Combined RBFN based MPPT and d-axis stator current control for permanent magnet synchronous generators

This paper proposes a new radial basis function neural network maximum power point tracking controller based on a differential evolution algorithm for machine side converter of permanent magnet synchronous generator wind turbine under variable wind speed. Direct axis stator current control methods of permanent magnet synchronous machine are reviewed shortly. A combined radial basis function neural network-based network maximum power point tracking method and d axis stator current control techniques including zero d axis stator current, unity power factor, and constant stator flux-linkage have been implemented to control the machine side converter of permanent magnet synchronous generator wind turbine. The dynamic performance of the proposed approach is assessed under different operating conditions through a simulation model based on MATLAB. It has been seen that the radial basis function neural network controller can not only track well the maximum power point but also can be reduced costly.

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PHOTOVOLTAIC MAXIMUM POWER POINT PREDICTION USING ROBUST RADIAL BASIS FUNCTION NETWORK

10.1063/1.3223962 ◽

2009 ◽

Author(s):

Chiung-Chou Liao ◽

Abdul Halim Hakim ◽

Pandian Vasant ◽

Nader Barsoum

Keyword(s):

Radial Basis Function ◽

Basis Function ◽

Radial Basis Function Network ◽

Maximum Power ◽

Maximum Power Point ◽

Radial Basis ◽

Power Point ◽

Function Network

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Application of radial basis function neuron networks in an identification and prediction of maximum power point of photovoltaic-array

2011 International Conference on Remote Sensing, Environment and Transportation Engineering ◽

10.1109/rsete.2011.5965658 ◽

2011 ◽

Author(s):

NguyenViet Ngu ◽

Honghua Wang ◽

NguyenXuan Truong

Keyword(s):

Radial Basis Function ◽

Basis Function ◽

Maximum Power ◽

Maximum Power Point ◽

Photovoltaic Array ◽

Neuron Networks ◽

Radial Basis ◽

Power Point

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Maximum Power Point Tracker Based on Fuzzy Adaptive Radial Basis Function Neural Network for PV-System

Energies ◽

10.3390/en12142827 ◽

2019 ◽

Vol 12 (14) ◽

pp. 2827 ◽

Cited By ~ 8

Author(s):

Bouarroudj ◽

Boukhetala ◽

Feliu-Batlle ◽

Boudjema ◽

Benlahbib ◽

...

Keyword(s):

Neural Network ◽

Fuzzy Logic ◽

Radial Basis Function ◽

Basis Function ◽

Maximum Power ◽

Pv System ◽

Maximum Power Point ◽

Pv Module ◽

Mppt Controller ◽

Power Point

In this article, a novel maximum power point tracking (MPPT) controller for a photovoltaic (PV) system is presented. The proposed MPPT controller was designed in order to extract the maximum of power from the PV-module and reduce the oscillations once the maximum power point (MPP) had been achieved. To reach this goal, a combination of fuzzy logic and an adaptive radial basis function neural network (RBF-NN) was used to drive a DC-DC Boost converter which was used to link the PV-module and a resistive load. First, a fuzzy logic system, whose single input was based on the incremental conductance (INC) method, was used for a variable voltage step size searching while reducing the oscillations around the MPP. Second, an RBF-NN controller was developed to keep the PV-module voltage at the optimal voltage generated from the first stage. To ensure a real MPPT in all cases (change of weather conditions and load variation) an adaptive law based on backpropagation algorithm with the gradient descent method was used to tune the weights of RBF-NN in order to minimize a mean-squared-error (MSE) criterion. Finally, through the simulation results, our proposed MPPT method outperforms the classical P and O and INC-adaptive RBF-NN in terms of efficiency.

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