Search Results (4,863)

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

With accelerating urbanization and climate change, outdoor thermal comfort (OTC) in high-intensity urban blocks presents a critical challenge. While existing studies have established the general correlation between morphology and microclimate, most remain descriptive and lack a systematic framework to quantitatively integrate the non-linear coupled effects between multi-dimensional morphological variables and green infrastructure. To address this, this study proposes an automated performance-based design (PBD) framework for urban morphology optimization in Shenzhen. Unlike traditional simulation-based analysis, this framework serves as a generative tool for urban renewal planning. It integrates a multi-dimensional design element system with a genetic algorithm (GA) workflow. Analysis across four urban typologies demonstrated that the Full Enclosure layout is the most effective strategy for mitigating thermal stress, achieving a final optimized UTCI of 37.15 °C. Crucially, this study reveals a non-linear synergistic mechanism: the high street aspect ratios (H/W) of enclosed forms act as a “radiation shelter”, which amplifies the cooling efficiency of green infrastructure (contributing an additional 1.79 °C reduction). This research establishes a significant, strong negative correlation between UTCI and the combined factors of building density and green shading coverage. The results provide quantifiable guidelines for retrofitting existing high-density districts, suggesting that maximizing structural shading is prioritized over ventilation in ultra-high-density, low-wind climates. Full article

The main aim of this paper is the uncertainty quantification and reliability assessment of the hybrid skeletal telecommunication tower subjected to dynamic wind pressure. The structural response of this aluminum–steel construction is contrasted with the original steel tower solution widely available in engineering practice in the numerical environment of the system ABAQUS 2024. Some design parameters of both towers are considered uncertain and distributed according to the Gaussian probability distribution so that the resulting reliability indices in the Ultimate Limit State (ULS), as well as the Serviceability Limit State (SLS), are determined. These indices are calculated using the First Order Reliability Method (FORM), and also from the probabilistic entropy scheme due to the Bhattacharyya theory. The first two probabilistic characteristics necessary for the reliability assessment result from the Stochastic Finite Element Method implemented according to the generalized iterative stochastic perturbation technique. All probabilistic calculus is programmed in the symbolic algebra of the system MAPLE 2015. As it is documented in this study, a choice of the hybrid tower enables for some mass savings under preservation of the same reliability level. Full article

To address the limitations of parameter drift in physical models and poor generalization in data-driven methods, this paper proposes a self-evolving digital twin framework for PMSM thermal safety. The framework integrates a dynamic-batch Bayesian calibration (DBBC) algorithm and a hierarchical physics-aware network (HPA-Net). First, the DBBC eliminates plant–model mismatch by robustly identifying stochastic parameters from operational data. Subsequently, the HPA-Net adopts a “physics-augmented” strategy, utilizing the calibrated physical model as a dynamic prior to directly infer high-fidelity temperature via a hierarchical training scheme. Furthermore, a real-time demagnetization safety margin (DSM) monitoring strategy is integrated to eliminate “false safe” zones. Experimental validation on a PMSM test bench confirms the superior performance of the proposed framework, which achieves a Root Mean Square Error (RMSE) of 0.919 °C for the stator winding and 1.603 °C for the permanent magnets. The proposed digital twin ensures robust thermal safety even under unseen operating conditions, transforming the monitoring system into a proactive safety guardian. Full article

Floating offshore wind turbines (FOWTs) constitute a pivotal offshore renewable energy technology, offering a sustainable and eco-friendly solution for large-scale marine power generation. Their low-carbon emission characteristics are highly aligned with global sustainable development goals, playing a crucial role in promoting energy structure transformation and reducing reliance on fossil fuels. This paper presents a numerical study on the coupled dynamic behavior of a semi-submersible FOWT during its mooring system installation. The proposed methodology incorporates environmental loads from incident waves, wind, and currents. Those forces act on not only the floating platform but also on the three tugboats employed throughout the installation procedure. Detailed evaluations of forces and motion responses are conducted across successive stages of the operation. The findings demonstrated the feasibility of the proposed mooring installation process for FOWTs while offering critical insights into suitable installation weather windows and motion responses of both the platform and tugboats. Furthermore, the novel installation scheme presented herein offers practical guidance for future engineering applications. Full article

Renewable energy sources like wind turbines and solar panels are integrated into modern power grids as Distributed Energy Resources (DERs). These DERs can operate independently or as part of microgrids. Interconnecting multiple microgrids creates Networked Microgrids (NMGs) that increase reliability, resilience, and independent power generation. However, the trustworthiness of individual DERs remains a critical challenge in NMGs, particularly when integrating previously deployed or geographically distributed units managed by entities with varying expertise. Assessing DER trustworthiness ensuring reliability and security is essential to prevent system-wide instability. Thisresearch addresses this challenge by proposing a lightweight trust metric generation system capable of processing structured and semi-structured DER data to produce key trust indicators. The system employs a Small Language Model (SLM) with approximately 16 million parameters for textual data understanding and metric extraction, followed by a regression head to output bounded trust scores. Designed for deployment in computationally constrained environments, the SLM requires only 64.6 MB of disk space and 200–250 MB of memory that is significantly lesser than larger models such as DeepSeek R1, Gemma-2, and Phi-3, which demand 3–12 GB. Experimental results demonstrate that the SLM achieves high correlation and low mean error across all trust metrics while outperforming larger models in efficiency. When integrated into a full neural network-based trust framework, the generated metrics enable accurate prediction of DER trustworthiness. These findings highlight the potential of lightweight SLMs for reliable and resource-efficient trust assessment in NMGs, supporting resilient and sustainable energy systems in smart cities. 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

Dynamic positioning (DP) systems play a critical role in maintaining vessel position and heading under environmental disturbances such as wind, waves, and currents. This study presents a model predictive control (MPC)-based DP system for a fireboat equipped with a rudder–propeller configuration, explicitly accounting for both environmental loads and the reaction force generated during water cannon operation. Unlike conventional DP architectures in which DP control and thrust allocation are treated as separate modules, the proposed framework integrates both functions within a unified MPC formulation, enabling real-time optimization under actuator constraints. Environmental loads are modeled by incorporating nonlinear second-order wave drift effects, while nonlinear rudder–propeller interaction forces are derived through computational fluid dynamics (CFD) analysis and embedded in a control-oriented dynamic model. This modeling approach allows operational constraints, including rudder angle limits and propeller thrust saturation, to be explicitly considered in the control formulation. Simulation results demonstrate that the proposed MPC-based DP system achieves improved station-keeping accuracy, enhanced stability, and increased robustness against combined environmental disturbances and water cannon reaction forces, compared to a conventional PID controller. Full article

The large-scale integration of electric vehicles (EVs) presents both challenges and opportunities for power grid stability and renewable energy utilization. Vehicle-to-Grid (V2G) technology enables EVs to serve as mobile energy storage units, facilitating peak shaving and valley filling while promoting the local consumption of photovoltaic and wind power. However, uncertainties in renewable energy generation and EV arrivals complicate the scheduling of bidirectional charging in stations equipped with hybrid energy storage systems. To address this, this paper proposes a multi-stage rolling optimization framework combined with a fuzzy logic-based decision-making method. First, a bidirectional charging scheduling model is established with the objectives of maximizing station revenue and minimizing load fluctuation. Then, an EV charging potential assessment system is designed, evaluating both maximum discharge capacity and charging flexibility. A fuzzy controller is developed to allocate EVs to unidirectional or bidirectional chargers by considering real-time predictions of vehicle arrivals and renewable energy generation. Simulation experiments demonstrate that the proposed method consistently outperforms a greedy scheduling baseline. In large-scale scenarios, it achieves an increase in station revenue, elevates the regional renewable energy consumption rate, and provides an additional equivalent peak-shaving capacity. The proposed approach can effectively coordinate heterogeneous resources under uncertainty, providing a viable scheduling solution for EV-aggregated participation in grid services and enhanced renewable energy integration. Full article

With the widespread adoption of renewable energy, distribution grids face increasing challenges in efficiency, safety, and economic performance due to stochastic generation and fluctuating load demand. Traditional operational models often exhibit limited adaptability, weak coordination, and insufficient holistic optimization, particularly in early-/mid-stage distribution planning where feeder-level network information may be incomplete. Accordingly, this study adopts a planning-oriented formulation and proposes a distributed energy storage system (DESS) planning strategy to enhance distribution network resilience under high uncertainty. First, representative wind and photovoltaic (PV) scenarios are generated using an improved Gaussian Mixture Model (GMM) to characterize source-side uncertainty. Based on a grid-based network partition, a priority index model is developed to quantify regional storage demand using quality- and efficiency-oriented indicators, enabling the screening and ranking of candidate DESS locations. A mixed-integer linear multi-objective optimization model is then formulated to coordinate lifecycle economics, operational benefits, and technical constraints, and a sequential connection strategy is employed to align storage deployment with load-balancing requirements. Furthermore, a node–block–grid multi-dimensional evaluation framework is introduced to assess resilience enhancement from node-, block-, and grid-level perspectives. A case study on a Zhejiang Province distribution grid—selected for its diversified load characteristics and the availability of detailed historical wind/PV and load-category data—validates the proposed method. The planning and optimization process is implemented in Python and solved using the Gurobi optimizer. Results demonstrate that, with only a 4% increase in investment cost, the proposed strategy improves critical-node stability by 27%, enhances block-level matching by 88%, increases quality-demand satisfaction by 68%, and improves grid-wide coordination uniformity by 324%. The proposed framework provides a practical and systematic approach to strengthening resilient operation in distribution networks. 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

In the preliminary design stage of wind farms, the theoretical energy output must be adjusted by multiple reduction factors to estimate the actual grid-connected power. As renewable energy becomes increasingly integrated into electricity markets, the conventional approach using static, averaged reduction coefficients for annual yield estimation can no longer meet the market’s demand for high-resolution power time series. Addressing this gap, the novelty of this paper lies in shifting the focus from total annual estimation to hourly-level dynamic allocation. This paper proposes a time-series reduction coefficient evaluation method based on the time-varying entropy weight method (TV-EWM). Under the assumption that the total annual reduction quantity adheres to standard design specifications, this method utilizes long-term wind measurement data, integrates unique ultra-high-altitude wind resource characteristics, and constructs a scenario-based indicator system. By quantifying the coupling relationships between key meteorological variables and incorporating a dynamic weighting mechanism, the proposed approach achieves hourly refined reduction estimation for theoretical power output. Comparative analysis was conducted against the traditional static average reduction method. Results indicate that, compared to the traditional average reduction method, the TV-EWM approach significantly enhances the model’s ability to capture seasonal variability, increasing the coefficient of determination (R²) by 4.19% to 0.7061. Furthermore, it demonstrates higher stability in error control, reducing the Normalized Root Mean Square Error (NRMSE) by 4.51% to 15.45%. The TV-EWM more accurately captures the temporal evolution and coupling effects between meteorological elements and curtailed generation under various reduction scenarios, retains full-load operational features, and enhances physical interpretability and time responsiveness, providing a new analytical framework for market-oriented power generation assessment. Full article

Accurate short-term forecasting of renewable energy sources is essential for stable and efficient microgrid operation. Existing models primarily focus on either solar or wind prediction, often neglecting their combined stochastic behavior within isolated systems. This study presents a comparative evaluation of three machine-learning models—Random Forest, ANN, and LSTM—for short-term solar and wind forecasting in microgrid environments. Historical meteorological data and power generation records are used to train and validate three ML models: Random Forest, Long Short-Term Memory, and Artificial Neural Networks. Each model is optimized to capture nonlinear and rapidly fluctuating weather dynamics. Forecasting performance is quantitatively evaluated using Mean Absolute Error, Root Mean Square Error, and Mean Percentage Error. The predicted values are integrated into a microgrid energy management system to enhance operational decisions such as battery storage scheduling, diesel generator coordination, and load balancing. Among the evaluated models, the ANN achieved the lowest prediction error with an MAE of 64.72 kW on the one-year dataset, outperforming both LSTM and Random Forest. The novelty of this study lies in integrating multi-source data into a unified ML-based predictive framework, enabling improved reliability, reduced fossil fuel usage, and enhanced energy resilience in remote microgrids. This research used Orange 3.40 software and Python 3.12 code for prediction. By enhancing forecasting accuracy, the project seeks to reduce reliance on fossil fuels, lower operational costs, and improve grid stability. Outcomes will provide scalable insights for remote microgrids transitioning to renewables. Full article

Greenhouse gas emissions are a primary contributor to climate change and the observed rise in global temperatures. To reduce these emissions, renewable energy sources (RESs) must replace fossil fuels in power generation. Because of the mismatch between production and demand, the increase in RES is limited. To address this phenomenon, the addition of renewable energy generation should be accompanied by storage systems. In this paper, the island of Crete is examined for various renewable energy generations and storage capacities using the PowerWorld Simulator software. Four main scenarios are studied in which the installed renewable energy generation is increased to reach substation limits. For every scenario, different renewable energy generation mixes are considered between wind farms and photovoltaics. Furthermore, for all sub-scenarios, different storage capacities are considered, ranging from 1.6 GWh to 12.8 GWh. This study proves that storage systems are mandatory to increase renewable energy penetration. In certain scenarios, a battery energy storage system can further increase renewable energy penetration from 6.15% to 28.07%. Although the battery energy storage system enhanced renewable penetration, increasing transmission line capacities should also be considered regarding the scenario. 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