Ensemble offshore Wind Turbine Power Curve modelling – An integration of Isolation Forest, fast Radial Basis Function Neural Network, and metaheuristic algorithm

Energy ◽  
2021 ◽  
pp. 122340
Author(s):  
Tenghui Li ◽  
Xiaolei Liu ◽  
Zi Lin ◽  
Rory Morrison
Keyword(s):  
Neural Network ◽  
Radial Basis Function ◽  
Wind Turbine ◽  
Basis Function ◽  
Offshore Wind ◽  
Offshore Wind Turbine ◽  
Power Curve ◽  
Metaheuristic Algorithm ◽  
Radial Basis ◽  
Isolation Forest

Wind turbine power curve modeling using radial basis function neural networks and tabu search

2021 ◽  
Vol 163 ◽  
pp. 2137-2152
Author(s):  
Despina Karamichailidou ◽  
Vasiliki Kaloutsa ◽  
Alex Alexandridis
Keyword(s):  
Neural Networks ◽  
Tabu Search ◽  
Radial Basis Function ◽  
Wind Turbine ◽  
Basis Function ◽  
Power Curve ◽  
Radial Basis ◽  
Curve Modeling

scholarly journals Variable Torque Control of Offshore Wind Turbine on Spar Floating Platform Using Advanced RBF Neural Network

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Lei Wang ◽  
Shan Zuo ◽  
Y. D. Song ◽  
Zheng Zhou
Keyword(s):  
Neural Network ◽  
Wind Speed ◽  
Wind Turbine ◽  
Output Power ◽  
Rbf Neural Network ◽  
Torque Control ◽  
Offshore Wind ◽  
Offshore Wind Turbine ◽  
Power Curve ◽  
Floating Platform

Offshore floating wind turbine (OFWT) has been a challenging research spot because of the high-quality wind power and complex load environment. This paper focuses on the research of variable torque control of offshore wind turbine on Spar floating platform. The control objective in below-rated wind speed region is to optimize the output power by tracking the optimal tip-speed ratio and ideal power curve. Aiming at the external disturbances and nonlinear uncertain dynamic systems of OFWT because of the proximity to load centers and strong wave coupling, this paper proposes an advanced radial basis function (RBF) neural network approach for torque control of OFWT system at speeds lower than rated wind speed. The robust RBF neural network weight adaptive rules are acquired based on the Lyapunov stability analysis. The proposed control approach is tested and compared with the NREL baseline controller using the “NREL offshore 5 MW wind turbine” model mounted on a Spar floating platform run on FAST and Matlab/Simulink, operating in the below-rated wind speed condition. The simulation results show a better performance in tracking the optimal output power curve, therefore, completing the maximum wind energy utilization.

scholarly journals Unsupervised Damage Detection for Offshore Jacket Wind Turbine Foundations Based on an Autoencoder Neural Network

Sensors ◽  
2021 ◽  
Vol 21 (10) ◽  
pp. 3333
Author(s):  
Maria del Cisne Feijóo ◽  
Yovana Zambrano ◽  
Yolanda Vidal ◽  
Christian Tutivén
Keyword(s):  
Neural Network ◽  
Wind Turbine ◽  
Environmental Conditions ◽  
Wind Farms ◽  
Offshore Wind ◽  
Offshore Wind Turbine ◽  
Small Subset ◽  
Scaled Model ◽  
Vibration Data ◽  
Large Water

Structural health monitoring for offshore wind turbine foundations is paramount to the further development of offshore fixed wind farms. At present time there are a limited number of foundation designs, the jacket type being the preferred one in large water depths. In this work, a jacket-type foundation damage diagnosis strategy is stated. Normally, most or all the available data are of regular operation, thus methods that focus on the data leading to failures end up using only a small subset of the available data. Furthermore, when there is no historical precedent of a type of fault, those methods cannot be used. In addition, offshore wind turbines work under a wide variety of environmental conditions and regions of operation involving unknown input excitation given by the wind and waves. Taking into account the aforementioned difficulties, the stated strategy in this work is based on an autoencoder neural network model and its contribution is two-fold: (i) the proposed strategy is based only on healthy data, and (ii) it works under different operating and environmental conditions based only on the output vibration data gathered by accelerometer sensors. The proposed strategy has been tested through experimental laboratory tests on a scaled model.

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