Optimal Turbine Research Articles

Hydrokinetic energy applications within hydropower tailrace channels: Implications, siting, and U.S. potential

Hydropower tailrace channels are unique and attractive locations for hydrokinetic energy harvesting due to fast currents, scheduled flow releases, proximity to existing structural and electrical infrastructures, and low risk of additional environmental impacts. However, energy-extracting devices create flow resistance, inducing a small but measurable water level increase which may diminish the available hydraulic head and reduce hydropower generation, defeating the initial value proposition. This study combines a one-dimensional momentum balance approach with the backwater equation for surface-varying open channel flow to analyze the water level increase and determine the optimal turbine siting distance that maximizes the net power production (balancing hydropower loss vs. hydrokinetic gain), as a function of the channel hydraulic conditions and the hydrokinetic turbine characteristics. Finally, using a subset of sites from the U.S. hydropower fleet, we provide a high-level estimation of the hydrokinetic potential available in tailraces in the United States and discuss two case studies. This work advocates for the adoption of hydrokinetic turbines downstream of dams as an opportunity to increase energy production at existing plants and Non-Powered Dams (NPDs) with minimal structural intervention, and, alternatively, as viable sites for large-scale field testing for hydrokinetic devices.

Optimization of a Gorlov Helical Turbine for Hydrokinetic Application Using the Response Surface Methodology and Experimental Tests

The work presents an analysis of the Gorlov helical turbine (GHT) design using both computational fluid dynamics (CFD) simulations and response surface methodology (RSM). The RSM method was applied to investigate the impact of three geometric factors on the turbine’s power coefficient (CP): the number of blades (N), helix angle (γ), and aspect ratio (AR). Central composite design (CCD) was used for the design of experiments (DOE). For the CFD simulations, a three-dimensional computational domain was established in the Ansys Fluent software, version 2021R1 utilizing the k-ω SST turbulence model and the sliding mesh method to perform unsteady flow simulations. The objective function was to achieve the maximum CP, which was obtained using a high-correlation quadratic mathematical model. Under the optimum conditions, where N, γ, and AR were 5, 78°, and 0.6, respectively, a CP value of 0.3072 was achieved. The optimal turbine geometry was validated through experimental testing, and the CP curve versus tip speed ratio (TSR) was determined and compared with the numerical results, which showed a strong correlation between the two sets of data.

Effect of Blade Gap Ratio on Turbine Performance in Drag-Based Vertical-Axis Wind Turbines

The aim of this study was to improve the performances of three-bladed vertical-axis wind turbines with gap distance. For this purpose, turbine design conditions such as a gap ratio between the blades and the addition of blades were changed to provide performance increase, and an aerodynamic performance-enhancing performance setup placed in front of the vertical-axis wind turbine was also used. Therefore, in this study, the effects of gap distance and blade addition on wind turbine performance were investigated, especially by keeping the aspect ratio of the vertical-axis wind turbine constant. The investigation of the effects of turbine design parameters, such as the gap distance and additional blade number, on turbine performance was carried out using the ANSYS Fluent program with a numerical analysis method validated with experimental data. The turbine gap ratio, at which the maximum torque coefficient of a three-bladed vertical-axis wind turbine is obtained, was determined to be 40% without performance setup and 30% with performance setup. It was determined that the average torque coefficient value obtained from the turbine with the addition of turbine blades and with the performance setup increased by approximately 66% compared to the torque coefficient value obtained from the three-bladed turbine without the performance setup. It was also determined that the maximum power coefficient obtained from the vertical-axis wind turbine with optimum turbine design parameters with the performance setup increased by approximately 46% compared to the maximum power coefficient obtained from the version of the turbine without the performance setup.

Scrutinizing effect of temperature and pressure variation of a double-pressure dual-cycle geothermal power plant turbines on the temperature profile and heat gain of the heat exchangers

In geothermal power plants with dual pressure cycle technology, the optimisation of turbine inlet parameters depending on the pressure and temperature of the geothermal fluid is a very important parameter affecting the production capacity of such plants. In combined systems, where the second stage (low pressure) is fed by the first stage (high pressure), failure to determine the appropriate operating conditions leads to the problem of not achieving optimum performance. In this context, the study aims to develop a methodology for predicting the performance of the system, based on the geothermal water temperatures entering and leaving the heat exchangers, in order to clearly see the effect of the operations carried out within the scope of optimising the turbine inlet parameters on the system behaviour. In this study, EBSILON® Professional software developed by Steag GbmH was utilised to simulate the determined correlations. The effect of the heat exchangers (preheater, evaporator and superheater) in both stage-1 and stage-2 on the temperature profiles and heat gains were determined at 10–17 bar, 126.5–165 °C for Turbine-1 and 4–8 bar, 84–135 °C for Turbine-2. Optimum turbine inlet temperature and pressure have been determined for maximum heat input and exergy efficiency. In this context, each cycle in the Energy Converter System (ECS) was first simulated by changing the turbine input parameters and then thermal analyses of the system were performed using the performance outputs obtained from the simulation software. For turbine-1, it is observed that heat transfer decreases in stage-1 with increasing pressure and temperature, while heat transfer increases in stage-2 fed from stage-1. After 12 bar and 136 °C, the heat transfer of the ECS started to increase and the maximum heat transfer amount was reached at 17 bar and 155 °C. However, it was determined that the exergy efficiency of the ECS started to decrease after 15 bar and 147.6 °C. For turbine-2, it was found that the increase in pressure and temperature decreases the ECS heat transfer but increases the exergy efficiency. As a result of numerous iterations with EBSILON® Professional software, a maximum exergy efficiency of 50.53% was achieved in turbine-1 (15 bar, 147.6 °C) and turbine-2 (8 bar, 114 °C).

Strategic analysis of wind energy potential and optimal turbine selection in Al-Jouf, Saudi Arabia

Wind power is considered one of the most environmentally friendly and rapidly growing form of renewable energy. This study aims at assessment of wind power potential for Al-Jouf region in Saudi Arabia. A long term historical wind speed data of 21 years (2000–2021) was analytically modeled using Weibull distribution function to determine the wind characteristics. The Weibull parameters and average wind speed for monthly and yearly were evaluated using MATLAB program. An online wind power calculator developed by Meteotest was used to evaluate the wind energy by selecting various turbines. Six types of commercial wind turbines of 3 MW capacity were selected for wind power assessment to optimize the turbine selection. Our analysis showed that average wind speed varies between 3.88 m/s and 4.99 m/s and an overall average wind speed is 4.38 m/s. The most frequent wind speed observed was 3.9 m/s with a probability of 20 % approximately. The Weibull distribution parameters, shape parameter “k” values ranged between 1.89 and 2.21 with an average of 1.98 and scale factor “c” values varied between 4.41 and 5.66 m/s with a mean value of 4.86 m/s. Performance evaluations of selected wind turbine models reveal that the Vestas V126 turbine outperforms others at the Al-Jouf site, generating an annual energy yield of 3779400 kWh at a capacity factor of 14.4 %. These results suggest that by constructing a wind farm consisting of 100 V126 turbines may compensate for the energy needs of 46000 individuals. These findings establish Al-Jouf as a viable location for utility-scale wind power projects, offering valuable insights for turbine manufacturers, developers, operators, and policymakers interested in deploying large-capacity turbines in the region. The method used in this study for wind power analysis and turbine selection is simple and helpful to provide an initial assessment about the site suitability and turbine selection without undergoing exhaustive efforts.

Multi-objective Mathematical Model for Optimal Wind Turbine Placement in Wind Farm under Uncertainty

The main objective of this research is to introduce three energy risk management models grounded in optimization techniques for the strategic placement of wind turbines, considering wake effects and uncertainties in wind speed and direction. For this purpose, wind speed and direction data are gathered, and Monte Carlo simulation is employed to model the uncertainties. Subsequently, the risk management models undergo optimization using Non-Dominated Sorting Genetic Algorithm II (NSGA-II), Pareto envelope-based selection algorithm II (PESA-II), and Multi-Objective Particle Swarm Optimization (MOPSO) algorithms. Findings reveal that the wind farm's maximum power output reaches approximately 5.8 megawatts across all three algorithms and optimal turbine placements. A risk assessment was conducted using a tenth percentile criterion, revealing a significant production risk within the study area, with production falling below 1.8 megawatts in 90% of cases. Regarding the performance evaluation of the algorithms across all three models, superior performance in terms of solution proximity to the ideal solution is exhibited by PESA-II, while enhanced diversity and solution spread compared to the other algorithms are demonstrated by NSGA-II.

Thermodynamic analysis and optimization of the semi-closed oxy-combustion supercritical CO2 power cycle with blending hydrogen into the natural gas/syngas

The semi-closed oxy-combustion supercritical CO2 (sCO2) cycle is regarded as a promising power cycle featuring the combined advantages of high efficiency and nearly 100 % CO2 capture. Blending hydrogen into fuels has attracted significant attention in the research of gas turbine and internal combustion engine power generation as it contributes to economy-wide decarbonization and bolsters the broader energy sources, especially green hydrogen. The present work carried out the thermodynamic analysis of a semi-closed sCO2 cycle to investigate how the hydrogen content in natural gas or syngas affects the operating parameters and performance. The results show that the energy efficiency decreases with increasing hydrogen content in natural gas whereas the maximum efficiency exists for the syngas with 60 % H2. The key factor is the increasing H2O content in the flue gas caused by adding hydrogen in fuels enhances both the heat-work conversion in the turbine and the heat transfer in the regenerator. The optimal turbine inlet temperature is around 1150 °C, which is the same as the basic cycle. The optimal turbine inlet pressure for natural gas and natural gas with 40 % H2 is both 28 MPa, while the optimal turbine inlet pressure decreases with hydrogen content in syngas. For all the fuels, the optimal turbine outlet pressure becomes lower for the higher hydrogen content. The maximum efficiencies obtained for natural gas, natural gas with 40 % H2, and syngas with 60 % H2 are 56.08 %, 55.52 % and 56.15 %. Finally, it is found that the recompression cycle and the multiple combustor arrangement (reheat cycle) cannot be the effective configuration for the semi-closed oxy-combustion sCO2 power cycle. The heat from the sCO2 recompression conflicts with the compression heat of the ASU, reducing the energy efficiency by 0.27 %, 0.34 %, and 1.23 % for the three fuels, respectively. The limitation of the regenerator allowable temperature requires the reheat cycle to either reduce the turbine inlet temperature or reduce the turbine outlet pressure, decreasing the energy efficiency for natural gas by 1.83 % and 6.38 % due to the reduced turbine output power or significantly increasing compression power.

Integration and Optimization of a Waste Heat Driven Organic Rankine Cycle for Power Generation in Wastewater Treatment Plants

The study focuses on achieving energy self-sufficiency in Wastewater Treatment Plants by proposing a comprehensive model for integrating, sizing, and optimizing an Organic Rankine Cycle system. The Organic Rankine Cycle system is designed to utilize waste heat from the gensets at As Samra Wastewater Treatment Plant in Jordan, where it will contribute to the overall electrical energy supply of the plant. Real data from As Samra Wastewater Treatment Plant is used to model and calculate the available waste heat using TRNSYS® software. The Organic Rankine Cycle model is then developed using ASPEN PLUS® software to explore the impact of operational parameters and determine their optimal values for maximizing the plant's energy profile. An economic analysis is conducted to assess the feasibility of the proposed model, considering system components, installation, operation, and maintenance costs. To optimize the Organic Rankine Cycle system, the study employs the Multi-Output Support Vector Regression technique to capture nonlinear relationships between independent variables (fluid type, turbine inlet pressure, turbine inlet temperature, turbine outlet pressure, and mass flow rate) and dependent variables (pump power input, waste heat input, and turbine specific work). The Osprey optimization algorithm is used to address the multi-objective optimization problem, with the proposed Pareto-based Osprey Optimization Algorithm and the Multi-Objective Particle Swarm Optimization technique being employed to evaluate critical performance and economic parameters such as system thermal efficiency, net power output, and the levelized cost of electricity. The results of the optimization strategies indicate that the M-SVR model's prediction accuracy is significantly improved after parameter optimization, with the model returning high R2 and low Mean Square Error values of 0.991 and 0.00216, respectively. The Pareto-Based Osprey Optimization Algorithm optimizer identifies the best working fluid as Isobutane/Isopentane in a ratio of 66:34, with optimal turbine inlet pressure and temperature of 15 bars and 218 °C, respectively. The Organic Rankine Cycle model at these optimal conditions achieves a cycle efficiency of 19.93 % and an Levelized Cost of Electricity value of 0.0353 USD/kWh.

Non-linear turbine selection for an OWC wave energy converter

Turbine-induced damping is a critical parameter affecting the performance of oscillating water column (OWC) wave energy converters. Therefore, selecting the appropriate turbine-chamber combination is an essential step in their design. In this work, a methodology is developed to determine the optimum turbine diameter for a given chamber, i.e., the diameter which maximizes the pneumatic energy capture of the chamber under an extensive set of wave conditions—covering virtually the entire range of wave conditions relevant for wave energy exploitation. This novel approach combines physical and numerical modelling with dimensional analysis. Importantly, it results in a turbine diameter that enables the turbine to operate at maximum efficiency. Through the different modelling techniques applied, the methodology accounts for air compressibility effects and other non-linear effects. It is applicable to non-linear turbines, with the study focusing on the promising biradial turbine. The results indicate that using the proposed methodology to select the turbine diameter significantly improves the capture-width ratio of the OWC, with increases of up to 100% for individual sea states. Two turbine diameters were identified as appropriate for the proposed OWC chamber design, 1.1 m for low-energy sites, and 1.4 m for mid- and high-energy sites.

Optimal control for helicopter/turboshaft engine system based on hybrid variable speed

In order to address the limitation of fixed-ratio transmission (FRT), which compromises the attainment of both optimal main rotor speed and optimal power turbine speed, an optimal speed control method based on hybrid variable speed (HVS) is proposed. Firstly, based on the integrated performance calculation model of helicopter/turboshaft engine system, the distribution factors of variable speed are applied, and the integrated optimization method of optimal speed is proposed based on the minimum engine fuel flow. Subsequently, an online estimation technique employing a high-order filter is devised and engineered to achieve superior cascaded control of turboshaft engines. Finally, a novel real-time optimal speed control method based on hybrid variable speed is proposed. The simulation results under different operation conditions demonstrate that regardless of whether it is FRT or HVS, the optimal main rotor speed increases with forward velocity. In the case of HVS, turboshaft engine degradations have a significant impact on the optimal power turbine speed rather than optimal main rotor speed. Adopting an estimation method based on high-order filtering for gas turbine rotational acceleration proves more advantageous in mitigating high-frequency oscillation and continuous saltation of estimated values. Moreover, in comparison with the optimal speed control method of FRT, HVS-based approach enables simultaneous attainment of the optimal main rotor speed and power turbine speed, thereby enhancing the overall efficiency of the integrated helicopter/turboshaft engine system and significantly decreasing engine fuel consumption by over 2%. Consequently, there has been a remarkable enhancement in the overall performance of the integrated helicopter/turboshaft engine system.

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.

Machine learning to rapidly predict turbine yaw angles for wake steering

Wake steering is an important control strategy to boost power production of a wind farm. Because of computational expense and problem complexity, wind farm layouts are typically optimized assuming they will operate without wake steering. However, performance gains are possible by simultaneously optimizing wind farm layout and the yaw angles for wake steering. In this paper, we present a method to train a machine learning model to predict turbine yaw angles as a function of their position relative to other turbines in the wind farm and the inflow wind speed. This model is able to predict turbine yaw angles with an R2 value of 0.98. The model also produces turbine yaw angles with wind farm power production that is similar to yaw angles that have been directly optimized. This method to rapidly compute optimal turbine yaw angles for wake steering enables control co-design of wind farms and the associated performance increase.

Optimizing wind farm layout for enhanced electricity extraction using a new hybrid PSO-ANN method

With the growing need for renewable energy, wind farms are playing an important role in generating clean power from wind resources. The best wind turbine architecture in a wind farm has a major influence on the energy extraction efficiency. This paper describes a unique strategy for optimizing wind turbine locations on a wind farm that combines the capabilities of particle swarm optimization (PSO) and artificial neural networks (ANNs). The PSO method was used to explore the solution space and develop preliminary turbine layouts, and the ANN model was used to fine- tune the placements based on the predicted energy generation. The proposed hybrid technique seeks to increase energy output while considering site-specific wind patterns and topographical limits. The efficacy and superiority of the hybrid PSO-ANN methodology are proved through comprehensive simulations and comparisons with existing approaches, giving exciting prospects for developing more efficient and sustainable wind farms. The integration of ANNs and PSO in our methodology is of paramount importance because it leverages the complementary strengths of both techniques. Furthermore, this novel methodology harnesses historical data through ANNs to identify optimal turbine positions that align with the wind speed and direction and enhance energy extraction efficiency. A notable increase in power generation is observed across various scenarios. The percentage increase in the power generation ranged from approximately 7.7% to 11.1%. Owing to its versatility and adaptability to site-specific conditions, the hybrid model offers promising prospects for advancing the field of wind farm layout optimization and contributing to a greener and more sustainable energy future.

Wind energy feasibility and wind turbine selection studies for the city Surat, India

Abstract Wind energy represents a clean, abundant and cost-effective power source, fostering job growth and environmental mitigation. Although wind energy harnesses several gigawatts today, its availability hinges on diverse factors, with geographical location standing out. Commercial turbines, with varying capacity ranges, saturate the market. Locating site-specific suitability and matching the appropriate turbine to meet specific requirements are of paramount importance. This study aims to assess the feasibility of wind energy in Surat, Gujarat, India and select an optimal small commercial turbine for residential use. The research involves Rayleigh and Weibull probability distribution functions based on yearlong velocity data. These distributions are fitted with actual data, revealing the most probable velocity (vmf = 3 m/s) and velocity at maximum power (vpmax = 5 m/s). The power availability of the site has been assessed as 42.6 W/m2 using both graphical and analytical methods. Several commercial turbines have been shortlisted based on on-site power criteria and their specifications are evaluated against site power availability. A comparative analysis culminates in identifying the most suitable turbine for the location. The best suitable turbine for the site with an annual energy yield of 8 MW has been suggested amongst selected turbines for small-scale residential applications.

Off-design operation of supercritical CO2 Brayton cycle arranged with single and multiple turbomachinery shafts for lead-cooled fast reactor

The lead-cooled fast reactor (LFR) engenders novel requisites for the energy conversion system when applied to maritime ships and distributed energy supply systems. The supercritical carbon dioxide (sCO2) Brayton cycle, with the advantages of high efficiency, space-saving, simplicity, and flexibility, is a promising candidate for LFR. Here, we present the first study on the off-design operation of the sCO2 cycle for LFR, with the turbine and compressor arranged on a single shaft (coaxial-shaft) or two shafts (split-shaft). The off-design performances of the system are investigated using four rotational speed (RS) control-based hybrid control strategies. We showed that the split-shaft system controlled by separate turbine speed and compressor speed has a wider operating space of power load than the coaxial-shaft system. Among the four investigated control strategies, the RS-inventory hybrid control can achieve the best thermal efficiency during load variation by operating at the turbine speed and mass flow rate corresponding to the optimal turbine isentropic efficiency. The maximum operating space of power load (0%–100 %) can be achieved by the RS-turbine inlet pressure hybrid control and the RS-bypass hybrid control. Our study can provide guidance for the flexible and safe part-load operation for small-scale modular Gen-IV nuclear reactors.

Optimising O&M scheduling in offshore wind farms considering weather forecast uncertainty and wake losses

Offshore wind farms, while strategically positioned to maximise wind energy capture, are exposed to harsh environments, impacting their reliability and accessibility. Low reliability and accessibility in turn translate into high Operation and Maintenance (O&M) costs, which represent approximately 30% of the total lifetime costs of the project. One approach to reduce O&M costs is to improve maintenance planning decisions. The present paper presents a novel O&M scheduling methodology for offshore wind farm operations based on wind and wave forecasts. The methodology forecasts farm yield considering farm wake effects to identify optimal operation schedules, vessel routing, and turbine maintenance sequences that minimise costs and revenue losses due to downtime. Weather forecast uncertainty is modelled and integrated into the decision-making process to minimise weather risks. A case study inspired by the WindFloat Atlantic floating wind project was used to demonstrate the optimisation potential. Results suggest that adopting the proposed methodology over business-as-usual scheduling significantly boosts offshore wind farm operational profits during the forecasted period, with increases ranging from 2% to 24%, depending on the scenario considered. It was found that integrating the wind farm wake losses into the scheduling decisions can improve the optimal scheduling solution and further minimise total operation costs.

Predicting wind farm wake losses with deep convolutional hierarchical encoder–decoder neural networks

Wind turbine wakes are the most significant factor affecting wind farm performance, decreasing energy production and increasing fatigue loads in downstream turbines. Wind farm turbine layouts are designed to minimize wake interactions using a suite of predictive models, including analytical wake models and computational fluid dynamics simulations (CFD). CFD simulations of wind farms are time-consuming and computationally expensive, which hinder their use in optimization studies that require hundreds of simulations to converge to an optimal turbine layout. In this work, we propose DeepWFLO, a deep convolutional hierarchical encoder–decoder neural network architecture, as an image-to-image surrogate model for predicting the wind velocity field for Wind Farm Layout Optimization (WFLO). We generate a dataset composed of image representations of the turbine layout and undisturbed flow field in the wind farm, as well as images of the corresponding wind velocity field, including wake effects generated with both analytical models and CFD simulations. The proposed DeepWFLO architecture is then trained and optimized through supervised learning with an application-tailored loss function that considers prediction errors in both wind velocity and energy production. Results on a commonly used test case show median velocity errors of 1.0%–8.0% for DeepWFLO networks trained with analytical and CFD data, respectively. We also propose a model-fusion strategy that uses analytical wake models to generate an additional input channel for the network, resulting in median velocity errors below 1.8%. Spearman rank correlations between predictions and data, which evidence the suitability of DeepWFLO for optimization purposes, range between 92.3% and 99.9%.

Wind speed prediction for site selection and reliable operation of wind power plants in coastal regions using machine learning algorithm variants

The challenge of predicting wind speeds to facilitate site selection and the consistent operation of wind power plants in coastal regions is a global concern. The output of wind turbines is subject to fluctuations corresponding to changes in wind speed. The unpredictable characteristics of wind patterns introduce vulnerabilities to wind power facilities in wind power plants. To address this unpredictability, an effective strategy involves forecasting wind speeds at specific locations during wind power plant operations. While previous research has explored various machine learning algorithms to tackle these issues, satisfactory results have not been achieved, and Bangladesh faces challenges in this regard, especially in low-wind speed areas. This study aims to identify the most accurate machine learning-based algorithm to forecast the short-term wind speed of two areas (Kutubdia and Cox's Bazar) located on the eastern coast of Bangladesh. Wind speed data for a span of 21.5 years, ranging from January 2001 to June 2022, were sourced from two outlets: the Bangladesh Meteorological Department and the website of NASA. Wind speed has been forecasted using 14 different regression-based machine learning models with a comprehensive overview. The results of the experiment highlight the exceptional predictive performance of a boosting-based ensemble method known as categorical boosting, especially in the context of forecasting wind speed data obtained from NASA. Based on the testing data, the evaluation yields remarkable results, with coefficients of determination measuring 0.8621 and 0.8758 for wind speed in Kutubdia and Cox's Bazar, respectively. The study underscores the critical importance of prioritizing optimal turbine site selection in the context of wind power facilities in Bangladesh. This approach can yield benefits for stakeholders, including engineers and project owners associated with wind projects.

Drivers for optimum sizing of wind turbines for offshore wind farms

Abstract. Large-scale exploitation of offshore wind energy is deemed essential to provide its expected share to electricity needs of the future. To achieve the same, turbine and farm-level optimizations play a significant role. Over the past few years, the growth in the size of turbines has massively contributed to the reduction in costs. However, growing turbine sizes come with challenges in rotor design, turbine installation, supply chain, etc. It is, therefore, important to understand how to size wind turbines when minimizing the levelized cost of electricity (LCoE) of an offshore wind farm. Hence, this study looks at how the rated power and rotor diameter of a turbine affect various turbine and farm-level metrics and uses this information in order to identify the key design drivers and how their impact changes with setup. A multi-disciplinary design optimization and analysis (MDAO) framework is used to perform the analysis. The framework uses low-fidelity models that capture the core dependencies of the outputs on the design variables while also including the trade-offs between various disciplines of the offshore wind farm. The framework is used, not to estimate the LCoE or the optimum turbine size accurately, but to provide insights into various design drivers and trends. A baseline case, for a typical setup in the North Sea, is defined where LCoE is minimized for a given farm power and area constraint with the International Energy Agency 15 MW reference turbine as a starting point. It is found that the global optimum design, for this baseline case, is a turbine with a rated power of 16 MW and a rotor diameter of 236 m. This is already close to the state-of-the-art designs observed in the industry and close enough to the starting design to justify the applied scaling. A sensitivity study is also performed that identifies the design drivers and quantifies the impact of model uncertainties, technology/cost developments, varying farm design conditions, and different farm constraints on the optimum turbine design. To give an example, certain scenarios, like a change in the wind regime or the removal of farm power constraint, result in a significant shift in the scale of the optimum design and/or the specific power of the optimum design. Redesigning the turbine for these scenarios is found to result in an LCoE benefit of the order of 1 %–2 % over the already optimized baseline. The work presented shows how a simplified approach can be applied to a complex turbine sizing problem, which can also be extended to metrics beyond LCoE. It also gives insights into designers, project developers, and policy makers as to how their decision may impact the optimum turbine scale.

U susret revitalizaciji turbina HE Đerdap 2: Merenje rasporeda vektora brzina na ulazima turbina

The continuous flow measurement on hydroelectric turbine units is conventionally carried out using the relative methods (mostly Winter-Kennedy), where the complex velocity field in flow cross section is replaced by a single measured quantity. On a physical model, mapping parameters are determined. The Winter-Kennedy method is most used, where this relative (index) flow quantity is obtained by measuring differential pressure. The measurement uncertainty of the determined flow using this method is usually significantly higher than the uncertainty of other relevant quantities for determining the optimal turbine operating conditions. To reduce the flow uncertainty, it is necessary to perform "absolute measurements" of the entire velocity field and determine the necessary corrections to the index method under real operating conditions. This is especially true for plants where disposition is different from one used on physical model, which is the case of “Đerdap 2” and recognized problem of skewed inflow. With the aim of better assessing the hydraulic efficiency of turbines and to collect data on the actual operating velocity distribution in the inlet section in front of trash-rack needed for planned revitalization works, an innovative system for absolute flow measurement has been designed and implemented. The moving frame was designed and installed at the turbine inlet, upstream from the trash-rack. The 15 electromagnetic (EM) sensors and two redundant acoustic Doppler sensors were installed on horizontal bar. Each sensor measures all three velocity components. The frame moves by lifting along the entire height of the flow section, allowing the entire velocity field to be scanned. The position of the frame is tracked by two encoders, while two pressure sensors are used to measure the water depth. Measurements are synchronized with the local SCADA system, so appropriate turbine operation data are also used. Considering the specificities of the measurement system itself with newly developed EM sensors, as well as hydraulic conditions, an adequate procedure has been developed to assess the uncertainty of the measured flow. This paper presents the measurement method and provides some measurement results on the units of the “Đerdap 2” hydroelectric power plant.