Turbine Power Output Research Articles

Investigation on the Effects of Volute Geometric Parameters on the Performance of Pico Francis Turbines Used in Water Pipes

The development of new hydro turbines or the optimization of traditional hydro turbines is one of the research directions that scholars have been interested in to broaden the source of hydropower and improve energy efficiency. In our previous work, a Francis turbine was used in the water supply systems of high-rise buildings to properly control water pressure inside the pipeline and recover excess water energy. In this paper, an optimization study of the volute for the sectional shape, design method, inlet parameters, outlet width, and tongue was conducted using numerical simulations to improve the hydro turbine performance. The simulated results show that a circular section could better enhance the flow velocity inside the volute and make the flow velocity distribution more uniform at the volute outlet, and the equal mean velocity method is a more suitable volute design method in this special condition. The results of range analysis show that the water head reduction increases significantly with the increase in the inlet height, and both inlet height and outlet width have a significant effect on the efficiency. In addition, in the tongue optimization, a larger tilt angle corresponds to smaller output power and water head reduction; a larger stretch length in the limited angle range corresponds to larger output power and lower efficiency. Ultimately, the output power of the hydro turbine was increased by 18.65% and the efficiency by 2.1% through the volute optimization.

Impact of incoming turbulence intensity and turbine spacing on output power density: A study with two 5MW offshore wind turbines

Turbulence intensity in offshore environments has significant effects on the performance of offshore wind turbines. To maximize output power of offshore wind turbines within a limited space, i.e., achieving maximum output power density, it is essential to investigate the influences of incoming turbulence intensity and turbine spacing on output power density. The Large Eddy Simulation coupled Actuator Line Method (LES-ALM) is used in this study to simulate two utility-scale NREL-5 MW wind turbines in different turbulent environments and turbine spacings. The optimal incoming turbulence intensity and turbine spacing are identified using an active learning method. The determined optimal parameters act as a benchmark for the impact analysis. The findings indicate that the increase of turbine spacing initially results in a considerable rise in the output power density of the two NREL-5 MW wind turbines, followed by a slight downward trend across various turbulent environments. The increase of the incoming turbulence leads to a steady rise of the output power density when the turbine spacing is less than 5.1D (D is the rotor diameter), while a rise followed by a decrease in the output power density is observed for the turbine spacing exceeding 5.1D. Notably, when the turbine spacing is 5.9D and the reference value of turbulence intensity is 15.2%, the output power density reaches its global maximum.

A novel strategy to enhance power management in AC/DC hybrid microgrid using virtual synchronous generator based interlinking converters integrated with energy storage system

Hybrid microgrids (HMGs) have a flexible structure, higher reliability, and both AC and DC MG merits. However, in this type of MGs, there are challenges such as accurate power sharing, the voltage control of the DC link, the AC side voltage and frequency stability, the negative impact of telecommunication delay, and so on. Therefore, a power management scheme is presented here that can perform proper and precise power sharing in a hybrid AC/DC MG. This HMG consists of one energy storage system (ESS) along with two interlinking converters (ILC) based on a virtual synchronous generator (VSG). Besides, the physical inertia of a MG is less than that of a power grid, hence, changing the output power of wind turbines and photovoltaic (PV) arrays might make system unstable. To overcome this challenge, virtual inertia is used. As the virtual inertia can be embedded with the VSG method, in this scheme, the VSG strategy is implemented to enhance the system dynamic behavior by imitating the synchronous generator kinetic inertia. The VSG-based ILC1 converter is used to establish interlinking and energy exchange between AC and DC sub-grids and share power. Also, other interlink devices including a bidirectional half-wave DC/DC converter integrated with the ILC2 are considered, which aim to control the voltage of the DC link and exchange part of the power between the AC and DC sub-grids. An ESS is placed between the bidirectional DC/DC converters and the DC link of the ILC2. Such a structure is referred to as a two stage interlinking converter with an ESS (TSILC-ESS). The power management system employed in AC/DC HMG according to the ILC controller is utilized to form the DCMG along with controlling the TSILC-ESS, providing additional and auxiliary services to the power grid. To assess the introduced scheme of both AC and DCMG to investigate VSG-based ILC1 and ILC2 in grid-connected and islanded operational mods, various resource and load profiles are assumed. The simulation study is performed in MATLAB/ Simulink environment to confirm the proposed method.

Urban energy transition in renewable energy management considering policy and law challenges and opportunities in the pursuit of clean and sustainable urban environments

This research presents a novel approach to enhance sustainable energy development based on wind turbine (WT) output power prediction through the development of a hybrid deep learning model, considering policy and law challenges associated with the problem. The proposed model integrates a Self-Supervised Generative Adversarial Network (GAN) with Attention-based Recurrent Neural Networks (RNNs). The Self-Supervised GAN contributes to improved feature representation learning, while Attention-based RNNs capture temporal dependencies for accurate WT output power forecasting considering political challenges. Additionally, a Modified Adaptive Flower Pollination Algorithm (AFPA) is introduced to optimize the selection of the GAN model structure in view of the legal and political objectives. This adaptive approach refines the architecture of the GAN, enhancing its ability to generate realistic and informative features for the subsequent Attention-based RNNs. The efficacy of the hybrid model is demonstrated through real-world applications in an urban area, emphasizing sensitivity and stability analyses. A thorough investigation into the sensitivity of key model parameters, including the number of hidden units in the Attention-based RNNs and GAN structures, is conducted to fine-tune the model's performance. Stability analysis is employed to assess the robustness of the proposed method under dynamic conditions, ensuring its reliability in practical scenarios. Results indicate that the hybrid model, guided by the Modified AFPA, outperforms existing approaches in WT output power prediction. The proposed methodology showcases the significance of combining state-of-the-art deep learning techniques with adaptive optimization algorithms for accurate and stable predictions in urban energy management applications.

Experimental Study on Aerodynamic Characteristics of Downwind Bionic Tower Wind Turbine.

The vibrissae of harbor seals exhibit a distinct three-dimensional structure compared to circular cylinders, resulting in a wave-shaped configuration that effectively reduces drag and suppresses vortex shedding in the wake. However, this unique cylinder design has not yet been applied to wind power technologies. Therefore, this study applies this concept to the design of downwind wind turbines and employs wind tunnel testing to compare the wake flow characteristics of a single-cylinder model while also investigating the output power and wake performance of the model wind turbine. Herein, we demonstrate that in the single-cylinder test, the bionic case shows reduced turbulence intensity in its wake compared to that observed with the circular cylinder case. The difference in the energy distribution in the frequency domain behind the cylinder was mainly manifested in the near-wake region. Moreover, our findings indicate that differences in power coefficient are predominantly noticeable with high tip speed ratios. Furthermore, as output power increases, this bionic cylindrical structure induces greater velocity deficit and higher turbulence intensity behind the rotor. These results provide valuable insights for optimizing aerodynamic designs of wind turbines towards achieving enhanced efficiency for converting wind energy.

Lattice Boltzmann simulation for wake interactions of aligned wind turbines using actuator line model with turbine control

A wind turbine wake causes a decrease in wind speed and an increase in turbulence intensity. The wind turbine wake interaction is essential for predicting the power output of a wind farm consisting of many wind turbines. This research proposes a CFD method able to reproduce wake interactions and power outputs of multiple wind turbines with high speed and accuracy. Large eddy simulations with the lattice Boltzmann method are used for fluid calculations, specifically for large-scale CFD simulations. The wind turbines are represented using an actuator line model. Optimal power generation efficiency is achieved by controlling the rotor speed and blade pitch angle. Large-scale simulations of eight aligned wind turbines are conducted using 1.75 billion grid points and 40 GPUs. We compare two cases with and without control to investigate the effect of turbine control on wake and power output. Both the instantaneous and mean streamwise velocities confirm that the turbine control reduces the wake velocity deficit of the downwind wind turbine. High-speed inflow of wind to the downstream turbines augments their power output. With implementation of turbine control, the power outputs of the downstream turbines agree well with the observation data obtained in an earlier study. The results demonstrate the importance of controlling the rotational speed and pitch angle for actuator line simulations.

Validating low- and high-fidelity simulations of a yawed 8 MW wind turbine against measurements

This paper presents a comparison study of the aerodynamic behavior of an 8 MW wind turbine operating under yawed conditions using field measurements, Blade Element Momentum (BEM) theory and Computational Fluid Dynamics (CFD). Turbulent inflow wind field measurements are used to generate an IEC-compliant turbulence box upstream of the wind turbine that is used within the BEM tool MoWiT and the CFD tool chain by IWES in OpenFOAM. Both simulation approaches take into account blade flexibility using modal reduced, anisotropic beam elements in BEM and Geometrically Exact Beam Therory (GEBT) in CFD. While the focus is being set towards the CFD approach, turbine power output, blade root bending moments and blade tip deflections are analyzed. It is shown that both modeling approaches react differently towards the generated wind field.

Estimating the performance of wind turbines misaligned with respect to the wind direction using a simplified CFD based approach

Wind direction in atmospheric boundary layer changes constantly. As such, the power extracted from the wind turbines in a wind farm is subjected to change. This change in wind direction impacts the wake pattern which in turn affects the power output of the downstream wind turbines. Empirical wind farm models are useful in such cases to assess the overall performance of the wind farms. However, the results provided by these models can be made more useful if fluid-structure interaction (FSI) effects are also considered. In the present work, we begin by looking at the potential flow model and the issues associated with it and used a modified model with constant vorticity to analyze the wind power available to the downstream wind turbines in a wind farm misaligned with respect to wind direction for irrotational flow. A simplified FSI based approach is used to carry out the simulations without the need of modelling the complicated wind turbine geometry.

Time-Series Based Surrogate Model For Wind Farm Performance Prediction

Aeroelastic codes are state-of-the-art simulation tools in both industry and academia for the modelling of wind turbine loads and power output. Although these codes are widely used for the analysis of individual turbines, they are in general computationally too expensive for the calculation of all turbines within a wind farm. Engineering models that are computationally cheaper but also provide a lower fidelity are therefore typically used for wind farm power performance predictions. In this paper, an alternative approach to simulate wind farm performance is presented: the use of a data-driven surrogate model that is trained on time series that were generated by the in-house aeroelastic tool BHawC. This surrogate model provides results with potentially higher fidelity than more simplistic engineering models but is computationally much cheaper than BHawC simulations.

CFD simulation of a multi-rotor system using diffuser augmented wind turbines by lattice Boltzmann method

A diffuser-augmented wind turbine (DAWT) achieves greater power generation efficiency by increasing wind speed through the diffuser. Nevertheless, scaling up this technology is difficult because of the considerable amount of wind drag on the diffusers. To overcome this difficulty, a multi-rotor system with two or more wind turbines on the same structure is one approach to increasing wind turbine power output. This research proposes a computational fluid dynamics (CFD) method to evaluate the hydrodynamic performance of a large-scale multi-rotor system of DAWTs. Compared to conventional wind turbines, CFD simulations of DAWTs necessitate higher computational costs because of the need of high-resolution meshes for the diffuser. Furthermore, the computational cost of a multi-rotor system increases with the number of rotors. To address the issue of high computational cost, we use the Lattice Boltzmann Method (LBM), which is well suited to large-scale CFD simulations. A wind turbine is modeled as an actuator line model and a diffuser as a wall boundary. An adaptive mesh refinement approach generates higher resolution meshes near the rotor and diffuser. LBM simulations were conducted for a single DAWT and a multi-rotor system with five DAWTs. The LBM results of the wake velocity and pressure distributions were in agreement with those obtained from wind tunnel experiments and general CFD methods in earlier studies. To investigate the diffuser gap effects, we simulated five DAWTs with diffuser gaps of 5%–25% of the diffuser diameter. The power gain of each DAWT was assessed. Great performance improvements were found with diffuser gaps of 20% and 25% of the diffuser diameter. On average, the five DAWTs achieved a power gain of more than 10%. These findings confirmed the accurate evaluation capability of the proposed CFD method for hydrodynamic characteristics of multi-rotor systems using DAWTs.

Frequency Regulation Adaptive Control Strategy of Wind Energy Storage System for Wind Speed Uncertainty

In order to reduce the negative influence of wind speed randomness and prediction error on frequency modulation, the reliability of the wind storage system was assessed effectively. In the wind storage frequency modulation system, a state of charge (SOC) adaptive adjustment method for wind speed randomness is proposed. Firstly, through the correlation analysis between the standby capacity of frequency modulation and the output power of wind turbine, the uncertainty of its frequency modulation capacity is revealed. Secondly, in view of the uncertainty of wind turbine frequency modulation, the output power of energy storage frequency modulation is optimized with the goal of minimizing the frequency modulation power deviation of the wind storage front under the framework of model predictive control, and the improved whale optimization algorithm (WOA) is used to solve the problem. Finally, the simulation results show that, under the given 5 min continuous disturbance, the root mean square of frequency regulation of the proposed restoration method is reduced by 56.65% compared to the SOC recovery base point set to 0.5. Under continuous large perturbations, the maximum frequency deviation is reduced by 0.0455 Hz. This effectively shows that this method can not only improve the frequency modulation reliability of wind power system but also improve the continuous frequency modulation capability of energy storage system.

Power Augmentation of Gas Turbine Using Exhaust Flue Gas Operated Vapor Absorption Machine

Industrial gas turbines (GT) that operate at constant speed are constant-volume-flow combustion machines. As the specific volume of air is directly proportional to the temperature, an increase in air density results in a higher air mass flow rate at low air ambient temperatures. This results more mass of air is now passes through same volume gas turbine space. Consequently, the gas turbine power output increases proportionally with the increase in air mass flow rate. For geographic regions where warm and humid months are predominant throughout the year, significant power loss occurs during these months, and a higher rate of expensive fuel is fired at reduced power output. A gas turbine inlet air cooling technique is a useful option to enhance GT output. One of the innovative options for power augmentation of gas turbine is inlet air conditioning using a vapor absorption machine (VAM). Using VAM for GT intake air conditioning has the advantage over other systems in that it can be operated from waste heat. Many gas turbine machines, particularly natural gas pumping stations, are operated without heat recovery steam generator (HRSG) or heat recovery option. Valuable heat energy is escaped with exhaust flue gas in these systems. This paper demonstrates how GT exhaust waste heat can be utilized for its own power generation enhancement. In this study, a flue gas-based vapor absorption machine is selected for cooling the inlet air of a 26 MW gas turbine, which is used to drive the booster compressor of a natural gas pumping station. The capacity of the VAM for the required cooling effect has been calculated. The expected GT power enhancement from intake air gas conditioning has also been estimated in this paper.

Utilizing Reversed Brayton Cycle for Enhanced Cooling Tower Technology

This study demonstrates an enhanced cooling tower technology (ECTT) under which the ambient air is precooled and dehumidified to improve the cooling tower efficiency. The reverse Brayton cycle, comprising a compressor, an air cooler, an expander, and a heat and mass exchanger, is used to precondition the ambient air before entering the cooling tower. A plant-scale thermodynamic model is constructed for ECTT, and the system parameters are optimized through genetic algorithm based on the maximum power gained from the system, including the effects of reducing the makeup water for the cooling tower. Compared to conventional cooling towers, the ECTT achieved sub-wet bulb cooling of water, which is 9.5 °C below the wet bulb temperature of ambient air, reduces steam condensation temperature by 34%, and increases steam turbine power output by 4.3%. It also reduces evaporative losses in the cooling tower and reduces the makeup water by 36%. With cooling tower exhaust recirculated in a closed loop, makeup water requirements are further reduced with additional gains in power output.

Wind Power Integration and Challenges in Low Wind Zones. A Study Case: Albania

High wind performance systems are influenced by many factors such as site wind resources and configuration, technical wind turbine features and many financial conditions. Scenario planning and modelling activities often focus on restricted parameters and numbers to justify wind power plant performance. To better understand possible pathways to scaling up the distributed wind market in Albania, a deep and multidimensional calculations based on Monte Carlo analysis, using RETScreen and wind JEDI model, to assess socio-economic impact as a function of turbine output power, operating and maintenance cost and many other financial inputs by testing different WT (i.e., VESTAS, GAMESA, W2E and NORDEX) with rated power from 3.45 MW up to 4.5MW applied on LCOE, NPV, SPP, equity payback, B-C, after-tax IRR on equity and effects of GHG credits extended at a sensitivity range of ±35% is scientifically performed. From the simulation results LCOE reaches a minimal value of €43.48/MWh, if the debt rate is 99 % and a debt interest rate of 5.0%, a TotCapEx of €828/MW (-35 % less expenditures) indexed as the best scenario. For the base case scenario LCOE results €62.79/MWh, when applying a debt rate of 80% and a TotCapEx of (€1274/MW), while in the worst-case scenario LCOE impart a maximal value of €87.63/MWh if a TotCapEx of €1720/MW (+35 % more expenditures) and a share of 52 % debt rate is applied. Local annual economic impact (m€) during construction period and operating period evaluated in the wind JEDI model result around m€ 89.92 and m€ 23.54, respectively. As a conclusion, wind power plants (WPP), installed in low wind zones (Albania and many other EU countries) would be of interest if an electricity export rate of 110€/MWh, and a GHG credit rate of €50/tCO2 were accepted.

Research on fault detection and remote monitoring system of variable speed constant frequency wind turbine based on Internet of Things

In order to study the operating characteristics of variable speed constant frequency wind turbine under different working conditions and the monitoring system of wind turbine. In this paper, the simulation model of each component system of wind turbine is established by MATLAB/Simulink module, and the influence law of different wind speed and ground fault types on the output power of wind turbine is studied. The active power of wind turbines under different short-circuit fault types is compared. At the same time, in order to realize real-time monitoring of wind turbine speed and output power, an online monitoring system for wind turbine operation based on industrial Internet of Things is proposed, and the composition and operation characteristics of this remote monitoring system are given. The practical application shows that the on-line monitoring system can accurately and remotely monitor the running status of the wind turbine and avoid the unstable running of the wind turbine. The research conclusions of this paper can provide reference for the design and construction of wind turbines and the operation of connecting to the power grid.

Effects of compositional uncertainties in cracked NH3/biosyngas fuel blends on the combustion characteristics and performance of a combined-cycle gas turbine: A numerical thermokinetic study

Blending of partially cracked ammonia with biosyngas is an attractive strategy for improving NH3 combustion. In practice, products of biomass gasification and those of thermo-catalytic cracking of NH3 are subject to some compositional uncertainties. Despite their practical importance, so far, the effects of such uncertainties on combustion systems remained largely unexplored. Hence, this paper quantifies the effects of small compositional uncertainties of reactants upon combustion of partially cracked NH3/syngas/air mixtures. An uncertainty quantification method, based on polynomial chaos expansion and a data-driven model, is utilised to investigate the effects of uncertainty in fuel composition on the laminar flame speed (SL) and adiabatic flame temperature (Tad) at different inlet pressures (Pi). The analysis is then extended to the power output of a combined-cycle gas turbine fuelled by the reactants. It is found that 1.5% fuel compositional uncertainty can cause 12–21% of SL uncertainty depending on the inlet pressure. Furthermore, the effect of compositional uncertainty on Tad increases at higher ratios of H2 to NH3. Sensitivity analysis reveals that the uncertainty of CO contribution to SL uncertainty is higher than that of NH3, while the trend is reversed for the Tad uncertainty. In addition, the power output from the combined-cycle gas turbine system varies between 4 and 6% with 1.5% of fuel compositional uncertainty. This become more noticeable at elevated Pi [5–10 atm], particularly when the fuel mixture contains high H2 which is the main contributor to Tad variability.

RANS representation of transition and separation over a low-Re number blade section at high angle of attack

Systems based on wind energy harvesting can successfully meet part of the increasing green energy demand worldwide. However, wind turbines operation might be undermined by varying atmospheric conditions, which could result in an increase of angle of attack and consequent onset of flow separation phenomena, especially at low Reynolds numbers. Such conditions are strongly influenced by blades geometry, and they negatively affect structural integrity and power output of wind turbines. For this reason, it is crucial to define a tool capable of swiftly allowing numerical investigations on different geometrical configurations to delay and mitigate flow separation occurrence. The present work aims at modelling laminar-turbulent transition and turbulent flow separation over a wind turbine blade section operating at angle of attack = 15°, Re = 66000 and Pr = 0.71 by means of a steady RANS approach. Turbulence is treated by means of the Transition SST k-ω and the Transition k-kL-ω models. The main aerodynamic and thermal coefficients are evaluated and compared against a high-order accurate DNS database for validation. The results highlight, for the present test case, a better capability of the Transition SST k-ω of perceiving the main thermo-fluid dynamic features of the separated flow over the blade section.

Experimental study of turbulent inflow on the aerodynamic performance of a wind turbine with Gurney flaps

Nowadays, wind turbines operate within complex inflow environments. Meanwhile, installing Gurney flaps on existing wind turbines could enhance wind energy efficiency. However, limited research has been conducted on the variation of aerodynamic characteristics of a wind turbine equipped with Gurney flaps under turbulent inflow conditions. Hence, wind tunnel test comparisons were made between the output power, wind load, and wake characteristics of a model wind turbine with and without Gurney flaps. The results demonstrated a correlation between the additional power increase in the wind turbine equipped with Gurney flaps and the aerodynamic variation of the corresponding airfoil. Gurney flaps could be effective at higher tip speed ratios, and the power enhancement efficiency initially increased but then decreased as turbulence intensity increased from a low value to 19.0%. Installing Gurney flaps resulted in significant pulsation peaks within the original inertial sub-range. The time-averaged thrust coefficient shifts upward, but the difference decreases slightly under turbulent conditions. Wake analysis revealed that the presence of additional wake velocity deficits primarily concentrated within the near-wake region, which extends along the spanwise direction. These findings could enhance a better understanding of the aerodynamic performances of wind turbines installing Gurney flaps under varying turbulent flow conditions.

Wind Power Forecasting with Machine Learning Algorithms in Low-Cost Devices

The urgent imperative to mitigate carbon dioxide (CO2) emissions from power generation poses a pressing challenge for contemporary society. In response, there is a critical need to intensify efforts to improve the efficiency of clean energy sources and expand their use, including wind energy. Within this field, it is necessary to address the variability inherent to the wind resource with the application of prediction methodologies that allow production to be managed. At the same time, to extend its use, this clean energy should be made accessible to everyone, including on a small scale, boosting devices that are affordable for individuals, such as Raspberry and other low-cost hardware platforms. This study is designed to evaluate the effectiveness of various machine learning (ML) algorithms, with special emphasis on deep learning models, in accurately forecasting the power output of wind turbines. Specifically, this research deals with convolutional neural networks (CNN), fully connected networks (FC), gated recurrent unit cells (GRU), and transformer-based models. However, the main objective of this work is to analyze the feasibility of deploying these architectures on various computing platforms, comparing their performance both on conventional computing systems and on other lower-cost alternatives, such as Raspberry Pi 3, in order to make them more accessible for the management of this energy generation. Through training and a rigorous benchmarking process, considering accuracy, real-time performance, and energy consumption, this study identifies the optimal technique to accurately model such real-time series data related to wind energy production, and evaluates the hardware implementation of the studied models. Importantly, our findings demonstrate that effective wind power forecasting can be achieved on low-cost hardware platforms, highlighting the potential for widespread adoption and the personal management of wind power generation, thus representing a fundamental step towards the democratization of clean energy technologies.

Proposing an Innovative Method for Predicting Optimum Wind Generator Output

Renewable energy sources offer environmental benefits and a more consistent supply, making them essential for integrating into energy fusions. Network operators have challenges with intermittent and stochastic power sources like wind power owing to their fluctuating production. This study presents an innovative method for predicting wind turbine power output, from any location in a vast region with high efficiency. To ensure a representative sample of geographical locations, the research region is divided into a grid and sampled accordingly. The ideal production times for each sub-area are discovered and utilized to anticipate electricity output. It uses the LSTM model to predict meteorological data and the linear model to approximate wind turbine power curves. The sub-area showed one-day wind speed, direction, and power projections clustering scores. Wind and directional predictions yielded RMSE (0.35m\sec, 7.9rad) and R2 (94%, 71%) scores.