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

被引：27

作者：

Gheisarnejad, Meysam ^{[1
]}

Mohammadzadeh, Ardashir ^{[2
]}

Farsizadeh, Hamed ^{[1
]}

Khooban, Mohammad-Hassan ^{[1
]}

机构：

[1] Aarhus Univ, Dept Elect & Comp Engn, DK-8200 Aarhus, Denmark

[2] Univ Bonab, Elect Engn Dept, Bonab 5551761167, Iran

来源：

IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS | 2022年 / 69卷 / 02期

关键词：

Telecommunications; Fuzzy logic; Power system stability; 5G mobile communication; Circuits and systems; Reinforcement learning; Robustness; 5G-telecom power system (5G-TPS); interval fuzzy type-3 fuzzy logic system (IT3-FLS); deep reinforcement learning (DRL); full-bridge (FB) converter;

D O I：

10.1109/TCSII.2021.3102282

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

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.

引用

页码：544 / 548

页数：5

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