Transformative Control Optimization in PMSG-Based Wind Energy Systems: A Deep Reinforcement Learning Approach-Reference-Cited by-同舟云学术

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Transformative Control Optimization in PMSG-Based Wind Energy Systems: A Deep Reinforcement Learning Approach

Published:2023-12-20 Issue: Volume:16 Page:1-6
ISSN:
Container-title:2023 3rd International Conference on Electrical Machines and Drives (ICEMD)
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Author:

Khademhamedani Bagher¹,Izadi Masoud²,Izadi Nahid²,Azarshab Amin³,Najari Ali⁴,Zaker Behnam⁵

Affiliation:

1. Hamedan University of Technology,Department of Electrical Engineering,Hamedan,Iran

2. Hamedan Azad University,Department of Electrical Engineering,Hamedan,Iran

3. Petroleum University of Technology,Instrumentation and Automation Engineering Department

4. Sapienza University of Rome,Department of Electrical Engineering,Roma,ITALY

5. Politecnico di Torino,Department of Automotive Engineering,Torino,ITALY

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10428933/10429068/10429181.pdf?arnumber=10429181

Reference20 articles.

1. Output power quality enhancement of PMSG with fractional order sliding mode control

2. Finite Position Set-Phase Locked Loop for Sensorless Control of Direct-Driven Permanent-Magnet Synchronous Generators

3. Identification and Diagnosis of Dynamic and Static Misalignment in Induction Motor Using Unscented Kalman Filter;Izadi

4. Predictive and Data-Driven Control of Traffic Lights in Urban Road Networks using Linear and Time-Varying Model;Jabari

5. Robust Predictive Control of Three-Level NPC Back-to-Back Power Converter PMSG Wind Turbine Systems With Revised Predictions

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