Wind Turbine Power Generation Research Articles

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.

Power transfer characteristics in fluctuation partition algorithm for wind speed and its application to wind power forecasting

Wind speed is the dominant meteorological factor affecting wind turbine power generation. Existing wind speed fluctuation division algorithms only focus on the wind speed changing process, but strong uncertainty exists in the wind speed–power conversion, which has been demonstrated as a multi-power transfer relationship under wind speed fluctuation. Therefore, a wind speed power transfer fluctuation partitioning (PTFP) algorithm is proposed, which fully considers the wind speed variation and power transfer characteristics to refine the modeling of wind power forecasting. The power transfer characteristics are investigated according to the wind speed–power conversion error of a wind farm. The measured wind speeds from three sources and fuzziness under different fluctuation trends are discussed based on the scatter distribution and probabilistic wind speed power curve. The model errors under different changing trends are quantitatively analyzed. Subsequently, the operation data of two wind farms in China are divided using the PTFP algorithm. The modeling error is reduced compared to that without considering the power transfer characteristics and the rationality of the proposed algorithm is demonstrated. The PTFP algorithm is also applied to wind power forecasting to verify its applicability. The results demonstrate that the proposed model can improve prediction accuracy on all time scales.

Hybrid Metaheuristic Model for Optimal Economic Load Dispatch in Renewable Hybrid Energy System

Hybrid generating systems in power networks have emerged as a result of the rapid growth of renewable infrastructure and widespread support for green energy. One of the most significant problems in designing and operating an electric power generation system is the efficient scheduling of all power generation facilities to meet the rising power demand. Economic load dispatch (ELD) is a generic procedure in the electrical power system, and the ELD in power system problems involves scheduling the power generating units to reduce cost and satisfy system constraints. Metaheuristic algorithms are gaining popularity for solving constrained ELD issues because of their larger global solution capacity, flexibility, and derivative-free construction. In this research, the ELD problem of integrated renewable resources is solved using a unique solution model based on hybrid optimization. Furthermore, this work considers multiobjectives such as total wind generation cost, total cost function of thermal units, and penalty cost function. The hybrid optimization model optimizes the power generation of thermal power plants within the maximum and minimum limitations. Additionally, the turbines are selected optimally by the hybrid optimization model to ensure the power generation of wind turbines based on the demands. The proposed hybrid optimization is a combination of particle swarm optimization (PSO) and cat swarm optimization (CSO), and the new algorithm is referred to as the particle oriented cat swarm optimization model (POCSO). Finally, the performance of the proposed work is compared to other conventional models. In particular, the cost function of POCSO is 6.25%, 6%, 11.7%, 36%, 27%, and 46.42% better than the cost function of whale optimization algorithm (WOA), elephant herd optimization (EHO), moth-flame optimization (MFO), dragonfly algorithm (DA), sealion optimization (SLnO), CSO, and PSO methods, respectively. Also, for IEEE-30 bus system, the best value of the proposed work is 7.46%, 5.41%, 16.30%, 14.88%, 17.60%, 13.86%, 15.21%, 17.49%, and 4.27% better than that of the PSO, CSO, SLnO, DA, MFO, EHO, WOA, multiagent glowworm swarm optimization (MAGSO), and Harris hawks optimization-based feed-forward neural network (HHO-FNN) methods, respectively.

Model Predictive Control Based Voltage Regulation Strategy Using Wind Farm as Black-Start Source

A coordinated voltage regulation method based on model predictive control (MPC) is proposed in this paper for utilizing wind farms (WF) as black-start (BS) source to start up a thermal generating unit. The reactive power regulation devices with different dynamic response characteristics including wind turbine generators (WTGs), energy storage system (ESS), and static var generator (SVG) are coordinated by the proposed MPC to handle disturbances caused by ancillary machine start during the BS process. The reactive power sharing between WTGs is optimized to maximize the dynamic reactive power reserve. The capabilities of ESS and SVG in providing sufficient dynamic reactive power against disturbances are also fully exploited, which helps accelerate voltage recovery for low voltage ride through to avoid the tripping incidents of WTGs. The impact of active power on bus voltage variation due to low <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">X</i> / <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R</i> ratio is also considered. The reactive power and active power of WTGs and ESS are coordinately controlled for handling voltage disturbance without harming frequency control. A WF with 33 WTGs rated 1.5 MW each is used in case studies to demonstrate the enhanced disturbance handling capability of the proposed voltage regulation strategy during BS progress.

Conceptual design and simulation of a stand-alone Wind/PEM fuel Cell/Hydrogen storage energy system for off-grid regions, a case study in Kuhin, Iran

In recent years the necessity of reducing environmental impacts and increasing flexibility in the energy sector has led to a growth in the penetration of renewable energy sources and the shift from centralized production to distributed production. This study aims to investigate the separation of the Kuhin, Qazvin region from the national grid network. In this case, two prominent scenarios are designed and proposed: the simultaneous power generation of wind turbine and proton-exchange membrane fuel cell (PEMFC) (first scenario) and fuel cell-hydrogen energy storage (second scenario). Algorithm optimization is employed to reach the best solution in the MATLAB environment to obtain the most efficient configuration of the hybrid systems. Accordingly, the proposed PEMFC is modeled and simulated using COMSOL software via the computational fluid dynamics methods. The results obtained from the optimization show that the desired PEMFC power plant suitable for the first scenario has a power capacity of 160 kW, generating electricity at $0.6452 per kWh and a present capital of $4,466,099. In the second scenario, the results show a power capacity of 168 kW at $0.7237 per kWh requiring a capital cost of $5,029,397.

A new Gaussian analytical wake model validated by wind tunnel experiment and LiDAR field measurements under different turbulent flow

Wind turbine wake has a great effect on wind power generation and fatigue loads of downstream wind turbines in wind farms. Analytical engineering wake model is adopted to evaluate the power loss caused by turbine wake for the optimization of wind farm layout. Gaussian distribution is often employed in wake model. Traditional Gaussian distributions, on the other hand, are unable to accurately describe wind speed especially near the wake boundary. Besides, the variable turbulent environment in wind farms with complex terrain poses a challenge to the turbine wake model. In this paper, the unfitness of the Gaussian wake model especially near the wake boundary is addressed and a Gaussian wake model with new turbulence intensity model is proposed. Compared with previous models, this new wake model has a more suitable wind speed distribution. The new wake model is then validated using wind tunnel measurements under different turbulence intensity. Results indicate that the new Gaussian wake model can more correctly predict the wind turbine wake than the previous Gaussian wake models. The new Gaussian wake model suitable shows a good application prospect in wind farm layout optimization which improves the power generation and reduces the power generation costs.

Application of 8D Method in the Study on Quality Problems of Wind Turbines

As a common quality management tool, 8D method is often used in the research of quality related problems of wind turbines. In the post-market service of wind power, it is often faced with low operating efficiency of old units, insufficient power generation and various quality problems. The causes of quality problems in wind turbines are usually multifaceted, involving components such as blades, impellers, and motors. In addition to the large components of the wind turbine itself, extreme weather and natural disasters such as snow, frost, wind and sand will also cause damage to the wind turbine, resulting in various quality problems. When the wind turbine quality problems, the unit's operating efficiency and power generation will be greatly reduced, the loss of power generation will be greatly increased. At this time, it is necessary to apply the 8D method to diagnose and sort out the problem solving process of wind turbines. In this paper, 8D method is applied to solve the problems of low operating efficiency and insufficient power generation of wind turbines, and finally the role and position of 8D method in solving the problem of wind turbine quality are clarified.

Research on 3D Modeling and Dynamic characteristics of wind turbine blade

In order to prevent blade resonance, reduce blade deflection, improve wind turbine power generation efficiency and wind energy utilization rate, the blade model of 3 kW wind turbine is established, and the natural frequency of wind turbine blade is analyzed. Based on the vibration theory, the effects of dynamic stiffening and rotational softening on the natural frequencies of blades are analyzed. Study the change rule of blade vibration mode under rotating speed of 0~300rpm, and analyze the influence of two effects on blade frequency. The results show that at low speed, the rotating softening effect of the blade is obvious, and the vibration frequency decreases; At high speed, the stress stiffening effect is more obvious, the vibration frequency increases, and the coupling vibration has greater destructiveness.

Search group algorithm for optimal allocation of battery energy storage with renewable sources in an unbalanced distribution system

ABSTRACT In recent decades, numerous distributed energy resources (DER) have been linked to distribution networks. Among these wind turbine generators (WTG) are popular for their uncertain power fluctuations. Using battery-based energy storage devices (BESS) as a solution to these unwanted fluctuations might be a realistic option. However, the integration of BESS with WTG on a wider scale requires additional attention to the size, position, and charge-discharge schedules. This article discusses these perspectives in addition to the location and power penetration levels of WTGs. In addition, a new charge-discharge control model is created for deciding when batteries should be charged or discharged at each hour. A multi-objective optimization problem is created with the goal of optimizing the economic, environmental, and technological goals of the unbalanced distribution network (UDN). The optimization task is solved using a Search Group Algorithm (SGA). The planning model leverages the average demand of the feeder at a particular hour of the day as the decision criteria for charging or discharging the battery. The charging of the BESS devices is totally done by the WTG power output. Furthermore, the conservative and free-running discharge approaches of the battery are studied. The proposed approach is tested using IEEE 37 bus UDN and the results demonstrate improvement in distribution system performance by coordinating BESS dispatches appropriately. The active and reactive power loss reduction for the three-phase system were 30.4% and 30.8% for the free-run mode, respectively, while the same for the conservative mode was 40.3% and 37.4%.

A feasibility and dynamic performance analysis of hydromechanical hybrid power transmission technology for wind turbines

Abstract. A multi-body dynamical model of a wind turbine power generation system (WTPGS) based on hydromechanical hybrid power transmission (HMHPT) technology is developed and simulated to overcome the individual drawbacks of the gear train and hydrostatic power transmission (HPT) system. The HMHPT is a hybrid concept of a single-stage planetary gear train (SSPGT) and a typical HPT. The input shaft of the SSPGT is coupled with the turbine rotor, whereas the output shaft of the SSPGT is coupled with the shaft of a hydraulic pump. The hydraulic pump supplies flow to the hydro-motor, and its shaft is coupled with the generator. An existing turbine blade model of 750 kW based wind turbine is used for further development and analysis of the HMHPT. The simulation responses indicate that the power generation and the control potential both have been improved using the HMHPT in a wind turbine. Moreover, the influence on the motor power generation due to variations of pump and motor leakages is addressed. Additionally, it is found that if the order of the SSPGT and the HPT are swapped in the proposed HMHPT, then the settling time, maximum overshoot, and rise time of the system responses are increased. As a result, the controllability of the system is decreased.