RBF neural network based PI pitch controller for a class of 5-MW wind turbines using particle swarm optimization algorithm

Poultangari, Iman; Shahnazi, Reza; Sheikhan, Mansour

doi:10.1016/j.isatra.2012.06.001

Cited by 116 publications

(89 citation statements)

References 10 publications

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“…In the first stage, the unsupervised method is implemented where the parameter is governed by the radial basis function. In the second stage, the supervised training method is employed to train the weights which are the same as the back propagation algorithm [21]. In this paper, the RBFN controller is implemented to control the WECS based on the wind speed regions.…”

Section: Radial Basis Function Networkmentioning

confidence: 99%

Coordinated Control Strategies for a Permanent Magnet Synchronous Generator Based Wind Energy Conversion System

2017

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Abstract:In this paper, a novel co-ordinated hybrid maximum power point tracking (MPPT)-pitch angle based on a radial basis function network (RBFN) is proposed for a variable speed variable pitch wind turbine. The proposed controller is used to maximise output power when the wind speed is low and optimise the power when the wind speed is high. The proposed controller provides robustness to the nonlinear characteristic of wind speed. It uses wind speed, generator speed, and generator power as input variables and utilises the duty cycle and the reference pitch angle as the output control variables. The duty cycle is used to control the converter so as to maximise the power output and the reference pitch angle is used to control the generator speed in order to control the generator output power in the above rated wind speed region. The effectiveness of the proposed controller was verified using MATLAB/Simulink software.

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Section: Radial Basis Function Networkmentioning

confidence: 99%

Coordinated Control Strategies for a Permanent Magnet Synchronous Generator Based Wind Energy Conversion System

2017

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“…The proposed RBFN controller consists of three layers: an input layer, a hidden layer with nonlinear RBF activation function and a linear output layer [30] as shown in Fig. 8.…”

Section: Radial Basis Function Networkmentioning

confidence: 99%

Comparative Analysis of Pitch Angle Controller Strategies for PMSG Based Wind Energy Conversion System

Tiwari¹,

Babu²

2017

IJISA

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Abstract-This paper proposes an advanced pitch angle control strategy based on neural network (NN) for variable speed wind turbine. The proposed methodology uses Radial Basis Function Network (RBFN) and Feedforward based Back propagation network (BPN) algorithm to generate pitch angle. The performance of the proposed control technique is analyzed by comparing the results with Fuzzy Logic Control (FLC) and Proportional -Integral (PI) control techniques. The control techniques implemented is able to compensate the nonlinear characteristic of wind speed. The wind turbine is smoothly controlled to maintain the generator power and the mechanical torque to the rated value without any fluctuation during rapid variation in wind speed. The effectiveness of the proposed control strategy is verified using MATLAB/Simulink for 2-MW permanent magnet synchronous generator (PMSG) based wind energy conversion system.

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“…The RBFNN introduced in [12,24] is used in self-learning systems composed of large numbers in a simple data set. The algorithm comprises three layers: an input layer, a hidden layer exhibiting a nonlinear activation function and an output layer.…”

Section: Rbfnn Algorithmmentioning

confidence: 99%

“…In [12], a PSO-RBF method was proposed for optimizing PI pitch control systems in MW-class wind turbines. After using PSO to determine the optimal PI gains, the RBF neural network (RBFNN) can be trained to locate the optimal dataset.…”

Section: Introductionmentioning

confidence: 99%

Optimal PID Controller Design Based on PSO-RBFNN for Wind Turbine Systems

2014

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Abstract:A strategy was proposed to determine the optimal operating point for the proportional-integral-derivative (PID) controller of a wind turbine, and identify the stability regions in the parameter space. The proposed approach combined particle swarm optimization (PSO) and radial basis function neural network (RBFNN) algorithms. These intelligent algorithms are artificial learning mechanisms that can determine the optimal operating points, and were used to generate the function representing the most favorable operating p i k k − parameters from each parameter of d k for the stability region of the PID controller. A graphical method was used to determine the 2D or 3D vision boundaries of the PID-type controller space in closed-loop wind turbine systems. The proposed techniques were demonstrated using simulations of a drive train model without time delay and a pitch control model with time delay. Finally, the 3D stability boundaries were determined the proposed graphical approach with and without time delay systems.

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RBF neural network based PI pitch controller for a class of 5-MW wind turbines using particle swarm optimization algorithm

Cited by 116 publications

References 10 publications

Coordinated Control Strategies for a Permanent Magnet Synchronous Generator Based Wind Energy Conversion System

Coordinated Control Strategies for a Permanent Magnet Synchronous Generator Based Wind Energy Conversion System

Comparative Analysis of Pitch Angle Controller Strategies for PMSG Based Wind Energy Conversion System

Optimal PID Controller Design Based on PSO-RBFNN for Wind Turbine Systems

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