Nonlinear Load Sharing and Voltage Compensation of Microgrids Based on Harmonic Power-Flow Calculations Using Radial Basis Function Neural Networks

2018 ◽  
Vol 12 (3) ◽  
pp. 2749-2759 ◽  
Author(s):  
Hamid Reza Baghaee ◽  
Mojtaba Mirsalim ◽  
Gevork B. Gharehpetan ◽  
Heidar Ali Talebi
Keyword(s):  
Neural Networks ◽  
Radial Basis Function ◽  
Basis Function ◽  
Power Flow ◽  
Load Sharing ◽  
Nonlinear Load ◽  
Voltage Compensation ◽  
Radial Basis ◽  
Harmonic Power

scholarly journals Improving Stability and Convergence for Adaptive Radial Basis Function Neural Networks Algorithm. (On-Line Harmonics Estimation Application)

2017 ◽  
Vol 6 (1) ◽  
pp. 9-17 ◽  
Author(s):  
Eyad K Almaita ◽  
Jumana Al shawawreh
Keyword(s):  
Neural Networks ◽  
Radial Basis Function ◽  
Basis Function ◽  
Estimation Error ◽  
Stability And Convergence ◽  
Stable Convergence ◽  
Nonlinear Load ◽  
Radial Basis ◽  
On Line ◽  
Harmonic Components

In this paper, an adaptive Radial Basis Function Neural Networks (RBFNN) algorithm is used to estimate the fundamental and harmonic components of nonlinear load current. The performance of the adaptive RBFNN is evaluated based on the difference between the original signal and the constructed signal (the summation between fundamental and harmonic components). Also, an extensive investigation is carried out to propose a systematic and optimal selection of the Adaptive RBFNN parameters. These parameters will ensure fast and stable convergence and minimum estimation error. The results show an improving for fundamental and harmonics estimation comparing to the conventional RBFNN. Also, the results show how to control the computational steps and how they are related to the estimation error. The methodology used in this paper facilitates the development and design of signal processing and control systems.Article History: Received Dec 15, 2016; Received in revised form Feb 2nd 2017; Accepted 13rd 2017; Available onlineHow to Cite This Article: Almaita, E.K and Shawawreh J.Al (2017) Improving Stability and Convergence for Adaptive Radial Basis Function Neural Networks Algorithm (On-Line Harmonics Estimation Application).  International Journal of Renewable Energy Develeopment, 6(1), 9-17.http://dx.doi.org/10.14710/ijred.6.1.9-17

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

Modeling and prediction of viscosity of water-based nanofluids by radial basis function neural networks

2015 ◽  
Vol 281 ◽  
pp. 173-183 ◽  
Author(s):  
Ningbo Zhao ◽  
Xueyou Wen ◽  
Jialong Yang ◽  
Shuying Li ◽  
Zhitao Wang
Keyword(s):  
Neural Networks ◽  
Radial Basis Function ◽  
Basis Function ◽  
Modeling And Prediction ◽  
Water Based ◽  
Radial Basis
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