Stabilization of 5G Telecom Converter-Based Deep Type-3 Fuzzy Machine Learning Control for Telecom Applications

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 brief presents an adaptive interval type-3 fuzzy logic system (IT3-FLS) employing deep reinforcement learning (DRL) for the efficient voltage stabilization of 5G-telecom 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.

Automated summary

This study presents an adaptive interval type-3 fuzzy logic system (IT3-FLS) employing deep reinforcement learning (DRL) to achieve voltage stabilization in the 5G-telecom power system (5G-TPS). Hardware-in-the-Loop (HiL) testing on an OPAL-RT platform is conducted to validate the effectiveness of the proposed adaptive IT3-FLS method.