Wind Turbine Power Generation Research Articles

Design, realization and testing of a synthetic inertia controller for wind turbine power generators

The progressive inclusion of Renewable Energy Sources (RES) in power grids is causing a reduction of the overall system inertia, leading to a reduction of the system resiliency. A possible solution is equipping RES with synthetic inertia controllers so that additional power can be provided in case of severe frequency transients. This work reports the results of a research activity that aimed at improving the maturity of synthetic inertia controllers for wind turbine generators through the design and implementation of a controller prototype based on an advanced version of a synthetic inertia controller. Hardware-in-the-loop simulations are performed to validate the correct behavior of the prototype.

Data-driven fault detection framework for offshore wind-hydrogen systems

Offshore wind-hydrogen systems operate in harsh marine environments for extended periods, posing risks of low accessibility and high failure rates. This paper proposes a data-driven fault detection framework for offshore wind-hydrogen systems, aiming to promptly detect potential faults based on the system's physical parameters. The research establishes a comprehensive model based on the operating principles of offshore wind-hydrogen systems. The study uses fault injection techniques to simulate common partial faults in offshore wind-hydrogen systems and collects relevant physical parameters (such as wind turbine power generation, energy storage battery charging/discharging current, and electrolyzer hydrogen production rate). A fault detection model was constructed using a combination of Convolutional Neural Networks (CNN) and Bidirectional Long Short-Term Memory (BiLSTM) to enable precise detection of potential faults in the system. The established model provides theoretical support for designing, optimizing, and operating offshore wind-hydrogen systems, reducing reliance on actual experiments and lowering project risks and costs. Introducing artificial faults can evaluate the system's performance in different fault scenarios. This paper proposes a CNN-BiLSTM fault detection method that achieves automatic feature extraction and high-precision classification of time-series fault data, confirming the practicality and feasibility of offshore wind-hydrogen systems. This investigation realizes the integration of the offshore wind-hydrogen system model and deep learning, as well as a fully automated learning process from data to fault detection. By calculating and comparing results, it is verified that the framework has a high accuracy of 96.9%. This innovative research offers new methods and perspectives for enhancing the reliability and efficiency of fault detection in offshore wind-hydrogen systems.

Optimal sizing of standalone hybrid renewable energy system based on reliability indicator: A case study

The economical electrification of rural settlements extensively utilizes renewable energy sources. In recent years, the deployment of solar photovoltaic, wind turbine, and biomass gasifier-based power generation technologies for electricity accessibility in remote regions has received greater prominence. This paper presents an optimal component design of a hybrid solar PV-wind and battery system along with a biomass gasifier to provide continuous electricity for rural communities of Uttarakhand, India. In this study, a recently developed population-based giza pyramids construction (GPC) methodology is applied to determine the optimal component sizing. The findings of the GPC method are contrasted with the particle swarm optimization (PSO) methodology to assess the effectiveness of the proposed method. The annualized system cost and levelized cost of energy of the hybrid renewable energy system have been minimized subject to technical reliability indicators such as loss of power supply probability and renewable fraction.Moreover, a systematic exploration of the system has been conducted to assess its optimal feasibility across different values of power loss probability ranging from (0% to 4%). The simulation indicates that the proposed method adeptly identifies globally optimal values, leading to a $177 decrease in annual cost and 8% reduction in average simulation time compared to PSO. The research reveals that the balanced point (relative attainment), where system reliability and cost-effectiveness intersect optimally, is achieved at a 3% power loss level, resulting in the most significant cost reduction of $199 compared to other probability levels.

Growth in turbine size and technological development of modern commercial large scale wind turbines in Türkiye

In this study, in addition to the recent advances and tendencies in wind turbine technology around the world, the progress of commercial wind turbine technology installed in Turkey was also thoroughly examined. In this respect, several metrics for installed wind turbines including number of turbines, installed power capacity (MW), mean rated capacity (MW), mean rotor diameter (m), mean specific power capacity (W/m2), and mean hub height (m) have been obtained between the years of 2011 and 2019. According to the obtained results, the mean rated capacity of Turkey’s annual installed wind turbines advanced from 1.86 MW in 2011 to 3.52 MW in 2019. However, the mean specific power of yearly installed wind turbines declined from 423.7 W/m2 to 314.1 W/m2. Outcomes revealed that the growth in the size and the reduction in the specific power have contributed to the tendency of higher power outputs, and wind turbine capacity factor and power generation capacity have been on the rise in Turkey. In time, wind turbines with greater rotor diameters and hubs started to show up more observable on land. For that purpose, the proposed solution to regulate turbine visibility during site selection is the potential visibility model (PVM), which is meant to be used as an auxiliary variable.

Optimal design of a renewable hydrogen production system by coordinating multiple PV arrays and multiple electrolyzers

In the design of a renewable hydrogen production system (RHPS), it is of great significance to coordinate the power generation and electrolyzers for efficient and stable hydrogen production. To investigate the coordination and complementary characteristics of multiple PV arrays and multiple electrolyzers in the optimal design of RHPS, a coordinated design approach based on mathematical optimization for the optimal RHPS with multiple PV arrays and multiple electrolyzers was proposed, which features comprehensive considerations of different characteristics of power generation and economy of different types of PV arrays, and the dynamic operating characteristics and economy of various types and capacity of electrolyzers to adapt to fluctuating hydrogen demand. The optimal configuration of the RHPS with minimal total cost can be obtained by combining multiple PV arrays with multiple electrolyzers. Furthermore, the impacts of power generation on the economy of the system are systematically explored, and electrolyzer types are optimally deployed. The influence of different PV array types on the economics of the system is analyzed. An example of the RHPS was employed to demonstrate the feasibility and effectiveness of the proposed method. Results show that for the RHPS, the selection of the type of electrolyzers is mainly affected by the characteristics of power generation. In the system with only PV arrays for power supply, the total cost of the system with proton exchange membrane electrolyzers (PEMEL) is 11.2% lower than that of the system with alkaline electrolyzers (AEL). In the system with simple wind turbines for power generation, the total cost of the system with AEL is lower than that of the system with PEMEL. The selections of type and capacity of the electrolyzers in the system are affected by the minimal power limit of the AEL. The total cost of the system can be effectively reduced by coordinating the power generation and electrolyzers and combining AEL and PEMEL in the system. The selection of PV types is related to the unit cost and the seasonal changes in the capacity factor of different PV arrays, which is also influenced by the hydrogen demand and the curtailment rate. When the variations of power generation align with hydrogen demands, the total cost can be reduced by lowering seasonal energy storage capacity.

Fluid–Structure Interaction Simulations of Wind Turbine Blades with Pointed Tips

The aerodynamic shapes of the blades are of great importance in wind turbine design to achieve better overall turbine performance. Fluid–structure interaction (FSI) analyses are normally carried out to take into consideration the effects due to the loads between the air flow and the turbine structures. A structural integrity check can then be performed, and the structural/material design can be optimized accordingly. In this study, three different tip shapes are investigated based on the original blade of the test wind turbine (Phase VI) from the National Renewable Energy Laboratory (NREL). A one-way coupled simulation of FSI is conducted, and results with a focus on stresses and deformations along the span of the blade are investigated. The results show that tip modifications of the blade have the potential to effectively increase the power generation of wind turbines while ensuring adequate structural strength. Furthermore, instead of using more complicated but computationally expensive techniques, this study demonstrates an effective approach to making quality observations of this highly nonlinear phenomenon for wind turbine blade design.

Multi‐dimensional evaluation and diagnostic methods for wind turbine power generation performance based on different influencing factors

AbstractThe power generation performance of wind turbines has consistently been a paramount concern for wind power operators, maintainers, and manufacturers, as it directly determines the profitability of wind farms. However, due to the combined influence of complex environmental conditions within wind farms and inherent deficiencies in wind turbine design, significant variations in power generation performance persist among turbines of the same model. This discrepancy can be attributed to two crucial factors: site conditions and operational efficiency. To achieve more precise and systematic diagnostic work on the power generation performance of wind turbines, this paper focuses on three factors: air density, turbulence intensity, and yaw adaptability. Based on this, three evaluation and diagnosis methods are proposed, including a conversion method for air density based on two‐dimensional interpolation, a turbulence correction method based on the zero‐turbulence curve, and a yaw adaptability diagnosis method based on the convergence degree. Finally, the effectiveness of these proposed methods is verified through the analysis of actual wind field data.

Development and assessment of a newly developed renewable energy-based hybrid system with liquid hydrogen storage for sustainable development

One of the most effective ways to address the environmental issues triggered by fossil fuels is through the use of renewable energy. In this study, clean and sustainable power and hydrogen generation with three renewable energy sources, a wind-solar-geothermal assisted hybrid plant is planned, investigated, and analyzed. An exhaustive thermodynamic performance analysis is executed to specify the system efficiency by the way energy and exergy efficiency methods. Moreover, this developed cycle comprises a solar photovoltaic (PV) unit, a triple flash geothermal power plant, wind turbines for power generation, and a hydrogen generation and liquefaction unit. Additionally, a case study for Isparta is conducted using actual data on wind speed and solar radiation. A parametric analysis, on the other hand, is conducted to figure out the consequences of the examined cycle's performance. In light of the examination outcomes, the developed plant's power rate is figured as 9942 kW. The hydrogen generation rate and sustainability index are also figured out as 0.02143 kg/s and 2.003, respectively. Finally, the proposed hybrid cycle encloses an energetic and exergetic performance of 32.755 % and 50.06 %, respectively.

Efficiency-Driven Iterative Model for Underwater Compressed Air Energy Storage (UW-CAES)

The competitiveness of large-scale offshore wind parks is influenced by the intermittent power generation of wind turbines, which impacts network service costs such as reserve requirements, capacity credit, and system inertia. Buffer power plants smooth the peaks in power generation, distribute electric power when the wind is absent or insufficient, and improve the capacity factor of wind parks and their profitability. By substituting the variable pressure storage with an underwater variable volume air reservoir and reducing the wastage of compression heat using liquid Thermal Energy Storage (TES), which eliminates the combustor, the plant design allows overcoming the most common drawbacks of CAES plants. Underwater Compressed Air Energy Storage (UW-CAES) plants are investigated with a thermodynamic model to drive the power plant design toward efficiency maximization. Functional maps, constrained on the plant pressure ratio and the number of compressor/turbine phases with inter-refrigerated/inter-heating phases, are drawn by solving the model iteratively for the heat exchangers’ effectiveness to meet the target turbine discharge temperature, selected in advance to avoid unfeasible mathematical solutions.

Performance improvement of horizontal axis wind turbines using curved gurney flap: 3D numerical investigation

The impact of curved Gurney flaps on the performance of a 660 kW V47 horizontal axis wind turbine (HAWT) is analyzed in this study. The trailing edge of the turbine blades is passively modified by curved flaps. The continuity and momentum equations are solved using a Reynolds-averaged Navier-Stokes solver and Shear-Stress-Transport turbulent model. The effect of the direction and radius of curved flaps on the aerodynamic performance of the wind turbine (torque and power generation, flow separation, and thrust loads) is examined. The study examines how the curved flap’s performance on HAWT is affected by the blade pitch angle ([Formula: see text]) and wind speed ([Formula: see text]). According to the results, curve flaps have a positive impact on the output torque at lower pitch angles ([Formula: see text]). The average torque increase for [Formula: see text] and [Formula: see text] is 3.7% for curve flaps, while it is only 2.9% for flat flaps. This conclusion is not valid for higher pitch angles ([Formula: see text]) where the flap disrupts the aerodynamic performance. Further, the use of curve flaps with the radius of [Formula: see text] (inward-type) can improve the torque produced (up to [Formula: see text]) compared to other curved flaps and even flat flaps, especially around the nominal operating point. This conclusion is valid for [Formula: see text], while for higher pitch angles, the flat Gurney flap has better performance generally.

A privacy-preserving framework integrating federated learning and transfer learning for wind power forecasting

Data-driven approaches show significant potential in accurately forecasting the power generation of wind turbines. However, it suffers from a lack of training data in various scenarios. Transfer learning has been employed as a solution to address the issue of limited data by leveraging data from other turbines. However, the conventional centralized training approach raises concerns about data privacy risks. In this study, we propose a privacy-preserving framework that incorporates federated learning and transfer learning for wind power forecasting. Firstly, we design a hybrid model with a series architecture as the backbone model to improve forecasting accuracy. Secondly, the proposed two-stage framework consists of federated learning-based pre-training and personalized fine-tuning. The federated learning stage pre-trains a knowledge-sharing model without disclosing raw data from source domains. Based on the pre-trained global model, personalized fine-tuning is applied to establish a customized model for the target turbine. Experimental results demonstrate that the proposed hybrid model achieves better forecasting accuracy. Additionally, the two-stage framework not only addresses insufficient data while considering privacy preservation but also enhances personalized adaptation of shared knowledge for the target turbine. Compared to local training and traditional federated learning, the proposed framework demonstrates obvious accuracy improvements, reaching up to 43.32 % and 27.94 %, respectively.

Numerical and experimental investigation of geometric parameters influence on power generation of INVELOX wind turbine

Wind energy is a key contributor to current transitional trends toward green energy resources. Wind turbines, therefore, act as the main of the wind energy power generation hierarchy, which converts the kinetic energy of wind into electric power. However, low wind velocity is a limiting factor that significantly diminishes the power generation capacity of wind turbines. Alternatively, ducted wind turbines provide a sustainable solution for application in areas with low wind velocity. In the present work, a numerical investigation has been performed to investigate the variation in the power generation capacity of INVELOX with varying geometric parameters. Initially, a numerical investigation of the original INVELOX model was performed to validate the numerical framework and establish the obtained results as a reference. Afterwards, the shape of the funnel, fins/supporting structure, and venturi parameters were varied to analyze their influence on the maximum velocity achieved at the throat. Subsequently, a novel semi-dome concept is added to the model based on numerical results. The results showed that the maximum velocity ratio has been improved from 2.125 to 3.13. Furthermore, power generation capacity was also increased by 3.2 times compared with the original INVELOX design by applying optimized geometric parameters and the semi-dome concept.

Wind resource of Ilorin City for vortex-induced wind turbine power generation and off-grid electrification

This study examined the appraisal of wind resources of Ilorin, Nigeria for vortex-induced wind turbine power generation and off-grid electrification. The technical potential of Modern-Era Retrospective Analysis for Research and Application, version 2 (MERRA-2) was employed as a tool to generate an estimated wind resource of Ilorin city, using five different hub-heights (10, 30, 50, 70, and 90 m). A statistical analysis of wind characteristics for 21 years from 2001 to 2021 was carried out using Weibull distribution function. The daytime and night-time wind characteristics were studied to determine prospective and investment hub-height(s). It was observed that the study area is a low wind region with a minimum and maximum mean wind speed of 2.89 m/s at 10 m and 7.68 m/s at 90 m, respectively. Wind turbines with cut-in wind speed of 2, 2.5, and 3 m have operational chances of 98%, 95% and 88%, respectively. Wind power density at 10, 30, and 50 m elevations was classified as poor while at 70 and 90 m elevations, was regarded as marginal and fair, respectively.

Increasing ecological sustainability using the combinations of technologies to produce power

The study attempts to analyze the impact of hybrid energy systems on environmental sustainability in the context of the circular economy network. The hybrid energy sources follow the principle of circular economy, which aims to reduce raw material use and waste which is very fruitful in promoting environmental sustainability. However, the study uses quarterly data from Pakistan from 2011Q1 to 2020Q4 to accomplish the proposed study objective. The study constructs 5 hybrid systems of energy to analyze the impact and applies Auto Regressive Distributive Lag Model (ARDL) to estimate the results. It is found that electricity generation through solar photovoltaic (SPV), wind turbine (WND), hydroelectricity (HYDE) and nuclear power generation (NPG) play a positive role in increasing environment sustainability. The results explain that SPV, NPG, and HYDE contribute 2.29%, 2.04%, and 0.42% to environmental sustainability, respectively. Hybrid systems of energy (Hybrid 1,2,3,4 and 5) positively impact ecological sustainability, but the intensity of each hybrid system in sustaining the environment is different. Among five hybrid systems, the hybrid-5 (SPV-WND-HYDE-NPG) energy system is more environmentally friendly and has the highest contribution towards environmental sustainability. The study suggests incorporating hybrid energy systems provides the means of transition toward a circular economy, ultimately promoting ecological sustainability. The study proposes to the officials of the Pakistani government and policymakers to initiate effective policies to encourage hybrid energy systems because the significance of hybrid systems ensures a low carbon economy and makes a path towards sustainable economic development.

Optimizing Production of Kwh Legon PLTD PLTD Using Wind Turbine Generator Model B. Ventus 250 KW(Case Study on Karimunjawa Island)

Electricity supply must be able to reach all users, including those on small islands in Indonesia, one of which is Karimunjawa Island. Starting in 2016 Karimunjawa Island is supplied by 2 units of Legon Bajak Diesel Power Plant (PLTD) with a capacity of 2x2.2 MW. Based on the peak electricity demand on Karimunjawa Island, each month from January 2017 to December 2018 increased by about 3.9% with an hourly peak load of 1,146 kWh. The average consumption of PLTD Legon Bajak every month in 2018 is 194,877 liters / month. The higher the kWh production of a PLTD, the higher the operational costs for fuel. In addition to the high price of fuel, combustion results from PLTD operations also cause air pollution. Based on these problems in this study will analyze the optimization of the kWh Legon Bajak PLTD using a wind turbine generator model b.ventus 250 kW of 3 units with an assumption of 50% of the peak load, because on Karimunjawa Island has a high enough wind energy potential. The average wind speed from 2004 to 2015 in several areas of Karimunjawa Island was 5.7 m / s. In this study will calculate technical analysis and economic analysis. In the technical analysis the wind turbine generator power output is calculated using the weibull method based on wind speed and power curve b.ventus 250 kW while the economic analysis calculates the cost of fuel savings that can be achieved after optimization calculations. Calculation results show wind turbine power output of 990,981 kWh / year. Based on the specific value of fuel consumption (SFC) of 0.432 liters / kWh fuel consumption during 2017 amounted to 1,605,453.9 liters / year while for 2018 it was 2,338,528.1 liters / year. From the 3 wind turbine electricity supply of 2,972,943 kWh / year, it will reduce fuel consumption by 1,284,311.4 liters / year with a saving cost of Rp.15,989,291,338.00. The amount of optimization of the kWh PLTD after the supply of 3 units of b.ventus 250 kW wind turbine model in 2017 was 80% while in 2018 it was 54.9%.

A Hybrid Solar Photovoltaic and Wind Turbine Power Generation for Stand-Alone System with IoT-Based Monitoring and MPPT Control

The goal of this effort is to monitor and manage a hybrid stand-alone photovoltaic (PV) and wind energy system (WES) using the Internet of Things (IoT). The suggested hybrid system uses Incremental Conductance (INC) Maximum Power Point Tracking (MPPT) and Perturb and Observe (P&O)-based Sliding Mode Control (SMC) approaches. The cost-effective Arduino Uno board in Matlab is used to analyze the P&O and INC MPPT methods. Energy from renewable sources is stored in a battery, and the output voltage of each source is monitored using the blynk app and GSM 800c module. Simulation results validate the controller’s operation, and the performance of both MPPT algorithms is compared. Experimental outcomes demonstrate the effectiveness of the IoT-based controller and its characteristic functionalities. Overall, this proposed hybrid PV and WES configuration offers advantages such as reduced human resources, cost-effectiveness, time savings, enhanced reliability, and improved data collection and control capabilities.

Demand Side Control for Energy Saving in Renewable Energy Resources Using Deep Learning Optimization

Convolutional neural networks a type of deep learning technology, are used for forecasting future power usage. The mean absolute error, mean square error, root mean square error, and mean constant percentage error are used to evaluate the performance of the models. These metrics are used to rank the models. Among the models tested, the CNN stack demonstrates the highest precision in estimating energy consumption and solar power output, with a mean absolute error of 0.015% points, a root mean square error of 0.23% points, and an average absolute percentage deviation of 1.71% points. However, for wind turbine (WT) energy generation, the recurrent neural network proves for most accurate model, achieving 0.070 as the median absolute percentage error is 2.65, Both the root of the mean square error and the average absolute error are 0.38. The training and validation data utilized in this study comprise the International Renewable Energy Agency’s data on solar power production, WT power generation, and the “ensue” dataset, which includes hourly power consumption information from the Pennsylvania, New Jersey, and Maryland interconnection.

Impact of Weibull Wind Speed Distribution on the Costs of Producing Power‐to‐X Products

AbstractA calculation method based on Weibull parameters and cost functions is presented to optimize the levelized cost of electricity and the costs for subsequent power‐to‐X (PTX) production from wind power. PTX products considered are methanol, ammonia, synthetic gasoline, and synthetic diesel as well as the hydrogenation of liquid organic hydrogen carrier. The onshore production sites Haru‐Oni in Chile, Tarfaya in Morocco, and Dornum in Germany were used for illustrative purposes. The model calculations show that the rated power of the installed wind turbines for power generation is generally optimal between 1 and 3 MWel in contrast to the current trend towards ever higher rated power. Also, it has been found that the recoverable wind energy increases with rising Weibull shape factor k if the wind speed at which the rated power is reached is smaller than the Weibull scaling factor A.

Novel Deep Learning Model for Predicting Wind Velocity and Power Estimation in Advanced INVELOX Wind Turbines

Wind energy is a renewable energy source that has grown rapidly in recent decades. This energy is converted into electricity using advanced INVELOX wind turbines. However, the wind velocity is critical, and predicting this velocity in real-time is challenging. As a result, a deep learning (DL) model has been developed to predict the velocity in advanced wind turbines using a novel enhanced Long Short-Term Memory (LSTM) model. The LSTM enhancement is executed by employing the Black Widow optimization with Mayfly optimization in the Python platform as application software. The dataset has been prepared using Ansys Fluent fluid flow analysis. In addition to that, the wind turbine power generation was computed analytically. A subsonic wind tunnel test is also performed by employing a 3-Dimensional printed physical model to validate the simulation dataset for this innovative design. The proposed MFBW-LSTM model (Enhanced LSTM with BWO and MFO) predicts efficiently, with an accuracy of 95.34%. Furthermore, the performance of the proposed model is compared to LSTM, BW-LSTM, and MF-LSTM. Accuracy, MAE, MAPE, MSE, and RMSE are among the performance criteria the proposed DL model achieves efficiently. As a result, the proposed DL model is best suited for velocity prediction of an Advanced INVELOX wind turbine in various cross sections with high accuracy.

ADRC‐based optimized control system for wind turbine power generator

AbstractAs a renewable and clean energy source, wind energy can be converted into electricity by wind turbines. Currently, it is challenging to suppress the edgewise vibration of the wind turbine blades to improve the efficiency and service life of wind turbines. In this paper, active disturbance rejection control (ADRC)is applied to vibration suppression at the blade edge of wind turbines. First, the proposed system is mathematically modeled and analyzed in the frequency domain. Second, the ADRC is designed for the proposed system. Then, time domain analysis, frequency domain analysis, and uncertainty analysis of the closed‐loop system were carried out. It has been shown that the proposed controller has 12.2% less overshoot and 55.8% less setting time when subjected to perturbations than the PID controller. And the proposed controller has 33.9% less overshoot and 55.9% less setting time when subjected to perturbations than the SMC. Furthermore, the resonance frequency range of the proposed controller has been effectively reduced, and the peak resonance has been reduced by 41.7%. This reflects that the ADRC has a significant vibration suppression effect on blade edgewise vibrations compared with PID controllers and SMC.