Typical Turbine Research Articles

Pengaruh Ratio Overlap Sudu Terhadap Performa Turbin Air Poros Horizontal Savonius

<p><em>The Savonius air turbine has the ability to generate electricity from low-flow water sources and can be used in various types of water sources. However, the efficiency of this turbine is still low compared to other types of water turbines. Previous studies have shown that the overlap ratio of the blades can affect the turbine efficiency and power generated. Therefore, this study aims to investigate the effect of blade overlap ratio on the performance of the horizontal-axis Savonius water turbine. The purpose of this research is to study the influence of blade overlap ratio. It is hoped that the results of this study can be used as a reference in the production of horizontal-axis Savonius water turbines. </em><em></em></p>

Investigating energy production and wake losses of multi-gigawatt offshore wind farms with atmospheric large-eddy simulation

Abstract. As a consequence of the rapid growth of the globally installed offshore wind energy capacity, the size of individual wind farms is increasing. This poses a challenge to models that predict energy production. For instance, the current generation of wake models has mostly been calibrated on existing wind farms of much smaller size. This work analyzes annual energy production and wake losses for future, multi-gigawatt wind farms with atmospheric large-eddy simulation. To that end, 1 year of actual weather has been simulated for a suite of hypothetical 4 GW offshore wind farm scenarios. The scenarios differ in terms of applied turbine type, installed capacity density, and layout. The results suggest that production numbers increase significantly when the rated power of the individual turbines is larger while keeping the total installed capacity the same. Even for turbine types with similar rated power but slightly different power curves, significant differences in production were found. Although wind speed was identified as the most dominant factor determining the aerodynamic losses, a clear impact of atmospheric stability and boundary layer height has been identified. By analyzing losses of the first-row turbines, the yearly average global-blockage effect is estimated to between 2 and 3 %, but it can reach levels over 10 % for stably stratified conditions and wind speeds around 8 m s−1. Using a high-fidelity modeling technique, the present work provides insights into the performance of future, multi-gigawatt wind farms for a full year of realistic weather conditions.

Economic optimization of hybrid renewable energy systems supplying electrical and thermal loads of a tourist building in different climates

Due to the high potential of tourism in Iran and the high utilization of tourist buildings in Iran, as well as the high energy consumption in the buildings sector, this study aims to provide a feasible model for designing a renewable energy supply system for a tourist building in different climates of Iran. According to the country’s climate, 5 cities of Ahvaz, Bandar Abbas, Rasht, Mashhad, and Yazd were selected as the cities under study. The hybrid system also consists of photovoltaic panels, wind turbine, battery, and converter for power supply and boilers with natural gas fuel and geothermal heat pump to provide building thermal load. The heat pump is used to supply the load needed to preheat the building’s hot water. The system is connected to the electricity grid, so by selling excess electricity, the net project costs (NPC) will be reduced and the system can provide part of its need from the grid. The scenarios which were studied are of two categories. In the first category, all the thermal load is supplied by the boiler. After selecting the best economic scenario by Homer software in this category, the heat pump is added to the system by calculating the thermal load required to preheat the hot water. The scenarios used two types of wind turbines with a capacity of 10 and 50 kW and two types of 25-W panels with different efficiencies of 15.3% and 18%. Finally, a top-down scenario was chosen for each city. The best city to run the project on economic criteria is Mashhad with an NPC of $ 195,745 and a renewable fraction of $ 50.5. Using a heat pump to preheat the hot water, would also save 7% on fuel consumption and reduce CO2 production by 639,000 kg per year.

Offshore wind farm layouts designer software's

Offshore wind energy can be considered one of the renewable energy sources with high force potential installed in marine areas. Consequently, the best wind farm layouts identified for constructing combined offshore renewable energy farms are crucial. To this aim, offshore wind potential analysis is essential to highlight the best offshore wind layouts for farm installation and development. Furthermore, the offshore wind farm layouts must be designed and developed based on the offshore wind accurate assessment to identify previously untapped marine regions. In this case, the wind speed distribution and correlation, wind direction, gust speed and gust direction for three sites have been analyzed, and then two offshore wind farm layout scenarios have been designed and analyzed based on two offshore wind turbine types in the Northwest Persian Gulf. In this case, offshore wind farm layouts software and tools have been reviewed as ubiquitous software tools. The results show Beacon M28 and Sea Island buoys location that the highest correlation between wind and gust speeds is between 87% and 98% in Beacon M28 and Sea Island Buoy, respectively. Considerably, the correlation between wind direction and wind speed is negligible. The Maximum likelihood algorithm, the WAsP algorithm, and the Least Squares algorithm have been used to analyze the wind energy potential in offshore buoy locations of the Northwest Persian Gulf. In addition, the wind energy generation potential has been evaluated in different case studies. For example, the Umm Al-Maradim buoy area has excellent potential for offshore wind energy generation based on the Maximum likelihood algorithm, WAsP algorithm, and Least Squares algorithm.

Investigation of Performance Enhancements of Savonius Wind Turbines through Additional Designs

This study examines the literature on improving the low performance of Savonius wind turbines, which are a type of vertical axis wind turbine. The literature studies on improving the performance of Savonius turbines have been summarized into two categories: interior structural design and exterior additional design. Due to the extensive nature of studies focusing on interior design changes, this research primarily focuses on performance studies related to exterior design modifications of Savonius wind turbines, particularly in recent years. This study aimed to provide a comprehensive examination of these performance studies and contribute to the existing literature by presenting a systematic reference on this issue. To achieve this objective, a thorough review of turbine exterior design studies has been conducted. The focus was on determining the percentage increase in power coefficient achieved by turbines with exterior design modifications compared to the classical turbine versions. Here, it has been determined that the power coefficient values of Savonius wind turbines can reach approximately 0.400 through interior design changes. However, with the implementation of additional exterior design modifications, these power coefficient values can be further increased to around 0.520. Thus, within the scope of this study, it has been determined that the turbine power coefficients show a fairly good increase with exterior design techniques compared to interior design techniques.

Techno-economic assessment of offshore wind energy potential at selected sites in the Gulf of Guinea

Offshore wind power has been found to stand out among the most dynamic renewable energy technologies. With its long coastal line, Nigeria has an overwhelming advantage in developing marine energy resources to relieve the power crisis effectively. This work analyzed and characterized observation data of sea-surface wind speed and direction at 30-minute intervals between 1979 and 2015 at five synoptic offshore stations in the Gulf of Guinea. The seasonal variations in hourly surface wind speed and directions as well as the Weibull distribution of wind speed and wind power at 100 m hub height were examined. The wind shears, capacity factors, and accumulated energy outputs for seven offshore wind turbine types were determined for the selected locations. In addition, the economic analysis of the selected offshore turbines using levelized cost of energy was carried out, while sensitivity analysis of the total levelized cost of energy to key input parameters was further determined. The results revealed large spatial and temporal variations in wind speed and wind power in the Gulf of Guinea. The most viable offshore site for wind energy exploitation was Agbami (the deepest offshore site), while Bonny (the shallow coastal site) had the least. The findings established very good fits (having mean bias (between −0.08 ms−1 and −2.44 ms−1), percentage bias (between −0.47% and −13.98%), correlation coefficients (between 0.97 and 0.98), Chi-square (between 0.2 and 1.2), and root mean square error (between 1.2 ms−1 and 3.1 ms−1)) between Weibull distribution and the actual wind data. The wind turbines with the highest and the lowest wind power densities, capacity factors, and power outputs across the seasons and sites were V236-15.0 MW and Siemens SWT113, respectively. The levelized cost of energy was considered for the deep waters due to the moderately high-capacity factors. The highest values ranged between 101.48 and 137.12 USD/MWh at Sea Eagle with V236-15 MW and V117-4.2 MW, respectively, while the lowest ranged from 52.29 to 69.66 USD/MWh at Agbami with V236-15 MW and Siemens (SWT113), respectively. The exploitation of Nigeria’s offshore wind resources could also be dedicated to producing renewable hydrogen and can serve to meet the country’s ambitious targets set for carbon neutrality by 2060.

Approaches Used In Adapting Metaheuristic Optimization Algorithms Developed For Continuous Problems to Discrete Problems

Many real-world problems such as determining the type and number of wind turbines, facility placement problems, job scheduling problems, are in the category of combinatorial optimization problems in terms of the type of decision variables. However, since many of the evolutionary optimization algorithms are developed for solving continuous optimization problems, they cannot be directly applied to optimization problems with discrete decision variables. Therefore, the continuous decision variable values generated by these metaheuristics need to be converted to binary values using some techniques. In other words, to apply such algorithms to discrete optimization problems, it is necessary to adapt the candidate solution vectors of the algorithms to discrete values and make changes in their working structures. In this study, firstly, adaptation methods that are frequently used in previous studies in transforming metaheuristic optimization algorithms designed for the solution of continuous optimization problems into discrete optimization algorithms are explained. Then, the popular location update strategies used in solving discrete optimization problems are explained. The presented work summarizes the process of adapting continuous optimization algorithms to solve combinatorial problems step by step.

Wind farm repowering optimization: a techno‐economic‐aesthetic approach

AbstractWhen a wind farm (WF) approaches the end of its life cycle, repowering is another opportunity for wind energy to prove its value. This paper proposes an optimization framework to guide the WF repowering, considering the power generation, the economic cost, and the aesthetic of the WF when various types of new wind turbines (WTs) are added. When calculating the wake deficits inside the WF, a three‐dimensional (3‐D) Gaussian wake model is applied which considers the height differences among the new WTs. A harmony pattern metric is used to assess the visual impact of the rebuilt WF. This optimization problem is formulated as an integer programming (IP) problem and is tackled by the integer particle swarm optimization (IPSO) algorithm. The wind data used for this optimization procedure is predicted by the auto‐regressive (AR) model. The case study on the OWEZ WF verifies the effectiveness of the proposed method. It is also validated that the application of predicted wind data is better than the historical data for WF repowering optimization.

Assessment of Wind Speed Statistics in Samaria Region and Potential Energy Production

Statistical characteristics of the wind speed in the Samaria region of Israel have been analyzed by processing 11 years of wind data provided by the Israeli Meteorological Service, recorded at a 10 m height above the ground. The cumulative mean wind speed at a measurement height was shown to be 4.53 m/s with a standard deviation of 2.32 m/s. The prevailing wind direction was shown to be characterized by a cumulative mean azimuth of 226° with a standard deviation of 79.76°. The results were extrapolated to a 70 m height in order to estimate wind characteristics at the hub height of a medium-scale wind turbine. Moreover, Weibull distribution parameters were calculated annually, monthly, and seasonally, demonstrating a good match with histogram-based statistical representations. The shape parameter of the Weibull distribution was shown to reside within a narrow range of 1.93 to 2.15, allowing us to assume a Rayleigh distribution, thus simplifying wind turbine energy yield calculations. The novelty of the current paper is related to gathering wind statistics for a certain area (Samaria), and we are not aware of any published statistics regarding wind velocity and direction in this area. These data may be interesting for potential regional wind energy development in which the obtained Weibull distribution could be used in calculations for the expected power generation of particular turbines with a known power dependence on velocity. We have given an example of these calculations for three different types of turbines and obtained their yield in terms of electric power and economic value. We also point out that the fact that realistic wind velocity statistics can be described well by an analytic formula (Weibull distribution) is not trivial, and in fact, the fit may have been poor.

Cause analysis of doubly-fed wind turbine faults

Doubly-fed wind turbine is the mainstream model at present. This paper first introduces the working principle of a doubly-fed wind power generation system, counts the fault frequency and fault duration of a doubly-fed wind turbine, and makes an in-depth analysis of the fault data in the recent two years. Then it summarizes the fault components and fault types of doubly-fed wind turbines, and on this basis, analyzes the causes of the faults, which can be used as a reference for wind farms to formulate fault prevention schemes for doubly-fed wind turbines.

A LIF Study of the Temperature Field behind the Pilot Flame Front of a Gas Turbine Type Model Combustor

A LIF Study of the Temperature Field behind the Pilot Flame Front of a Gas Turbine Type Model Combustor

A RANS-based surrogate model for simulating wind turbine interaction

A new wake surrogate model based on Reynolds-averaged Navier-Stokes (RANS) single rotor simulations is presented. The model relies on a series of three-dimensional pre-calculated deficit and added turbulence intensity flow fields, stored in a look-up table (LUT) as a function of the thrust coefficient and the ambient turbulence intensity. For any combination of these parameters, the flow around a wind turbine can be predicted by linearly interpolating within the look-up table. Furthermore, the resulting three-dimensional flow fields from different turbine sources can be superposed linearly to calculate the total wind farm flow. The model is implemented in PyWake and benchmarked against other, commonly employed engineering wake models, namely the Gaussian-Bastankhah, the N. O. Jensen and the Zong models, where RANS wind farm simulations are used as reference. In both full and partial wake cases, the surrogate model achieves a higher accuracy than any other model. Besides providing an accuracy comparable to a full RANS solution, the model can compute a flow case in the order of 1 s on a single processor. The main disadvantage is that the generation of the look-up table is time consuming, computationally expensive and can be memory demanding (especially if more inputs, such as the yaw misalignment angle, stability, etc. are added). Nevertheless, generating the LUT only has to be done once per wind turbine type.

Calculating energy capacity and capacity factors of wind energy generating in the Special Region of Yogyakarta using RETScreen Expert with variations in height

This study aims to get simulation results for the energy capacity and capacity factor of wind turbine type Wind Energy Solutions 80 kW in the Special Region of Yogyakarta at heights of 30m, 60m, and 90m. Energy capacity and capacity factors were calculated using the RETScreen Expert software simulation, beginning with gathering monthly wind speed data from the Special Region of Yogyakarta for use in simulations with height and wind turbine variations. This study indicates that wind turbine type Wind Energy Solutions 80 kW is best suited for installation in the Special Region of Yogyakarta with annual energy capacities of 42.6 MWh, 54.1 MWh, and 63.1 MWh, for heights of 30m, 60m, and 90m, respectively. Meanwhile, the capacity factors for 30m, 60m, and 90m are 6.1%, 7.7%, and 9.0%, respectively. Based on these results, it can be concluded that the wind turbine type Wind Energy Solutions 80 kW is a viable future alternative for the planned construction of a power plant in the Special Region of Yogyakarta. With the wind speed and a power capacity that is not excessively high, it is suitable as a wind turbine type for the Special Region of Yogyakarta.

New Software for the Techno–Economic Analysis of Small Hydro Power Plants

Project SMART (Strategies to Promote Small-Scale Hydro Electricity Production in Europe) from the Intelligent Energy Europe (IEE) program, in which 7 institutions from 5 European states participate, pointed to the important barriers for the expansion of small hydro power plants (SHP) in Europe. One of the main barriers is the lack of suitable methodology and software able to create a clear view of the SHP potential in the given territory, as well as a complete techno-economic analysis for certain locations. Worldwide, there are a certain number of software for this purpose, and will be presented in this paper. However, in practical application for concrete cases, they show certain disadvantages. For example, one software is not able to take into account all the specifics of watercourses and plants; another does not have the option of selecting all types of turbines; in others, the calculation models are based on a limited number of equations that do not describe all possible cases; in some, economic analysis is oversimplified, etc. The aim of this paper is to develop software that is more comprehensive than any existing software. A new software for the techno-economic analysis of SHP is developed using Python and will be presented in this paper. The software is very useful for experts in the field of SHP, but also much wider, for decision-makers, potential investors, and stakeholders, especially in developing countries. It will improve water resources management, disseminate opportunities to investors, and increase the interest of stakeholders to invest in SHP, resulting in their wider use. The software is tested on location for SHP in the Republic of Croatia by comparison with the results obtained by the usual classical calculation. The agreement of the results is satisfactory.

Multivariate ensembles-based hierarchical linkage strategy for system reliability evaluation of aeroengine cooling blades

To improve the computing accuracy and efficiency of system reliability evaluation for aeroengine cooling blades, by fusing the benefits of multivariate ensembles model (ME) into the hierarchical linkage technique (HL), a multivariate ensembles-based hierarchical linkage strategy (ME-HL) is proposed. In the ME-HL modeling, the complex evaluation system is first decomposed into multiple subsystems (i.e., frail site and failure mode) by the developed HL strategy, after that the multiple output responses of subsystems are synchronously mapped by proposing the ME model, and the multi-level system reliability framework is finally built with the Copula-based correlation quantification. The reliability evaluation of a typical aeroengine turbine cooling blade is regarded as a case, to verify the effectiveness of the proposed strategy. From the reliability evaluation results, we observe that the acquired reliability degree considering the failure correlation (i.e., 0.9547) is higher than that of without considering the failure correlation (i.e., 0.9356). From the methods comparison, we discover that the computing efficiency of the ME-HL method is 86.89%, 80.12%, 80.63%, and 18.85% greater than that of the ANN, BT, ME, and BT-HL methods, respectively; and the computing efficiency of the ME-HL method is 12.47%, 11.26%, 6.11%, and 0.93% higher than that of the ANN, BT, ME, and BT-HL methods, respectively. The evaluation and comparison results demonstrate that the proposed ME-HL holds significant advantages in computing accuracy and efficiency in system reliability evaluation problems. The current study can shed a light on the complex multi-level structural system reliability evaluation.

Aerodynamic analysis of a 2-stage elliptical-bladed Savonius wind rotor: Numerical simulation and experimental validation

ABSTRACT The Savonius rotor is a type of vertical-axis wind turbine that utilizes drag to generate power. Its design simplicity and ability to self-start at low wind speeds make it an attractive option for small-scale wind energy generation. Previous research has focused on optimizing the geometric configuration of a 1-stage, 2-bladed rotor with semicircular blade profiles to improve its performance and mitigate negative torque. However, limited research has been conducted on 2-stage, 2-bladed Savonius rotors with elliptical blade profiles. The present investigation aims to fill this research gap by evaluating the aerodynamic characteristics of a 2-stage, 2-bladed Savonius rotor with elliptical profiles. 3D unsteady simulations using the SST k-ω turbulence model in ANSYS Fluent are conducted to evaluate the performance coefficients over a range of tip speed ratios. The numerical results are validated through wind tunnel experiments. The results show that the CPmax (maximum power coefficient) for the 1-stage rotor is 0.19 and 0.12 for numerical and experimental analyses, respectively. For the 2-stage rotor, the CPmax is 0.21 and 0.17 for numerical and experimental analyses, respectively. These findings suggest that the 2-stage rotor with elliptical profiles has the potential to efficiently harness wind energy.

A CNN encoder decoder LSTM model for sustainable wind power predictive analytics

Wind Power (WP) proliferates as one of the significant sustainable energies available in the form of temporal intervals. WP exists as a natural energy generating resource that depends on climatic parameters like wind speed, wind direction. These parameters are highly undetermined, thus collected over a fixed time interval. Hence, the nature of WP is quite uncertain over the time sequences and intricate to forecast. Accurate WP prediction has high significance for wind plants. It sequences the planning for different types of wind farm layouts, number and type of wind turbines required, peak load estimation, distribution areas and plans, cost and benefit risk mitigation etc. Long Short-Term Memory (LSTM) involves prediction using temporal relationships of data points collected over a period of time. The type of predictive analytics required in WP forecasting is very similar to the working of LSTM model. A hybrid Deep Learning (DL) based model using encoder decoder framework with Convolution Neural network (CNN) encoder with LSTM as decoder, named CNN Encoder-Decoder LSTM (CNN-ED-LSTM) is proposed in this study. Hence, to enhance the existing WP predictive analytics (WPPA), this paper focuses on optimization of WPPA using LSTM as the decoder and one-dimensional CNN as encoder. Evaluation of the proposed model is done using five different performance evaluation metrics Mean Absolute Error (MAE), Mean Squared Error (MSE), Mean Absolute Percentage Error (MAPE), Root Mean Squared Error (RMSE) and computational time. Also, other traditional DL models such as VanillaLSTM, StackedLSTM, CNN-LSTM and Bidirectional-LSTM (Bi-LSTM) are assessed over the same metrics. Additionally, five statistical models namely, ARIMA-LSTM, AutoARIMA, AutoRegressive Integrated Mean Average (ARIMA), AutoRegressive Mean Average (ARMA) and AutoRegressive (AR), are evaluated. Then, the efficacy of CNN-ED-LSTM is compared with these conventional models of time series predictive analytics as well as traditional DL models that are already used in different studies for WPPA. The proposed CNN-ED-LSTM model for WPPA is performing better than traditional DL models from 3.2% to 9.6% in terms of MSE metric and 3.2–7.5% for RMSE metric.

Fatigue Life Convergence of Offshore Wind Turbine Support Structure According to Wind Measurement Period

This paper investigated the fatigue life of offshore wind turbine (OWT) support structures. For this purpose, a 3 MW-capacity typical wind turbine is investigated using time-domain finite element simulations. In numerical simulations, different stochastic wind models corresponding to different accumulation periods are applied. Then, the stress-based fatigue life is estimated following the rain-flow counting algorithm and Palmgren-Miner linearly cumulative damage rule. The study also addresses the joint distribution of loads at the site of interest. Generally, the study emphasizes the significance of the long-term distribution of the applied environment loads and its influence on the fatigue life of OWT’s substructures. The results imply that the wind measurement period is directly linked to the fatigue life of offshore wind turbine support structures. Accordingly, its fatigue life is significantly reduced at the 25-year accumulative period of wind. Therefore, this study recommends that a sufficient number of accumulative periods of wind and other environmental loads should be considered appropriately.

Uncertainty analysis of measured geometric variations in turbine blades and impact on aerodynamic performance

Inevitable geometric variations significantly affect the performance of turbines or even that of entire engines; thus, it is necessary to determine their actual characteristics and accurately estimate their impact on performance. In this study, based on 1781 measured profiles of a typical turbine blade, the statistical characteristics of the geometric variations and the uncertainty impact are analyzed, and some commonly used uncertainty modelling methods based on Principal-Component Analysis (PCA) are verified. The geometric variations are found to be evident, asymmetric, and non-uniform, and the non-normality of the random distributions is non-negligible. The performance is notably affected, which is manifested as an overall offset, a notable scattering, and significant deterioration in several extreme cases. Additionally, it is demonstrated that the PCA reconstruction model is effective in characterizing major uncertainty characteristics of the geometric variations and their impact on the performance with almost the first 10 PCA modes. Based on a reasonable profile error and mean geometric deviation, the Gaussian assumption and stochastic-process-based model are also found to be effective in predicting the mean values and standard deviations of the performance variations. However, they fail to predict the probability of some extreme cases with high loss. Finally, a Chi-square-based correction model is proposed to compensate for this deficiency. The present work can provide a useful reference for uncertainty analysis of the impact of geometric variations, and the corresponding uncertainty design of turbine blades.

A comparison study of power performance and extreme load effects of large 10‐MW offshore wind turbines

AbstractThe utilisation of offshore wind turbines has rapidly increased in the last decade, which has resulted in a steady increase in wind turbine sizes. The global average offshore wind turbine size has increased from 1.5 MW to 6 MW in the last two decades. The research community has started to investigate huge 10 to 15 MW offshore wind turbines in recent years, resulting in the study of very innovative floating wind turbines using various substructure technologies. With this backdrop, this paper will investigate and thoroughly compare the power performance of extreme load effects of a large offshore 10 MW turbine installed on the monopile, spar, and semisubmersible substructures. This is performed by using the average conditional exceedance rate (ACER) and Gumbel methods to predict the extreme responses under the operating conditions of 8, 12, and 16 m/s mean wind speed, representing the below‐rated, rated, and above‐rated regions, respectively. The results show that the power performance and extreme loads experienced depends significantly on the operating regions. The mean power generation between the three different types of offshore wind turbines (OWTs) are closely in the whole operating range, which standard deviations differ significantly. Large standard deviations of power generation appear in the spar turbine under the below‐rated condition. Further, it was observed that the spar wind turbine generally experiences larger extreme loads due to larger platform pitch motion. In addition, the ACER method shows a better prediction for the 1, 2 and 5‐year extreme responses than the Gumbel method, which is due to the relatively poor data fitting of the Gumbel method at the upper tail. The study is believed to consolidate and close the knowledge gap in understanding wind turbine responses across the most common offshore substructure technologies and provide a basis for design and deployment of OWTs.