Open AccessArtificial Neural Network Active Power Filter with Immunity in Distributed Generation
Author(s) -
Mohamed Kadem,
Abdelhafid Semmah,
Patrice Wira,
Abdelkader Slimane
Publication year - 2020
Publication title -
periodica polytechnica. mechanical engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.259
H-Index - 16
eISSN - 1587-379X
pISSN - 0324-6051
DOI - 10.3311/ppme.12775
Subject(s) - harmonics , distributed generation , computer science , ac power , electronic engineering , artificial neural network , nonlinear system , phase locked loop , smart grid , renewable energy , voltage , electrical engineering , engineering , artificial intelligence , physics , quantum mechanics , jitter
With an electrical grid shifting toward Distributed Generation (DG), the emerging use of renewable energy resources is continuously creating challenges to maintain an acceptable electrical power quality thought-out the grid; Therefore, in an energy market where loads are becoming more and more sensitive in a distributed generation filled with polluting nonlinear loads, power quality improvement devices such Active Power Filters (APFs) have to evolve to meet the new standards, since theirs conventional control strategies can't properly operate when multiple power quality problems happens at once, even the one using AI based control as it will be proven in this paper. In this paper a neural network based Active Power Filter will be tested in a DG environment where both current and voltage harmonics, along with fast frequency variation occurs, we will see how the PLL can downgrade its performances enormously under such hostile conditions, We propose to solve this problem by replacing the conventional PLL with a nonlinear least square (NLS) frequency estimator, this novel NLS-ADALINE SAPF is immune in high DG penetration environment, as it will be tested and validated experimentally on an Opal-RT OP5600 FPGA based real-time simulator.