Power prediction method applicable to horizontal axis hydrokinetic turbines

2017 ◽  
Author(s):  
Chihaia Rares-Andrei ◽  
Bunea Florentina ◽  
Oprina Gabriela ◽  
El-Leathey Lucia-Andreea
Keyword(s):  
Prediction Method ◽  
Horizontal Axis ◽  
Power Prediction ◽  
Hydrokinetic Turbines

Long-Term Wind Power Prediction Based on Rough Set

2013 ◽  
Vol 329 ◽  
pp. 411-415 ◽  
Author(s):  
Shuang Gao ◽  
Lei Dong ◽  
Xiao Zhong Liao ◽  
Yang Gao
Keyword(s):  
Neural Network ◽  
Wind Power ◽  
Rough Set ◽  
Wind Farm ◽  
Rough Set Theory ◽  
Prediction Method ◽  
Key Factors ◽  
Power Prediction ◽  
Wind Power Prediction

In long-term wind power prediction, dealing with the relevant factors correctly is the key point to improve the prediction accuracy. This paper presents a prediction method with rough set analysis. The key factors that affect the wind power prediction are identified by rough set theory. The chaotic characteristics of wind speed time series are analyzed. The rough set neural network prediction model is built by adding the key factors as the additional inputs to the chaotic neural network model. Data of Fujin wind farm are used for this paper to verify the new method of long-term wind power prediction. The results show that rough set method is a useful tool in long-term prediction of wind power.

A Study on Resistance Performance for Various Trim Conditions and Bulb Shapes on a Container Ship Under Slow Steaming

2015 ◽  
Author(s):  
Hyun-Suk Park ◽  
Dae-Won Seo ◽  
Ki-Min Han ◽  
Dae-Heon Kim ◽  
Tae-Bum Ha
Keyword(s):  
Form Factor ◽  
Prediction Method ◽  
Experimental Result ◽  
Operating Efficiency ◽  
Container Ship ◽  
Power Prediction ◽  
Hull Form ◽  
Slow Steaming ◽  
Bulbous Bow ◽  
Resistance Performance

Hull form had been unavoidably optimized for a single speed condition, normally a contract speed at design draft in the past many years due to various reasons such as limited design period, less advanced data processing capacity of a computer and so on. For this reason, for maximizing present ship’s operating efficiency, additional analysis relevant to resistance performance for slow steaming condition is newly required since the original hull form for this study also was developed about 10 years ago. In this paper, the resistance performances corresponding to various trim conditions are investigated not only for ship’s original contract speed (Fn: 0.255) but for slow speed (Fn: 0.163∼0.183) by slow steaming. Through this study, it can be accomplished to identify the optimum trim condition meeting the objectives of ship operator. Further to the trim optimization, bulbous bow shape renovation was carried out for off design condition (Fn:0.173) and both of CFD results, one is from an original bulbous bow shape, the other is from a reformed bulbous bow shape by us, are compared each other to identify the concrete reason for the improvement of resistance performance. Commercial CFD code of the STAR-CCM+ was utilized to evaluate the ship’s resistance performance on a 6,800 TEU container ship. To validate of the effectiveness of Starccm+, the experimental result of the subject hull form is referred and compared with the result from STAR-CCM+. Form factor prediction method by CFD that is based on extracting form pressure resistance component from difference of two different computational domains is presented. In this study, it is investigated to compare the form factor calculated by CFD with the model test result. This approach allows hull form designer to calculate a form factor corresponding ship’s trim variation by CFD in order to separate total resistance into wave making resistance and viscous resistance for more accurate effective power prediction.

An approximate power prediction method

1982 ◽  
Vol 29 (335) ◽  
pp. 166-170 ◽  
Author(s):  
J. Holtrop ◽  
G.G.J. Mennen
Keyword(s):  
Prediction Method ◽  
Power Prediction ◽  
Approximate Power

A state-of-the-art review on wind power deterministic prediction

2020 ◽  
pp. 0309524X2094120 ◽  
Author(s):  
Zhongda Tian
Keyword(s):  
Wind Power ◽  
Prediction Method ◽  
Scale Model ◽  
Ensemble Prediction ◽  
Prediction Methods ◽  
Power Prediction ◽  
Continuous Growth ◽  
Advantages And Disadvantages ◽  
Wind Power Prediction ◽  
The Impact

With the continuous growth of wind power access capacity, the impact of intermittent and volatile wind power generation on the grid is becoming more and more obvious, so the research of wind power prediction method has been widely concerned. Accurate wind power prediction can provide necessary support for the power grid dispatching, combined operation of generating units, operation, and maintenance of wind farms. According to the existing wind power prediction methods, the wind power prediction methods are systematically classified according to the time scale, model object, and model principle of prediction. The physical methods, statistical methods include single and ensemble prediction methods related to wind power prediction are introduced in detail. The error evaluation indicator of the prediction method is analyzed, and the advantages and disadvantages of each prediction method and its applicable occasions are given. At the same time, in view of the existing problems in the wind power prediction method, the corresponding improvement plan is put forward. Finally, this article points out that the research is needed for wind power prediction in the future.

scholarly journals A Novel Power Curve Prediction Method for Horizontal-Axis Wind Turbines Using Artificial Neural Networks

2021 ◽  
Vol 118 (3) ◽  
pp. 507-516
Author(s):  
Vin Cent Tai ◽  
Yong Chai Tan ◽  
Nor Faiza Abd Rahman ◽  
Chee Ming Chia ◽  
Mirzhakyp Zhakiya ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Wind Turbines ◽  
Prediction Method ◽  
Horizontal Axis ◽  
Power Curve ◽  
Horizontal Axis Wind Turbines ◽  
Artificial Neural

scholarly journals A Wind Power Prediction Method Based on Bayesian Fusion

2017 ◽  
Author(s):  
Jianqi An ◽  
Zhangbing Chen ◽  
Min Wu ◽  
Takao Terano ◽  
Min Ding ◽  
...  
Keyword(s):  
Wind Power ◽  
Prediction Method ◽  
Power Prediction ◽  
Wind Power Prediction ◽  
Bayesian Fusion

scholarly journals Trim Solutions of Multirotor Vehicles using a Fast Performance Prediction Method

2021 ◽  
Author(s):  
Julia D. Tsaltas
Keyword(s):  
Test Data ◽  
Performance Prediction ◽  
Prediction Method ◽  
Flight Test ◽  
Performance Comparison ◽  
Performance Model ◽  
Pitching Moment ◽  
Rotor Speed ◽  
Power Prediction ◽  
Steady Level

A fast multirotor performance prediction method is presented. The method uses an algorithm to determine the flight performance and trim solutions of multirotor vehicles in steady, level flight. The method considers parasitic drag, force trim, fuselage interference, rotor interference, moment trim, and power prediction. In order to validate the method, vehicle lift, drag, and pitching moment predictions are compared to experimental data from NASA Ames for the 3DR Solo, a commercially available vehicle. The performance comparison with wind tunnel data show similar lift, drag and pitching moment trends when using estimated rotor and vehicle geometries. In addition, the predicted rotor speeds, vehicle power, and vehicle pitch are compared to flight test data of the Aeryon SkyRanger. The lead and rear rotor speed results show that the application of moment trim into the performance model provides rotor speed estimates that reflect the differential rotor speeds the flight test. An orientation study is conducted to explore the effects of rotor and fuselage interference velocities on rotor performance and the performance differences of a four-rotor vehicle flying in diamond and square configurations. Finally, a mass offset study is presented to predict the changes in rotor speed distribution of a SkyRanger vehicle when a 100 g mass is added to the support arm, which simulates asymmetry in centre of gravity location. The predicted performance results show overlapping results with flight testing with and without the mass offset at airspeeds below 5 m/s. At higher airspeeds, the rotor speed predictions that are established by moment trim requirements reflect the rotor speed trends shown from flight test data.

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