Stabilization of 5G telecom Converter-Based Deep Type-3 Fuzzy Machine Learning Control for Telecom Applications, By Ardashir Mohammadzadeh

Research

Title	Stabilization of 5G telecom Converter-Based Deep Type-3 Fuzzy Machine Learning Control for Telecom Applications
Type	Article
Keywords	Telecommunications , Fuzzy logic , Power system stability , 5G mobile communication , Circuits and systems , Reinforcement learning , Robustness
Researchers	Meysam Gheisarnejad، Ardashir Mohammadzadeh، Hamed Farsizadeh، Mohammad-Hassan Khooban

Abstract

For the 5G base transceiver stations (BTSs), the effective stabilization of full-bridge (FB) converters is necessary to supply the connected loads without any interruption. The stability challenges of such technologies are more intensified when the 5G BTS supplies constant power loads (CPL) with negative impedance instabilities. To meet this need, this letter presents an adaptive interval type-3 fuzzy logic system (IT3-FLS) employing deep reinforcement learning (DRL) for the efficient voltage stabilization of 5Gtelecom power system (5G-TPS) supplying CPL. The Hardware-in-the-Loop (HiL) examinations are accomplished using an OPAL-RT platform to test the usefulness of the adaptive IT3-FLS from a systematic perspective.