Wind Turbine Research Articles

A Novel Fuzzy Logic EMS for Hybrid Microgrids with Photovoltaic, Wind, Fuel Cell, and Energy Storage Integration

This paper presents an innovative Energy Management Strategy (EMS) for a hybrid microgrid that combines two main renewable energy sources (RESs), photovoltaic (PV) and wind turbine (WT) generators working at the maximum power point (MPP) to extract the maximum available energy, and an energy storage system (ESS) based Lithium batteries to ensure DC-bus stability. This system includes a backup device based on Fuel Cell (FC) to assure system dependability and minimize blackouts, as well as power electronic converters for flexible system parts and a residential community as DC loads. The proposed EMS is based on the advanced Fuzzy Logic Controller (FLC), which regulates voltage and manages power flow inside the microgrid. MATLAB/Simulink simulations were conducted to assess the performance of innovative microgrid stability strategy. A comparative analysis with conventional EMS approaches demonstrates the superior effectiveness and viability of the proposed FLC technique in energy management within microgrids. The simulation-based comparison indicates that the proposed optimization method enhances system performance and DC-bus stability.

Design and analysis of a CO2-to-olefins process, using renewable energy

This paper presents a light olefins production plant that hydrogenates carbon dioxide to C2-C4 using green hydrogen and electricity produced from solar and wind energy resources. In this regard, combining Fischer-Tropsch and methanol-mediated pathways on a large scale is analyzed as a new method to increase light olefins production. Additionally, an optimized hybrid renewable energy system comprised of solar panels, wind turbines, electrolyzers, batteries, converters, and so on is designed to supply the necessary utilities for the plant. The simulation results indicate that 590.9, 744.8, and 522.9 kg/h of ethylene, propylene, and butylene can be produced by processing 10% of carbon dioxide emitted from a cement factory, resulting in the negative emission of 2.14 kg CO2/kg C2-C4. This plant needs 1420 kg/h of hydrogen to convert carbon dioxide into light olefins and 32 MW of electricity to meet hot, cold, and electric utility requirements, all powered by renewable energy. The optimization results demonstrate that the initial capital and net present costs of the renewable energy system are $1.15B and $1.38B, respectively, leading to the levelized costs of hydrogen and electricity of 3.56 $/kg and 0.12 $/kWh, respectively.

Dynamic event‐triggered‐based adaptive frequency control of microgrids under cyber‐attacks via adaptive dynamic programming

AbstractThe increasing penetration of renewable energy sources (RES) and the development of the cyber‐physical microgrids (CPMs) make greater demands for frequency control of microgrids. The common approach for frequency control is controlling the micro‐turbine to compensate for frequency deviations, with energy storage systems serving as an auxiliary approach. This article proposes an online adaptive frequency control method to control the governor and energy storage to realize the frequency recovery of microgrid, subject to the external unknown disturbances caused by wind turbine, power load and false data injection (FDI) attacks. First, the non‐zero sum (NZS) games of the considered system are modeled in this work, where the unknown disturbances are also taken into account. For the sake of estimating the unknown disturbances, a disturbance observer (DOB) for the microgrid system is introduced. Then, on the basis of the estimated results of the applied DOB, the disturbance compensation input is derived to offset the interference of the unknown disturbance. Meanwhile, the adaptive dynamic programming (ADP) approach is employed to derive the adaptive optimal control input for the NZS games of microgrid system. Besides, the dynamic event‐triggered (DET) control is introduced, reducing the occupation of computing resources. By utilizing the Lyapunov's method, the stability of the closed‐loop system, the convergence of the estimation weight, the estimation disturbances and the system state are guaranteed. The effectiveness of the proposed method is ultimately verified by the simulation results.

Virtual inertia and damping‐based cascaded control approach for enhancing load frequency control in low‐inertia multi‐area power systems

AbstractThis paper introduces a comprehensive cascaded controller to enhance transient and dynamic stability in load frequency control for low‐inertia multi‐area power systems (LIMAPSs) integrating solar photovoltaic and wind turbine sources. The proposed method combines a composite energy storage‐based virtual synchronous generator with a novel cascaded controller employing an adaptive neuro‐fuzzy inference system (ANFIS) and a fractional‐order proportional–integral with a fractional‐order proportional‐tilt‐integral (FOPI‐FOPTI) control strategy. Firstly, a dynamic model of a thermal power plant incorporating photovoltaic, wind turbine, and virtual synchronous generator is developed. Subsequently, the ANFIS‐FOPI‐FOPTI controller parameters, optimized via the whale optimization algorithm, provide adaptive and robust frequency stability control under disturbances. Finally, extensive simulations demonstrate the proposed approach's effectiveness in enhancing the transient and dynamic stability of LIMAPSs. The simulations are conducted on two‐area power systems and a modified IEEE 10‐generator 39‐bus system, considering disturbances like stochastic load‐generation. Comparative analysis with existing controllers shows the proposed cascaded controller effectively addresses load frequency control challenges in LIMAPSs. It outperforms other control approaches in terms of settling time, overshoot, and specified objective functions, with the lowest integral time absolute error values recorded: 0.1202 for the two‐area system and 0.480 for the modified IEEE 39‐bus four‐area power system.

Frequency Constrained Dispatch With Energy Reserve and Virtual Inertia From Wind Turbines

Frequency Constrained Dispatch With Energy Reserve and Virtual Inertia From Wind Turbines

Fast Power Regulation Method During System Restoration for D-PMSG-Based Wind Turbines

Fast Power Regulation Method During System Restoration for D-PMSG-Based Wind Turbines

Windfarm construction alters soil multinutrient cycling by destabilizing microfauna community in a mountain ecosystem.

In recent decades, investors attracted to wind power's promise of zero-emission electricity have fueled the proliferation of large windfarms across the world. However, the effects of windfarm construction with different land use subtypes (i.e., wind turbine, waste slag, and construction production and living areas) on soil microfauna community stability and subsequent consequences for ecosystem functioning remains poorly understood. Here, we evaluated how these three land use types affect soil microfauna community stability relative to natural vegetation types (forest, shrubland, and grassland) in a mountain area. We found that all three windfarm land use subtypes reduced soil multinutrient cycling and microfauna community stability compared with natural ecosystems. However, among these natural ecosystems, reductions of soil multinutrient cycling were 66.36% and 79.10% lower in grassland than in forest and shrubland, respectively. Reductions of soil nematode community stability were 62.15% and 77.22% lower in grassland than in forest and shrubland; as well as soil protist community stability were 74.01% and 56.74%, respectively. Interspecific interactions and environmental filtering jointly drove variation in soil microfauna community stability. Soil microfauna community stability was significantly and positively linked to soil multinutrient cycling, which has the potential to initiate cascading ecological consequences. Our finding implies that grasslands may be more suitable than shrublands or forests for windfarm development based on the responses of soil microfauna communities.

Voltage support strength analysis and stability control strategy for grid‐forming wind power transmission system

AbstractIncreasing the short‐circuit ratio (SCR) of the power transmission system is crucial to ensuring voltage stability when the system has a high‐penetration of wind energy resources. This paper first analyses the weak nodes in the power system and quantifies the SCR requirements of these nodes under voltage safety constraints. Based on this analysis, a third‐order dynamic model of a virtual synchronous generator is established, and a design method for virtual transient reactance is proposed to meet the system's SCR requirements. Finally, a power system simulation with high‐penetration of wind energy is constructed, validating that under the proposed voltage stability support control strategy, grid‐forming wind turbines can meet the system's SCR requirements by optimizing the design of transient reactance, thereby improving the voltage stability of the system.

Research of the differential power control method for novel wind turbine

Research of the differential power control method for novel wind turbine

Evaluating Machine Learning and Deep Learning models for predicting Wind Turbine power output from environmental factors.

This study presents a comprehensive comparative analysis of Machine Learning (ML) and Deep Learning (DL) models for predicting Wind Turbine (WT) power output based on environmental variables such as temperature, humidity, wind speed, and wind direction. Along with Artificial Neural Network (ANN), Long Short-Term Memory (LSTM), Recurrent Neural Network (RNN), and Convolutional Neural Network (CNN), the following ML models were looked at: Linear Regression (LR), Support Vector Regressor (SVR), Random Forest (RF), Extra Trees (ET), Adaptive Boosting (AdaBoost), Categorical Boosting (CatBoost), Extreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LightGBM). Using a dataset of 40,000 observations, the models were assessed based on R-squared, Mean Absolute Error (MAE), and Root Mean Square Error (RMSE). ET achieved the highest performance among ML models, with an R-squared value of 0.7231 and a RMSE of 0.1512. Among DL models, ANN demonstrated the best performance, achieving an R-squared value of 0.7248 and a RMSE of 0.1516. The results show that DL models, especially ANN, did slightly better than the best ML models. This means that they are better at modeling non-linear dependencies in multivariate data. Preprocessing techniques, including feature scaling and parameter tuning, improved model performance by enhancing data consistency and optimizing hyperparameters. When compared to previous benchmarks, the performance of both ANN and ET demonstrates significant predictive accuracy gains in WT power output forecasting. This study's novelty lies in directly comparing a diverse range of ML and DL algorithms while highlighting the potential of advanced computational approaches for renewable energy optimization.

Numerical Study of the Effect of Corona Discharge on Upward Wake Flow in the Horizontal Axis Wind Turbine Farm

Numerical Study of the Effect of Corona Discharge on Upward Wake Flow in the Horizontal Axis Wind Turbine Farm

Generalized Synchronous Stabilization Control For Large-Scale Offshore Wind Power Plants During Severe AC Faults

Generalized Synchronous Stabilization Control For Large-Scale Offshore Wind Power Plants During Severe AC Faults

EMT Model Validation Throughout the Life Cycle of Wind Power Plants based on Type IV Turbines: Experiences and Recommendations

EMT Model Validation Throughout the Life Cycle of Wind Power Plants based on Type IV Turbines: Experiences and Recommendations

Wind Power Plant Dispatch for Power Grid Frequency Dynamics Improvement: A Surrogate Model-based Method

Wind Power Plant Dispatch for Power Grid Frequency Dynamics Improvement: A Surrogate Model-based Method

Leveraging LSTM-SMI and ARIMA architecture for robust wind power plant forecasting

Leveraging LSTM-SMI and ARIMA architecture for robust wind power plant forecasting

Enhancing the risk-oriented participation of wind power plants in day-ahead, balancing, and hydrogen markets with shared multi-energy storage systems

Enhancing the risk-oriented participation of wind power plants in day-ahead, balancing, and hydrogen markets with shared multi-energy storage systems

Grid disturbance resilience and stability improvement of grid-connected wind power plants

Grid disturbance resilience and stability improvement of grid-connected wind power plants

Resource selection and survival of plains sharp‐tailed grouse at a wind energy facility

AbstractAs the demand for wind energy development increases across much of the Great Plains region, there is a need to understand how this type of energy generation may impact wildlife. Due to their extensive range across areas with high wind resources, plains sharp‐tailed grouse (Tympanuchus phasianellus jamesiTympanuchus phasianellus jamesi) represent a valuable species to evaluate how selection and survival are associated with existing wind energy infrastructure. We used spatial and demographic data collected from radio‐marked female sharp‐tailed grouse to evaluate resource selection (nest, brood‐rearing, and breeding season) and survival (nest and female) near existing wind energy infrastructure during the April to August breeding season over a 3‐year period from 2020 to 2022 in northeastern South Dakota, USA. We monitored 119 GPS‐marked females captured at eight leks over the study period. We did not find evidence that females selected nest sites in relation to wind energy infrastructure but found that females with broods and females during the breeding season (April–August) avoided areas near high densities of wind turbines within 1.0 and 5.0 km of their home range, respectively. We found consistent selection for lower lengths of transmission lines across all life stages at the home range scale. We did not detect an effect of wind energy infrastructure on nest or female survival. Based on the results of our study, limiting the siting (the process of selecting the optimal location for a project and the associated features) of wind turbines within 5.0 km of sharp‐tailed grouse breeding habitat may represent an important siting tool to minimize avoidance of otherwise suitable habitats.

Structure failure and strength evaluation of honeycomb-based sandwich composites under variable hydro-thermal-mechanical load

The high-strength and lightweight sandwich structures have broad application prospect in aerospace, wind turbine generator, traffic and civil engineering. The sandwich structures usually service with severe environment and complicated mechanical load, structure failure and strength prediction are crucial issues. Under time-varying and optional position hydro-thermal–mechanical loading, this paper systematically analyzes strength failure, buckling and delamination of a sandwich beam with carbon fiber-reinforced polymer face sheet and aluminum honeycomb core. Effects of elastic boundary conditions, hydrothermal stress, configuration of honeycomb cell and thickness of face sheet on failure pattern and critical failure loading are evaluated. The theoretical deformation model is verified by performing a bending experiment of cantilever beam. For the honeycomb core with small re-entrant angle and shot horizontal cell wall, the sandwich cantilever beam occurs strength failure of face sheet and delamination is happened in simply supported beam. With increase of re-entrant angle and cell wall length, buckling of horizontal cell wall becomes the primary failure pattern of sandwich beam. With thickness increase of face sheet, the failure pattern switches from face sheet’s strength failure to delamination. The critical load for delamination decreases to a volley value and then increases with thickness of face sheet.

An improved decoupling method for dynamic modeling of wind turbine gearbox with planet gear journal bearings

An improved decoupling method for dynamic modeling of wind turbine gearbox with planet gear journal bearings