scholarly journals Axial-Flux Permanent-Magnet Dual-Rotor Generator for a Counter-Rotating Wind Turbine

Energies ◽  
2020 ◽  
Vol 13 (11) ◽  
pp. 2833
Author(s):  
Filip Kutt ◽  
Krzysztof Blecharz ◽  
Dariusz Karkosiński

Coaxial counter-rotating propellers have been widely applied in ships and helicopters for improving the propulsion efficiency and offsetting system reactive torques. Lately, the counter-rotating concept has been introduced into the wind turbine design. Distributed wind power generation systems often require a novel approach in generator design. In this paper, prototype development of axial-flux generator with a counter-rotating field and armature is presented. The design process was composed of three main steps: analytical calculation, FEM simulation and prototype experimental measurements. The key aspect in the prototype development was the mechanical construction of two rotating components of the generator. Sturdy construction was achieved using two points of contact between both rotors via the placement of the bearing between the inner and outer rotor. The experimental analysis of the prototype generator has been conducted in the laboratory at the dynamometer test stand equipped with a torque sensor. The general premise for the development of such a machine was an investigation into the possibility of developing a dual rotor wind turbine. The proposed solution had to meet certain criteria such as relatively simple construction of the generator and the direct coupling between the generator and the wind turbines. The simple construction and the lack of any gearbox would allow for such a system to be constructed relatively cheaply, which is a key aspect in further system development.

Electronics ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 261
Author(s):  
Tianyang Liu ◽  
Zunkai Huang ◽  
Li Tian ◽  
Yongxin Zhu ◽  
Hui Wang ◽  
...  

The rapid development in wind power comes with new technical challenges. Reliable and accurate wind power forecast is of considerable significance to the electricity system’s daily dispatching and production. Traditional forecast methods usually utilize wind speed and turbine parameters as the model inputs. However, they are not sufficient to account for complex weather variability and the various wind turbine features in the real world. Inspired by the excellent performance of convolutional neural networks (CNN) in computer vision, we propose a novel approach to predicting short-term wind power by converting time series into images and exploit a CNN to analyze them. In our approach, we first propose two transformation methods to map wind speed and precipitation data time series into image matrices. After integrating multi-dimensional information and extracting features, we design a novel CNN framework to forecast 24-h wind turbine power. Our method is implemented on the Keras deep learning platform and tested on 10 sets of 3-year wind turbine data from Hangzhou, China. The superior performance of the proposed method is demonstrated through comparisons using state-of-the-art techniques in wind turbine power forecasting.


2012 ◽  
Vol 55 (3-4) ◽  
pp. 396-404 ◽  
Author(s):  
Tugrul U. Daim ◽  
Elvan Bayraktaroglu ◽  
Judith Estep ◽  
Dong Joon Lim ◽  
Jubin Upadhyay ◽  
...  
Keyword(s):  

2013 ◽  
Vol 7 (2) ◽  
pp. 170-177 ◽  
Author(s):  
Alessio Balleri ◽  
Allann Al‐Armaghany ◽  
Hugh Griffiths ◽  
Kinfai Tong ◽  
Takashi Matsuura ◽  
...  

Author(s):  
Wei Jun Zhu ◽  
Wen Zhong Shen ◽  
Jens Nørkær Sørensen

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