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All rotating electrical machines are susceptible to vibrations arising from electromagnetic (EM) forces, electrical faults, mechanical defects, imbalance, and structural resonance. In Doubly Fed Induction Generators (DFIGs), such electromechanical vibrations are especially important because they can degrade reliability, increase noise, and lead to severe damage if resonance-prone operating conditions are not identified in time. Although fault diagnosis in DFIGs has been widely investigated using current, voltage, and flux signatures, comparatively fewer studies have examined fault-specific vibration behaviour under stator and rotor interturn faults (ITTFs), particularly through a coupled EM structural framework. In addition, prior vibration-based studies have not examined the influence of end winding ITTFs, its location, severity, and modal interaction investigating resonance risk. This paper considers vibration characteristics of a variable-speed 2.8 MW DFIG used in a grid-connected Type-3 wind turbine unit (WTU) at no-load operating condition. The DFIG is modelled in ANSYS Academic Research v 2022 R2 Maxwell for EM behaviour assessment for ITTFs in both stator and rotor windings along with modal analysis (MA) in ANSYS Workbench to examine the undamped stator and rotor modes over a range of frequencies. This coupled approach enables identification of vibration signatures associated with different ITTF types. The results show the magnetic flux density near faulty end-winding region increases with fault severity and ranges from 4.19 T to 4.39 T in proximity to faulty windings. A dominant modal frequency band of 60–65 Hz is identified, where stator and rotor modes coincide, creating probable resonance conditions. A severe vibration response is observed for single-phase stator ITTF, showing an amplitude of 2116 mm/s at 480 Hz for a larger number of shorted turns, indicating that asymmetric faults can produce stronger EM excitation than multi-phase faults. The main contribution of this paper is demonstration of a fault-specific, MA and vibration-based Condition monitoring system (CMS) implementation workflow for a DFIG. Unlike prior vibration-based studies that primarily focus on general machine vibration, mechanical faults, bearings, etc., this paper links stator and rotor ITTF induced EM excitation to modal characteristics, resonance behaviour, and measurable vibration signatures, establishing vibration analysis (VA) as a practical complementary technique for CMS of ITTFs in DFIGs. Full article

To address the challenges of complex multi-channel signal coupling and insufficient long-term temporal dependency characterization in remaining useful life (RUL) prediction of rotating machinery, this paper proposes a multivariate time series forecasting framework integrating multi-channel information fusion and a self-attention gated augmentation unit (SGAU). First, a multilayer perceptron (MLP) explicitly models nonlinear coupling among channels; SGAU replaces the conventional feed-forward network in the Transformer encoder, using multi-head self-attention outputs as gating signals to adaptively regulate feature transformation. Second, multi-channel signals are predicted via this framework; high-dimensional feature vectors are extracted to construct multi-channel Gaussian mixture models (GMMs). Third, Jensen–Shannon divergence (JSD) quantifies deviations between the target and initial data clusters; centroid distance evolutionary trajectory is fused with JSD to construct the health indicator (HI). Continuous HI predictions yield the RUL prediction curve. Experiments on a self-designed wind turbine gearbox platform and the XJTU-SY bearing dataset demonstrate that the proposed framework outperforms baseline methods on Mean Square (MS), Root Mean Square (RMS), and Energy metrics, with average error reductions of 6.6% and 12.1% in the horizontal and vertical directions on the gearbox dataset and 20.9% and 32.3% on the bearing dataset, confirming its effectiveness and generalization capability. Full article

The control of micro energy grids (MEGs) is characterized by volatility, uncertainty, and decentralization. Traditional power distribution algorithms, designed for centralized, dispatchable generators, are inadequate for MEG environments. Controllable load management provides peak shaving, load balancing, frequency regulation, and voltage stability, as well as fast balancing services for renewable energy grids in distributed power systems. A non-grid-tied inverter costs a fraction of its grid-tied counterpart for the same capacity. In the initial setting, one or more inverters are used. As the demand grows, more non-grid-tied inverters are added to the mix. Non-grid-tied inverters cannot be connected in parallel. There is no practical solution available in the market for the optimum utilization of this type of setting. Unlike a grid-tied microgrid, in non-grid-tied mode, a microgrid uses grid power only when needed, prioritizing renewable sources. This paper explores autonomous strategies for controlling and coordinating multiple renewable energy sources in MEG settings. It reviews and develops an algorithmic framework for optimal load distribution among multiple renewable sources, including solar photovoltaic (PV), wind turbines, and battery energy storage systems (BESSs). The proposed framework integrates resource forecasting, multi-objective optimization, and adaptive supervisory control to ensure stability, maximize renewable penetration, and minimize operational costs. Performance considerations, mathematical modelling, and potential implementation architectures are discussed. A hybrid approach, combining multiple algorithms, is therefore proposed. In this paper a real-life solution is proposed to a real-life problem. Full article

The integration of photovoltaic (PV) and wind turbine (WT) systems into modern power grids demands not only accurate component-level models but also a holistic understanding of their coordinated operation. This review bridges the gap between low-level device physics and high-level system coordination, offering a dual perspective often overlooked in existing surveys that treat generation and management separately. We systematically analyze PV models, from single-diode equivalent circuits to data-driven approaches, and WT models, ranging from aerodynamic and mechanical representations to simplified electrical equivalents suitable for stability studies. Critically, we then shift focus to the system level by examining energy management systems (EMS) that enable hybrid PV–WT coordination. Unlike prior reviews that emphasize either component accuracy or dispatch strategies alone, this paper highlights the emerging synergy between hybrid PV–WT modeling and EMS architectures. By identifying mismatches between model fidelity and EMS requirements, this review maps a pathway towards more integrated hybrid renewable systems. The discussion synthesizes key trade-offs in scalability, uncertainty handling, and real-time feasibility, underscoring that true potential is unlocked only through intelligent integration of component models and control architectures. Full article

The increasing penetration of wind generation into autonomous and weakly coupled industrial microgrids requires control strategies that can maintain power-supply reliability under stochastic generation and sharply variable loads. This paper proposes an adaptive corridor-based supervisory control algorithm for a lithium-ion battery energy storage system (BESS) integrated with a wind-turbine generator. The novelty of the method is not the general use of battery storage for power smoothing but a control law that maintains the generator within a predefined active-power corridor while transferring fast and medium-duration imbalances to the battery under state-of-charge, power-limit, and response-delay constraints. Unlike PI-based smoothing, model predictive control, or fixed rule-based switching, the proposed approach uses corridor retention as the primary operating criterion and relies only on directly measurable variables. The model was implemented in MATLAB/Simulink for a 2 MW wind-turbine generator coupled with a 444 kWh/1776 kW lithium-ion battery energy storage system. Field-measurement-based simulation validation was performed in MATLAB/Simulink using industrial load data measured at an autonomous oilfield power plant; the validation scenarios included extracted step disturbances, a real multi-peak load profile, prolonged deficit operation, and a scaled configuration scenario. The algorithm compensated for 86.3–87.4% of short-term load peaks, reduced the standard deviation of generator power from 467 to 98 kW, and decreased recovery time from 6.8 to 1.6 s. Full article

The high penetration of renewable energy has exposed integrated energy systems (IES) to stronger source-load uncertainties. Traditional scheduling methods that primarily pursue economic optimality often fail to account for system regulation margins, which may lead to excessive charging and discharging of energy storage systems, frequent fluctuations in unit output, and insufficient supply–demand matching capability under uncertain operating scenarios. To address these issues, this paper proposes a Flex-TD3 optimal scheduling method for IESs with embedded flexibility rules. First, a regional IES model incorporating photovoltaic generation, wind power, micro-gas turbines, gas boilers, electric chillers, waste heat recovery units, heat exchangers, and battery energy storage systems is established to describe the coupling relationships among electricity, heat, cooling, and gas flows, as well as the operational constraints of key devices. Second, active regulation flexibility indicators are constructed from the perspectives of system upward regulation capability, downward regulation capability, energy storage state health, and electro-thermal decoupling regulation margin. A comprehensive flexibility score is then formulated to characterize the system’s capability to cope with renewable energy fluctuations and load disturbances under the current operating state. Third, the flexibility indicators are embedded into the state space and reward function of the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm, and a rule-based physical feasibility mapping mechanism is introduced to modify the raw scheduling actions generated by the agent according to device operational constraints, thereby enhancing the physical consistency and operational safety of the scheduling strategy. Case study results show that, compared with traditional optimal scheduling methods, the proposed method achieves better overall performance in terms of training convergence speed, operational economy, and scheduling stability. It can effectively reduce system operating costs, improve renewable energy accommodation capability, and decrease renewable energy curtailment, supply shortages, and constraint violations. Under uncertain scenarios involving renewable energy prediction errors, load disturbances, and high renewable energy penetration, the proposed method still maintains favorable scheduling performance, demonstrating its effectiveness and robustness. Full article

In this paper, we address shaft oscillations and grid-connected oscillation frequency coupling in doubly fed induction generators (DFIGs) under DC-link dynamics. A comprehensive DFIG shaft system model incorporating DC-link dynamics is established, and frequency coupling is analyzed. From our findings, we reached the following conclusions: (a) DC-link voltage fluctuations alter electromagnetic torque through rotor-side converter (RSC) and grid-side converter (GSC) coupling, affecting shaft dynamics; (b) DC-link dynamics compromise grid connection stability by influencing both GSC and RSC output voltages. To mitigate these effects, a DC-link dynamics suppression module is proposed. Simulations confirm that in maximum power point tracking (MPPT) mode, the module enhances electrical positive damping and improves shaft stability. In constant power mode, its stabilizing effect is comparatively limited. The suppression module effectively mitigates grid-connected frequency coupling during DC-link voltage fluctuations. Full article

The rapid expansion of wind energy as a key pillar of sustainable electricity generation has intensified the need for reliable and efficient wind turbine operation, particularly in minimizing failures of critical components such as gearboxes, which significantly impact maintenance costs, downtime, and overall lifecycle sustainability. This study proposes a vibration-based fault diagnosis framework integrating Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and a Multiscale Convolutional Neural Network (MSCNN) for wind turbine gearbox condition monitoring. The approach decomposes non-stationary vibration signals into Intrinsic Mode Functions (IMFs) to capture meaningful oscillatory characteristics, which are then processed through parallel multiscale convolutional branches to learn both transient and long-term signal patterns. Experimental validation using the NREL Gearbox Reliability Collaborative dataset demonstrates that the proposed CEEMDAN-MSCNN model demonstrates strong performance compared to conventional machine learning methods and single-scale CNN architectures, achieving 99.50% accuracy on an unseen holdout dataset. The proposed framework supports predictive maintenance strategies by enabling reliable fault diagnosis, reducing unplanned downtime, and improving the operational efficiency and long-term sustainability of wind energy systems. Full article

Offshore renewable energy is widely recognised as a critical pathway for decarbonising electricity systems, but the integration of floating offshore wind turbines with wave energy converters remains technically challenging. This paper presents a structured literature review of hybrid wave–wind offshore energy platforms, drawing on 114 reviewed sources published between 2000 and 2026. The review classifies hybrid concepts using a three-axis framework based on floating platform type, wave energy converter (WEC) integration approach, and energy-dominance category. It then compares representative configurations, including point absorbers, oscillating water columns, flap-type devices, and heaving torus concepts, with emphasis on hydrodynamic response, energy contribution, structural complexity, mooring implications, validation status, and optimization suitability. The findings show that no single hybrid configuration can be ranked as universally superior because reported performance depends strongly on platform geometry, WEC scale, site wave climate, modelling assumptions, and validation maturity. Point absorber systems offer modularity and lower integration complexity, oscillating water column (OWC)-based systems provide protected power take-off (PTO) integration and moderate hydrodynamic interaction, flap-type systems can provide stronger motion-control potential but impose higher structural and mooring demands, and spar–torus concepts remain geometrically compatible with spar platforms but are generally wind-dominated. The review further shows that optimization method selection should depend on problem class: gradient-based methods are most suitable for local PTO tuning, evolutionary methods for non-convex multi-objective layout problems, surrogate-based methods for high-cost coupled simulations, and data-driven methods for adaptive control. The paper concludes that future progress requires standardized benchmark models, transparent evidence-level reporting, multi-physics co-optimization, techno-economic assessment, and systematic experimental or field validation before definitive concept ranking or commercial-readiness claims can be made. For decision-makers, industry stakeholders, and policymakers, the framework supports early-stage concept screening, identification of technology-specific risk factors, prioritisation of validation and investment pathways, and alignment of hybrid-platform development with site conditions, infrastructure constraints, and policy objectives. Full article

Maintaining stable operation and maximum power extraction in wind turbines under significant wind speed variations remains a key challenge in wind energy systems. This study aims to analyze the stability and maximum power point operation of a wind turbine equipped with a squirrel-cage induction generator using stator-side frequency control. This study examines the operational performance of medium-power wind turbines in the kilowatt range under significant wind speed variability. The analysis focuses on a turbine equipped with a squirrel-cage induction generator and a control architecture that incorporates a power converter integrated into the stator circuit. The findings show that adjusting the stator frequency through the converter allows the generator to track the optimal rotational speed, ensuring operation at the maximum power point across a wide range of wind conditions. Based on these results, the study defines the stable operating region of the turbine under time-varying wind speeds, making it suitable for distributed energy projects in coastal regions where wind can be highly variable. It also shows that, for a given electrical load, the generator must be calibrated to an appropriate maximum stator frequency to maintain stable and efficient energy conversion. Full article

Wind turbine gearbox failures lead to substantial downtime and high maintenance costs. Although condition-monitoring systems are widely used, traditional life-extension methods that simply reduce power output often decrease revenue. Current research frequently treats life optimization and power generation independently, and as such lacks a quantitative link between control strategies and remaining useful life. To address this gap, this paper proposes a novel life-extension strategy that optimizes power generation by dynamically adjusting rotor speed and pitch angle. A transfer learning–long short-term memory model enhanced by multi-source information fusion is developed to predict remaining useful life accurately under conditions with limited fault data. Utilizing real operational data from 2 MW wind turbines in Northeast China, the study quantitatively analyzes the impact of variable-speed and pitch control. The results demonstrate that while both strategies extend life, variable-speed control offers superior effectiveness in improving remaining useful life. Furthermore, maximum power generation is achieved not at full capacity, but when the output is reduced to approximately 70% of the nominal power. At this optimal point, the proposed strategy increases power generation by up to 7.3%. This establishes a dynamic balance between operational safety and economic efficiency, overcoming the limitations of conventional methods. Full article

Islanded renewable microgrids must balance power internally, so day-ahead dispatch is affected by wind and photovoltaic variability, battery state-of-charge (SOC) dynamics, demand-response (DR) participation, and emissions from dispatchable generation. This paper proposes a low-carbon economic dispatch model for an islanded photovoltaic–wind-turbine–battery-energy-storage–dispatchable-generator–demand-response (PV-WT-BESS-DG-DR) microgrid. The objective includes fuel, operation and maintenance, BESS degradation, renewable curtailment, load shedding, DR compensation, and carbon-emission costs. A repair-based constraint-handling strategy keeps the search space continuous while enforcing power balance, DG ramping, BESS operating and SOC limits, terminal SOC, and DR constraints. An improved whale optimization algorithm (WOA) is then developed with three modules: diversity enhancement, exploration–exploitation balancing, and local escape and refinement. The method is assessed through base-case dispatch, benchmark comparison, strategy comparison, ablation tests, and sensitivity analysis. In 30 independent runs, the proposed method achieves a mean cost of 2662.96 CNY/day, 4.07% lower than standard WOA, and reduces the standard deviation by 79.72%. Wilcoxon and Friedman tests confirm significant differences from the benchmark algorithms. Sensitivity tests show that higher BESS degradation coefficients and carbon prices increase the accounting cost but do not change the qualitative feasibility of the deterministic dispatch framework. Full article

Accurate prediction of pile-cap strain is crucial for the safety of wind-turbine foundations, yet conventional methods struggle to screen key features from high-dimensional monitoring data and to model the nonlinear coupling between endogenous and exogenous variables, hindering both accuracy and interpretability. To address these limitations, this paper proposes a pile-cap-strain prediction method integrating XGBoost-SHAP feature selection with the TimeXer deep-learning model. XGBoost-SHAP first identifies critical predictors from high-dimensional pile-stress data; the TimeXer model then exploits its endogenous–exogenous fusion mechanism for strain prediction. The results show that XGBoost-SHAP effectively selected 10 key features, of which the upper-middle and middle windward-side stresses (Z1-4A, Z1-5A) contributed over 40% of the explanatory power. This stage performs dimensionality reduction and sensor-importance interpretation, halving the input dimensionality while maintaining accuracy comparable to the full 19-channel input. TimeXer achieved a coefficient of determination (R²) of 0.993 in single-step prediction, comparable to the best-performing baselines, and maintained stable performance over a 120 min multi-step horizon. In a zero-shot cross-site transfer test, TimeXer attained the highest eight-step average R² (0.914) among all models, indicating strong cross-site generalization. Attention-mechanism visualization further suggested consistency between the model’s prediction logic and structural mechanics principles. The proposed framework provides a technical solution combining high accuracy with strong interpretability for wind-turbine foundation health monitoring. Full article

This paper introduces a new combination of hybrid energy storage in a split-shaft wind energy conversion system based on a hydraulic transmission system. In the hybrid energy storage, a flywheel, supercapacitor, and battery are integrated into the wind energy conversion system with minimal additional supporting hardware. The split-shaft configuration allows the direct connection of the flywheel to the doubly fed induction generator (DFIG) shaft without a power electronic converter. The principal operation and minimization of this hybrid storage, as well as the energy management strategy, are explained. The goal is to smooth out output power fluctuations using the response surface method. A 1.5 MW hydraulic wind turbine is simulated in Matlab 23, and the hybrid storage is configured and optimized. The direct connection of the flywheel facilitates reaching a suitable level of smoothness at a reasonable cost. The proposed configuration is compared with conventional storage, and the results demonstrate that the integrated hybrid energy storage reduces the annualized storage cost by 71%. Full article

Energy-intensive industries deploying hybrid renewable energy systems require performance monitoring frameworks that evolve with phased system implementation. This paper introduces the performance and sustainability framework, a simulation-grounded evolution of the sustainability balanced scorecard for longitudinal assessment of renewable energy infrastructure. The framework requires that key performance indicators derive from validated techno-economic simulations, that assessment is repeated at temporal checkpoints corresponding to physical system changes, and that each balanced scorecard perspective includes at least one environmental or circular-economy indicator. The framework is demonstrated in a cheese manufacturing facility in Crete, Greece, where a 38 kW cheese whey biomass generator, 72.2 kW photovoltaic system, and 10 kW wind turbine are deployed over five years. Annual HOMER Pro re-simulations are combined with weighted SWOT scoring to track technical, economic, environmental, and organisational performance. By Year 5, the system achieves an 88.7% electrical renewable fraction, 60.0% gross-operational CO₂-eq reduction, 0.1148 EUR/kWh levelised cost of energy, and 22.3% internal rate of return. The longitudinal trajectory also reveals declining delivered thermal renewable contribution and cheese whey utilisation, exposing operational trade-offs that single-point scorecard assessments would miss. Applicability of the PSF to community-scale governance under ISO 37101:2016 and to renewable energy communities under Directive (EU) 2018/2001 is examined exclusively as a conceptual scaling framework for future research. The present empirical demonstration is restricted to a single-facility case study, and no community-level stakeholder data are collected or analysed. Full article