scholarly journals Implementation of Variable Blade Inertia in OpenFAST to Integrate a Flywheel System in the Rotor of a Wind Turbine

Energies ◽  
2021 ◽  
Vol 14 (10) ◽  
pp. 2783
Author(s):  
Laurence Alhrshy
Keyword(s):  
Wind Turbine ◽  
Power Production ◽  
Turbine Rotor ◽  
Rotor Blades ◽  
Simulation Tool ◽  
Ice Accretion ◽  
Simulation Tools ◽  
Load Simulation ◽  
Rotor Mass ◽  
Wind Turbine Rotor

In this paper, the integration of the dynamic behavior of the flywheel system into the load simulation tool OpenFAST is presented. The flywheel system enables a wind turbine to vary the inertia of its rotor blades to control the power production and, most importantly, to affect the vibratory behavior of wind turbine components. Consequently, in order to simulate the behavior of a wind turbine with a flywheel system in its rotor, the variable blade characteristics need to be considered in the load simulation tool. Currently, computer-aided engineering tools for simulating the mechanical loads of wind turbines are not designed to simulate variable blade inertia. Hence, the goal of this paper is to explain how variable inertias of rotor blades are implanted in such load simulation tools as OpenFAST. OpenFAST is used because of it is free, publicly available, and well documentation. Moreover, OpenFAST is open source, which allows modifications in its source code. This add-on in the load simulation is applied to correct rotor mass imbalance. It can also be applied in many cases related to the change in the inertia of wind turbine rotor blades during its operation as, for example, atmospheric ice accretion on the blades, smart blades, etc.

Harmonization of new wind turbine rotor blades development process: A review

2014 ◽  
Vol 39 ◽  
pp. 874-882 ◽  
Author(s):  
B. Rašuo ◽  
M. Dinulović ◽  
A. Veg ◽  
A. Grbović ◽  
A. Bengin
Keyword(s):  
Wind Turbine ◽  
Development Process ◽  
Turbine Rotor ◽  
Rotor Blades ◽  
Wind Turbine Rotor

Monitoring system of wind turbine rotor blades

2009 ◽  
Author(s):  
B. Frankenstein ◽  
L. Schubert ◽  
N. Meyendorf ◽  
H. Friedmann ◽  
C. Ebert
Keyword(s):  
Wind Turbine ◽  
Monitoring System ◽  
Turbine Rotor ◽  
Rotor Blades ◽  
Wind Turbine Rotor

Powder binders used for the manufacturing of wind turbine rotor blades. Part 1. Characterization of resin-binder interaction and preform properties

2016 ◽  
Vol 39 (3) ◽  
pp. 708-717 ◽  
Author(s):  
Stefan Schmidt ◽  
Thorsten Mahrholz ◽  
Alexandra Kühn ◽  
Peter Wierach
Keyword(s):  
Wind Turbine ◽  
Turbine Rotor ◽  
Rotor Blades ◽  
Resin Binder ◽  
Wind Turbine Rotor

Design of a fibrous composite preform for wind turbine rotor blades

2014 ◽  
Vol 56 ◽  
pp. 635-641 ◽  
Author(s):  
J. Zangenberg ◽  
P. Brøndsted ◽  
M. Koefoed
Keyword(s):  
Wind Turbine ◽  
Fibrous Composite ◽  
Turbine Rotor ◽  
Rotor Blades ◽  
Wind Turbine Rotor

scholarly journals FPGA-Based Optical Surface Inspection of Wind Turbine Rotor Blades Using Quantized Neural Networks

Electronics ◽  
2020 ◽  
Vol 9 (11) ◽  
pp. 1824 ◽  
Author(s):  
Lino Antoni Giefer ◽  
Benjamin Staar ◽  
Michael Freitag
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Wind Turbine ◽  
Defect Detection ◽  
Detection System ◽  
Turbine Rotor ◽  
Rotor Blades ◽  
Surface Inspection ◽  
Wind Turbine Rotor

Quantization of the weights and activations of a neural network is a way to drastically reduce necessary memory accesses and to replace arithmetic operations with bit-wise operations. This is especially beneficial for the implementation on field-programmable gate array (FPGA) technology that is particularly suitable for embedded systems due to its low power consumption. In this paper, we propose an in-situ defect detection system utilizing a quantized neural network implemented on an FPGA for an automated surface inspection of wind turbine rotor blades using unpiloted aerial vehicles (UAVs). Contrary to the usual approach of offline defect detection, our approach prevents major downtimes and hence expenses. To our best knowledge, our work is among the first to transfer neural networks with weight and activation quantization into a tangible application. We achieve promising results with our network trained on our dataset consisting of 8024 good and defected rotor blade patches. Compared to a conventional network using floating-point arithmetic, we show that the classification accuracy we achieve is only slightly reduced by approximately 0.6%. With this work, we present a basic system for in-situ defect detection with versatile usability.

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