Application of radial basis function networks for solar-array modelling and maximum power-point prediction

2000 ◽  
Vol 147 (5) ◽  
pp. 310 ◽  
Author(s):  
A. Al-Amoudi ◽  
L. Zhang
Keyword(s):  
Radial Basis Function ◽  
Basis Function ◽  
Maximum Power ◽  
Solar Array ◽  
Radial Basis Function Networks ◽  
Maximum Power Point ◽  
Radial Basis ◽  
Power Point

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

2021 ◽  
Vol 12 (4) ◽  
pp. 2459
Author(s):  
Tuan Ngoc Anh Nguyen ◽  
Duy Cong Pham ◽  
Luu Hoang Minh ◽  
Nguyen Huu Chan Thanh
Keyword(s):  
Neural Network ◽  
Radial Basis Function ◽  
Permanent Magnet ◽  
Basis Function ◽  
Current Control ◽  
Maximum Power ◽  
Maximum Power Point ◽  
Stator Current ◽  
Radial Basis ◽  
Power Point

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.

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

Energies ◽  
2019 ◽  
Vol 12 (14) ◽  
pp. 2827 ◽  
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.

Universal Approximation Using Radial-Basis-Function Networks

1991 ◽  
Vol 3 (2) ◽  
pp. 246-257 ◽  
Author(s):  
J. Park ◽  
I. W. Sandberg
Keyword(s):  
Finite Number ◽  
Radial Basis Function ◽  
Basis Function ◽  
Universal Approximation ◽  
Radial Basis Function Networks ◽  
Feedforward Networks ◽  
Rbf Networks ◽  
Smoothing Factor ◽  
Radial Basis ◽  
Hidden Layer

There have been several recent studies concerning feedforward networks and the problem of approximating arbitrary functionals of a finite number of real variables. Some of these studies deal with cases in which the hidden-layer nonlinearity is not a sigmoid. This was motivated by successful applications of feedforward networks with nonsigmoidal hidden-layer units. This paper reports on a related study of radial-basis-function (RBF) networks, and it is proved that RBF networks having one hidden layer are capable of universal approximation. Here the emphasis is on the case of typical RBF networks, and the results show that a certain class of RBF networks with the same smoothing factor in each kernel node is broad enough for universal approximation.

Sign in / Sign up

Export Citation Format

Share Document