scholarly journals Performance of a wind turbine blade in sandstorms using a CFD-BEM based neural network

2020 ◽  
Vol 12 (5) ◽  
pp. 053310
Author(s):  
Iham F. Zidane ◽  
Greg Swadener ◽  
Xianghong Ma ◽  
Mohamed F. Shehadeh ◽  
Mahmoud H. Salem ◽  
...  
Keyword(s):  
Neural Network ◽  
Wind Turbine ◽  
Turbine Blade ◽  
Wind Turbine Blade

scholarly journals PSO-BP Neural Network-Based Strain Prediction of Wind Turbine Blades

Materials ◽  
2019 ◽  
Vol 12 (12) ◽  
pp. 1889 ◽  
Author(s):  
Xin Liu ◽  
Zheng Liu ◽  
Zhongwei Liang ◽  
Shun-Peng Zhu ◽  
José A. F. O. Correia ◽  
...  
Keyword(s):  
Neural Network ◽  
Wind Turbine ◽  
Turbine Blade ◽  
Turbine Blades ◽  
Full Scale ◽  
Wind Turbine Blade ◽  
Blade Design ◽  
Wind Turbine Blades ◽  
Static Testing ◽  
Bpnn Model

The full-scale static testing of wind turbine blades is an effective means to verify the accuracy and rationality of the blade design, and it is an indispensable part in the blade certification process. In the full-scale static experiments, the strain of the wind turbine blade is related to the applied loads, loading positions, stiffness, deflection, and other factors. At present, researches focus on the analysis of blade failure causes, blade load-bearing capacity, and parameter measurement methods in addition to the correlation analysis between the strain and the applied loads primarily. However, they neglect the loading positions and blade displacements. The correlation among the strain and applied loads, loading positions, displacements, etc. is nonlinear; besides that, the number of design variables is numerous, and thus the calculation and prediction of the blade strain are quite complicated and difficult using traditional numerical methods. Moreover, in full-scale static testing, the number of measuring points and strain gauges are limited, so the test data have insufficient significance to the calibration of the blade design. This paper has performed a study on the new strain prediction method by introducing intelligent algorithms. Back propagation neural network (BPNN) improved by Particle Swarm Optimization (PSO) has significant advantages in dealing with non-linear fitting and multi-input parameters. Models based on BPNN improved by PSO (PSO-BPNN) have better robustness and accuracy. Based on the advantages of the neural network in dealing with complex problems, a strain-predictive PSO-BPNN model for full-scale static experiment of a certain wind turbine blade was established. In addition, the strain values for the unmeasured points were predicted. The accuracy of the PSO-BPNN prediction model was verified by comparing with the BPNN model and the simulation test. Both the applicability and usability of strain-predictive neural network models were verified by comparing the prediction results with simulation outcomes. The comparison results show that PSO-BPNN can be utilized to predict the strain of unmeasured points of wind turbine blades during static testing, and this provides more data for characteristic structural parameters calculation.

Crack Diagnosis Method of Wind Turbine Blade Based on Convolution Neural Network with 3D Vibration Information Fusion

2020 ◽  
Vol 40 (22) ◽  
pp. 2212004
Author(s):  
郭迎福 Guo Yingfu ◽  
全伟铭 Quan Weiming ◽  
王文韫 Wang Wenyun ◽  
周浩 Zhou Hao ◽  
邹龙洲 Zou Longzhou
Keyword(s):  
Neural Network ◽  
Wind Turbine ◽  
Turbine Blade ◽  
Information Fusion ◽  
Wind Turbine Blade ◽  
Convolution Neural Network ◽  
Diagnosis Method

scholarly journals Aeroservoelastic Pitch Control of Stall-Induced Flap/Lag Flutter of Wind Turbine Blade Section

2015 ◽  
Vol 2015 ◽  
pp. 1-20 ◽  
Author(s):  
Tingrui Liu
Keyword(s):  
Neural Network ◽  
Wind Turbine ◽  
Turbine Blade ◽  
Learning Algorithm ◽  
Wind Turbine Blade ◽  
Pid Controllers ◽  
Flutter Suppression ◽  
Pitch Control ◽  
Blade Section ◽  
Neural Network Pid

The aim of this paper is to analyze aeroelastic stability, especially flutter suppression for aeroelastic instability. Effects of aeroservoelastic pitch control for flutter suppression on wind turbine blade section subjected to combined flap and lag motions are rarely studied. The work is dedicated to solving destructive flapwise and edgewise instability of stall-induced flutter of wind turbine blade by aeroservoelastic pitch control. The aeroelastic governing equations combine a flap/lag structural model and an unsteady nonlinear aerodynamic model. The nonlinear resulting equations are linearized by small perturbation about the equilibrium point. The instability characteristics of stall-induced flap/lag flutter are investigated. Pitch actuator is described by a second-order model. The aeroservoelastic control is analyzed by three types of optimal PID controllers, two types of fuzzy PID controllers, and neural network PID controllers. The fuzzy controllers are developed based on Sugeno model and intuition method with good results achieved. A single neuron PID control strategy with improved Hebb learning algorithm and a radial basic function neural network PID algorithm are applied and performed well in the range of extreme wind speeds.

System modeling and instability control of wind turbine blade based on hydraulic pitch system and radial basic function neural network proportional–integral–derivative controller

2018 ◽  
Vol 232 (10) ◽  
pp. 1412-1428 ◽  
Author(s):  
Tingrui Liu
Keyword(s):  
Neural Network ◽  
Wind Turbine ◽  
Turbine Blade ◽  
System Modeling ◽  
Basic Function ◽  
Wind Turbine Blade ◽  
Proportional Integral Derivative ◽  
Proportional Integral Derivative Controller ◽  
Thin Walled ◽  
Pitch System

System modeling and aeroservoelastic control for divergent instability of stall-induced composite wind turbine blade modeled as thin-walled symmetric layup beam analysis have been investigated based on hydraulic pitch system and radial basic function neural network control. The blade is modeled as single-cell thin-walled beam structure with the circumferentially asymmetric stiffness design, exhibiting flap/lead-lag bending coupling deformation. The stall flutter and aeroservoelastic control of composite blade are investigated based on dynamic stall Beddoes–Leishman aerodynamic model and radial basic function neural network proportional–integral–derivative controller, with pitch actuator performed by hydraulic system. The system motion equations consist of the aeroelastic equations and the six-order pitch equation. The nonlinear aeroelastic responses, including both flap/lead-lag responses and pitch angle responses under different parameters, are solved by Galerkin method and the nonlinear time integration scheme with nonlinear residual analysis. To verify the effectiveness of the control scheme and realize visualized display of large thin-walled blade in the laboratory, experimental platform based on hardware-in-the-loop simulation is built to realize real-time control and virtual simulation. The platform structure consists of PLC hardware, monitoring interface in configuration software, and simulation environment that is connected by the OPC server with PLC system. The platform lays the foundation for vibrational behavior research on visualization of large wind turbine blade under divergent stall situation and verifies the real-time feasibility of the control algorithm proposed.

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