-driven workflow that links empirical modeling to implementable pitch control. To focus on fundamental regulation dynamics, the turbine is modeled as a rigid single-mass drivetrain driven by identified quasi-steady aerodynamics. First, we identify a compact shaft-power surface $P(\omega ,V,\beta )$ and recover the associated MPP condition, which clarifies why the optimal rotor speed rises with wind and motivates a comparison between capped-MPP operation and constant-speed regulation. We then synthesize a practical Region-3 loop—PI in rate with a first-order pitch servo and saturation handling—and evaluate proportional (P), PI, and PI + servo controllers under sinusoidal and Kaimal-turbulent inflow. Finally, we propose an adaptive PI variant that keeps a fixed acceleration feed-through but retunes the integral path online via ARX(1,1) + RLS to maintain a target closed-loop bandwidth. Performance metrics computed over the full simulation window (t ∈ [0, 50] s) show that P-only control exhibits large steady bias and cap violations; PI recenters speed and power around their targets; adding a pitch servo further trims peaks and ripple. In steady-state turbulent tests, PI + servo achieves tight regulation, Δω_peak ≈ 0.033% (0.079 rad/s), P_RMS ≈ 0.62%, while the adaptive PI maintains similar tightness with the lowest variability overall Δω_peak ≈ 0.0104% (0.025 rad/s), P_RMS ≈ 0.17. The workflow yields a practically implementable β(V) schedule and a lightweight adaptation mechanism that compensates for slow aerodynamic performance drift without changing the control structure. While structural loads and aeroelastic modes are not explicitly modeled, the proposed controller enforces strict speed and power constraints via a rigid-body dynamic analysis. Extensions to IPC, preview/forecast augmentation, and validation on higher-fidelity aeroelastic/drivetrain models are identified as future work. Full article

As the proportion of renewable energy generation in the power grid continues to rise, the operational state of the power system changes frequently with fluctuations in renewable power output. However, the traditional fixed-weight multi-objective reactive power optimization method lacks the necessary flexibility and adaptability, as it is unable to dynamically adjust the priority levels of different objectives based on real-time operating conditions (such as load fluctuations and changes in network structure). As a result, its optimization decisions may deviate from the system’s most urgent economic or security needs. To address this issue, this paper proposes an adaptive multi-objective reactive power optimization control method. The proposed approach formulates the objective function as the weighted sum of system active power loss and voltage deviation at the grid connection point, with weight coefficients adaptively adjusted based on the voltage deviation at the grid connection point. First, the relationship between voltage fluctuations at the offshore wind farm grid connection point and active/reactive power output is analyzed, and a corresponding reactive power allocation model is established. Second, taking into account the input–output characteristics of wind turbine generators and static var compensators, a reactive power control model is constructed. Third, considering offshore operational constraints such as power and voltage limits, a weighted variation particle swarm optimization algorithm (WVPSO) is developed to solve for the reactive power control strategy. Finally, the proposed method is validated through tests using a practical offshore wind farm as a case study. The test results demonstrate that, compared with the traditional fixed-weight multi-objective reactive power optimization approach, the proposed method can rapidly adjust the priority of each optimization objective according to the real-time grid conditions, achieving effective coordinated optimization of both active power loss and voltage at the grid connection point, and the voltage deviation is kept within 5%, even with power system fluctuations. In addition, compared with the traditional PSO algorithm, for various test situations, WVPSO exhibits above 15% improvement in solution speed and enhanced solution accuracy. Full article