Search Results (485)

This monograph synthesizes the current state of knowledge on mathematical models and wind turbine (WT) modeling methods, in particular in the field of implementation of wind turbine emulators (WTE). The areas covered include the methods used to create a physical and simulation model of a WT and its individual components, as well as an analysis of published results of research conducted using WTEs. The analysis of wind turbine generator inertia modeling and its impact on accurate simulation of WT operation in dynamic states is given special attention in this publication. This publication provides in-depth analysis of the dynamic properties of WTEs. The analysis identifies the shortcomings and limitations of existing models. As a result of the research, an original and effective method was proposed for creating an emulator of a real WT with a doubly fed induction machine. Achieving significantly better simulation accuracy in dynamic states than in models found in scientific publications. The tests results presented in the paper proved that the dynamic properties of the emulator developed by the authors allow its use in research and development work in a laboratory on a real turbine. The proposed solution was used to develop a scalable emulator of a real WT utilizing the physical properties of a doubly fed machine which is an element of the emulator. Full article

This paper reviews and discusses the behavioural modelling techniques used to simulate the dynamic behaviour of switching metal-oxide-semiconductor field-effect transistors (MOSFETs) and insulated gate bipolar transistors (IGBTs) employed in power electronic converters. It is necessary to develop efficient, accurate and computationally fast models for simulating the efficiency and power losses of power converters used in modern applications such as electric vehicles (EVs), solar photovoltaic (PV) systems, wind turbines (WTs) and other energy systems. In this context, the paper focuses on the approaches used to estimate the switching losses of these devices. The paper discusses the main differences, advantages, and drawbacks of the behavioural modelling methods presented in the literature including average models, charge-based models, and other physical models. The paper focuses on the Miller Plateau phenomenon in these devices, as it plays a major role in calculating the switching losses of power electronic converters. The paper provides a comprehensive review of the different modelling methods in terms of accuracy, computational effort, execution speed, and feasibility for hardware-in-the-Loop (HiL) systems. The paper also discusses the modern data-driven methods and their potential integration into future power electronic systems. At the end, the review identifies some research gaps and highlights promising directions for future behavioural modelling research. Full article

Recycled glass fiber-reinforced polymer (GFRP) powder from decommissioned wind turbine blades offers sustainable reinforcement for wood–plastic composites (WPCs), but its efficiency is limited by poor interfacial adhesion with the polypropylene (PP) matrix caused by surface epoxy residues. In this study, GFRP/WPCs with a fixed formulation of 15 wt% GFRP, 10 wt% wood flour, and 75 wt% PP were used to compare two modification strategies: MAPP compatibilization (1–7 wt%) and H₂O₂ treatment (5–30%). MAPP modification improved the mechanical properties of GFRP/WPCs, with the optimal concentration varying by property: flexural strength reached its maximum (75.45 MPa, +24.7%) at 1 wt% MAPP, while tensile strength (+9.2%), flexural modulus (+20.7%), and impact strength (+18.9%) were maximized at 3 wt% MAPP. H₂O₂ at 10% achieved higher strength gains (tensile: +23.65%, i.e., 26.7 MPa; flexural: +27.93%, i.e., 77.4 MPa; impact: +35.29%, i.e., 16.98 kJ/m²) but moderately reduced flexural modulus. FTIR confirmed up to 71.7% epoxy removal by H₂O₂, exposing cleaner fibers. Both modifications slightly lowered thermal decomposition temperatures but increased char residues and PP crystallinity via enhanced nucleation. SEM showed that MAPP created a compatible interphase, while H₂O₂ enabled direct mechanical interlocking. Surface free energy analysis revealed that MAPP increased polar components, whereas H₂O₂ increased dispersive components. Overall, MAPP offers simpler processing and balanced properties, while H₂O₂ provides superior strength at the cost of some stiffness—providing practical guidance for tailoring recycled GFRP/WPCs for construction and sustainable applications. Full article

The stable crosslinked structure of glass-fiber-reinforced epoxy composites in end-of-life wind turbine blades complicates their recycling and reuse. Mechanically ground blade powder can be incorporated into new epoxy systems, but its heterogeneous surface composition and limited compatibility with the matrix restrict its reinforcing efficiency. In this study, sequential NaOH activation and KH560 treatment were used to regulate the surface characteristics of recycled wind-turbine-blade powder. FTIR and direct powder XPS measurements revealed changes in the hydroxylated silicate environment and the introduction of KH560-associated organic and organosilicon surface species. Representative SEM observations showed greater resin attachment and a more integrated local powder-matrix morphology after sequential treatment. At a fixed recycled-powder loading of 40 phr, corresponding to approximately 28.4 wt% of the total uncured mixture, EP-KH560 achieved mean flexural and tensile strengths of 67.00 and 38.81 MPa, respectively. These values were 47.8% and 57.1% higher than those of the composite containing untreated powder. Under the fixed formulation and processing conditions investigated, the results demonstrate that sequential NaOH/KH560 treatment improves the interfacial compatibility and comparative mechanical performance of recycled wind-turbine-blade powder/epoxy composites. Full article

This study considers the multi-objective optimization of a standalone hybrid nanogrid system (HNGS) providing electricity power to a residential load in an off-grid area of Djelfa Province, Algeria. The focus of this study is to obtain the optimum design of a standalone hybrid nanogrid system consisting of photovoltaic (PV) panels, wind turbines (WTs), battery storage (BT), diesel generators (DGs), and power converters to satisfy the energy demand of residential consumers in Djelfa Province, Algeria. In this context, four multi-objective optimization algorithms (MOPs), NSGA-II, MOPSO, MOSSA, and MODE, are used to solve the optimal sizing problem of the proposed system. The formulated multi-objective optimization problem takes into account multiple performance criteria such as cost of energy (COE), loss of power supply probability (LPSP), renewable energy penetration, and diesel generator usage reduction, balancing economic, reliability, and sustainability aspects. The optimization process optimizes critical design parameters, including the size of the PV system, the number of wind turbines, and the size of the battery storage system, for a realistic operating scenario. The optimization algorithms are combined with an energy management strategy (EMS) that helps to coordinate the power flow distribution between various parts of the system to achieve optimum system performance. The effectiveness of each of the proposed approaches is analyzed based on the obtained results, where it was found that the MODE algorithm provides the best compromise solution, with a COE of 0.167 USD/kWh and an LPSP of 6.372%, and the lowest carbon dioxide emissions of 205.1 kg/year compared to MOPSO, NSGA-II, and MOSSA. Moreover, the results obtained from this process will provide a set of feasible design solutions, which will allow decision-makers to choose the most suitable design solution based on technical and economic specifications. Full article

An advanced power control and energy management strategy for an isolated hybrid microgrid system is presented in this paper. With the help of a battery energy storage system (BESS), the architecture combines a wind turbine (WT) emulator, photovoltaic (PV) arrays, and a variable-speed diesel generator (VSDG) emulated by a controlled DC source on a 1/22 reduced-scale laboratory platform. Three-level converters are used in a robust power control method to reduce the inherent intermittency of renewable sources and the stochastic nature of isolated loads. These converters are used to improve power quality, lower harmonic distortion, and control dynamic interactions between the sources in real time. The main goal is to minimize the VSDG contribution and estimated fuel consumption while optimizing renewable energy penetration, with fuel consumption reduction assessed through power dispatch analysis rather than direct measurement. Comprehensive simulations and real-time experimental prototyping using the dSPACE (CP1104) controller on a 1/22-scale platform were used to validate the proposed control strategy. Results from both simulations and experiments support the strategy’s viability and efficacy in preserving system stability and maximizing energy dispatch under various load profiles. Full article

Existing DG allocation studies commonly rely on static or balanced feeder assumptions, leaving two practical issues insufficiently addressed: the hourly mismatch between renewable outputs and feeder demands and diesel-backup phase-imbalance safety in unbalanced networks. This paper presents a safety-aware allocation framework for hybrid photovoltaic (PV) and wind turbine (WT) systems in unbalanced three-phase feeders. The methodology explicitly accounts for 24 h generation–load coordination and diesel backup operating limits through two post-load-flow indicators: load-following error (LFE), which measures the hourly mismatch between aggregate distributed generation (DG) outputs and a load-proportional target, and power unbalance ratio (PUR), which limits diesel-unit phase-power imbalance to 10% during dispatch. The constrained siting and sizing problem is solved using a genetic algorithm (GA) on the IEEE 37-bus feeder under realistic diurnal load, solar, and wind profiles. While an unconstrained allocation achieves 64.57% active-power loss reduction, it exceeds the adopted diesel PUR screening threshold. Enforcing the PUR constraint yields a feasible operating state with 60.87% loss reduction, retaining 94.27% of the unconstrained benefit. Robustness checks across 30 independent runs and GA/PSO/ACO benchmarking confirm that the adopted GA provides the lowest dispersion and highly repeatable feasible outcomes. The results show that the framework improves energy efficiency, voltage regulation, and daily coordination while satisfying the adopted diesel phase-power screening criterion under severe feeder asymmetry. Full article

Accurate ultra-short-term wind power forecasting is of great significance for grid integration scheduling and the secure operation of power systems. However, due to meteorological disturbances and turbine operating states, wind power series generally exhibit non-stationary, multi-scale fluctuations and strong nonlinearity. To improve forecasting accuracy, this paper proposes an ultra-short-term wind power forecasting model based on a two-stage signal decomposition and a hybrid architecture combining iTransformer, LSTM, and KAN. First, a cascaded decomposition module is constructed using the wavelet transform (WT) and ICEEMDAN to attenuate the non-stationarity of the original power series and to extract multi-scale features. An iTransformer branch is then employed to model global dependencies among multiple variables, while an LSTM branch captures temporal dynamics in the historical power series. Subsequently, a cross-attention mechanism is introduced to achieve cross-branch feature fusion, and a KAN output layer is adopted to enhance the model’s representation of the wind speed–power nonlinear mapping. A particle swarm optimization (PSO) algorithm, combined with a cosine annealing strategy, is used to optimize key hyperparameters and improve training stability. Experimental results using SCADA data from a 150 MW wind farm in southern Hunan Province show that the proposed model achieves an MAE of 9.8327 MW, an RMSE of 13.1872 MW, an SMAPE of 18.8474%, and an $R^2$ of 0.7798. These values correspond to the fixed main comparison protocol used for baseline evaluation, while the ablation study reports multi-seed mean and standard deviation results to assess module-level robustness. Compared with LSTM and WT-ICEEMDAN-CNN-LSTM, the proposed model achieves clear improvements in forecasting accuracy and fitting capability. Additional cross-wind-farm validation on a second wind farm shows that WT-ICEEMDAN-iTransformer-LSTM-KAN-PSO (hereafter referred to as ILKP) maintains the best overall performance, achieving an MAE of 27.2193 MW, an RMSE of 36.1862 MW, an SMAPE of 27.8429%, and an $R^2$ of 0.5189, demonstrating transferability and robustness under different operating conditions. Full article

The energy management (EM) of smart multi-carrier energy hub (SMCEH) systems for cost and emission reduction remains a challenging problem due to the diversity of renewable energy resources (RERs), varying load demands, and the stochastic nature of these resources. This paper addresses the EM problem of SMCEHs to minimize operational costs and greenhouse gas (GHG) emissions using the particle swarm optimization (PSO) algorithm. The studied SMCEHs are designed to simultaneously supply electrical, cooling, and thermal demands. The hub system comprises wind turbines (WTs), photovoltaic (PV) panels, gas turbines (GT), electric chillers (EC), gas boilers (GBs), absorption chillers (AC), battery storage systems, and thermal storage units. To assess system performance and the impact of key technologies, three case studies are investigated: (i) EM of SMCEHs without RERs, (ii) EM of SMCEHs with RERs, and (iii) EM of SMCEHs with RERs and an integrated carbon capture unit (CCU). These scenarios enable a systematic evaluation of the role of renewable integration and carbon capture in enhancing system performance. The results demonstrate that incorporating RERs into SMCEHs leads to a substantial reduction in both operational costs and GHG emissions. Furthermore, the integration of a CCU provides additional emission reductions, underscoring its effectiveness in supporting the low-carbon operation of SMCEHs. The obtained results show that integrating RERs into SMCEH decreases the total cost and emissions by 64.12% and 7.95%, respectively, compared to the scenario without RERs. Furthermore, the integration of the CCU into SMCEHs provides a 39.36% reduction in total costs and a 72.57% decrease in CO₂ emissions. The suggested energy management solution promotes a sustainable and low-carbon emission system by maximum utilization of the RERs and CCU. Full article

This paper proposes an optimal operational framework for enhancing the economic, technical, and environmental performance of a renewable energy-based microgrid. The proposed system integrates photovoltaic (PV) generation, wind turbines (WTs), battery energy storage systems (BESSs), diesel generators (DGs), and utility grid interaction. Three multi-objective optimization algorithms, namely Multi-Objective Particle Swarm Optimization (MOPSO), Multi-Objective Genetic Algorithm (MOGA), and Multi-Objective Celestial Orbit Optimization (MOCOO), are employed to minimize the total operating cost and grid dependency. The obtained results demonstrate that MOPSO achieves the best techno-economic performance with a minimum operating microgrid cost of 2.2 M$/year and a low grid dependency ratio of 0.0333. The operational analysis confirms that the proposed renewable-priority scheduling strategy significantly reduces operational emissions and reliance on the utility grid through coordinated BESS charging/discharging and efficiency-aware DG dispatch. The microgrid (MG) achieves zero-emission operation during operating periods dominated by renewable generation. Furthermore, the DG operates within an efficiency range of 36.8–39.3%, improving fuel utilization and reducing unnecessary emissions. The battery degradation analysis indicates high lifetime cycle capability under shallow depth-of-discharge operation, demonstrating improved long-term operational sustainability. Overall, the proposed framework provides a reliable and economically balanced solution for sustainable microgrid energy management. Full article

Accurate impedance modeling of wind turbines (WTs) is essential for assessing the small-signal stability of power systems with high penetration of renewable energy. Existing approaches face a fundamental trade-off: physics-based “white-box” models require proprietary manufacturer parameters that are rarely disclosed, while purely data-driven “black-box” models often lack physical interpretability and exhibit poor generalization under unseen operating conditions. To address this gap, this paper proposes a gray-box framework—the Physics-Informed Hybrid Model (PIHM)—that integrates a simplified physical impedance branch with a Bidirectional Long Short-Term Memory (Bi-LSTM) network in a novel parallel architecture. The physical branch, systematically parameterized via a constrained phase-error minimization method, captures the dominant baseline dynamics and decouples the learning task, allowing the Bi-LSTM to focus exclusively on the complex nonlinear residual. The framework is validated on a high-fidelity simulation platform of a doubly fed induction generator (DFIG) wind farm. Quantitative results demonstrate that the PIHM achieves an average coefficient of determination ($R^2$) of 0.989 and a mean squared error (MSE) of $1.24\times {10}^{-4}$ on unseen test data, while producing smooth, physically consistent impedance profiles that generalize across four distinct wind speed conditions. These results establish the PIHM as a reliable, parameter-free tool for impedance-based stability analysis of modern wind power systems. Full article

Wind turbine gearbox condition monitoring increasingly combines Supervisory Control and Data Acquisition (SCADA) data with Explainable Artificial Intelligence (XAI) for predictive maintenance. However, current XAI applications report attributions as static or globally aggregated feature-importance results. Such representations do not reveal when fault-related variables emerge, how dominance shifts between features, or how the explanatory structure evolves as degradation progresses. This limits their value for time-resolved diagnostic interpretation. To address this gap, this study proposes the Temporal Attribution Matrix (TAM), a temporal interpretability framework that tracks the evolution of XAI-derived feature importance across degradation periods. The central hypothesis is that temporal attribution patterns contain diagnostic information not captured by static feature-importance summaries. TAM was applied to a three-year SCADA dataset from Fuhrländer FL2500 wind turbines using XGBoost-SHAP and 1D-CNN Grad-CAM within sliding weekly windows. Four temporal measures were derived: feature onset time, dominance transition, attribution entropy, and cross-model consistency. Both XAI methods independently identified gearbox bearing temperatures 451 and 152 as the most influential features. TAM further revealed a synchronized thermal-feature onset on 23 October 2012, 14 SHAP dominance transitions compared with 70 Grad-CAM transitions, and a moderate cross-model Spearman correlation of 0.488. Secondary validation using WT82 confirmed TAM’s applicability beyond a single turbine. These results demonstrate that TAM extends static XAI by producing time-resolved degradation narratives for SCADA-based wind turbine predictive maintenance. Full article

Offshore wind energy offers abundant resources and significant potential for large-scale development. Efficient design of collection systems is critical to the economic viability of offshore wind farms (OWFs). This study proposes an upper–lower level topology optimization framework based on the Artificial Lemming Algorithm (ALA) to address the complexity arising from large numbers of wind turbines (WTs). At the upper level, wind turbines can be partitioned into different numbers of regions according to practical engineering requirements using the Radial Fuzzy C-Means (RFCM) clustering algorithm. At the lower level, the ALA is applied to optimize the collection system topology within each region, aiming to minimize total construction cost while satisfying operational constraints. A case study involving a 75-WT offshore wind farm is conducted. Comparative simulations against various heuristic algorithms including Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Differential Evolution (DE) show that the proposed method achieves faster convergence, lower total costs and greater robustness. Specifically, the ALA reduces the best cost by 9.9% and improves average runtime by 28.5%, indicating its advantages in best-cost search and computational efficiency in the tested case. In addition, based on 10 independent runs, the ALA achieves the lowest median cost of $6684\times {10}^4$ CNY, with an interquartile range of 6593–$6813\times {10}^4$ CNY and a cost range of 6362–$7087\times {10}^4$ CNY. Overall, the proposed framework provides a practical optimization approach for obtaining low-cost feasible collection-system layouts in the studied offshore wind farm case. 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

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