Investigating the Performance of Deep Reinforcement Learning-Based MPPT Algorithm under Partial Shading Condition-Reference-Cited by-同舟云学术

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Investigating the Performance of Deep Reinforcement Learning-Based MPPT Algorithm under Partial Shading Condition

Published:2023-08-28 Issue: Volume: Page:
ISSN:
Container-title:2023 IEEE Regional Symposium on Micro and Nanoelectronics (RSM)
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Author:

Yew Weng Ho¹,Fat Chau Chien¹,Mahmood Zuhdi Ahmad Wafi¹,Syakirah Wan Abdullah Wan²,Yew Weng Kean³,Amin Nowshad¹

Affiliation:

1. Universiti Tenaga Nasional,Institute of Sustainable Energy (ISE),Kajang,Selangor,Malaysia

2. Tenaga Nasional Berhad,TNB Renewables Sdn. Bhd.,Petaling Jaya,Malaysia

3. Heriot-Watt University,School of Engineering & Physical Sciences,Putrajaya,Malaysia

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10326449/10326731/10326748.pdf?arnumber=10326748

Reference14 articles.

1. An Improved Particle Swarm Optimization (PSO)–Based MPPT for PV With Reduced Steady-State Oscillation

2. Comparison between the Conventional Methods and PSO Based MPPT Algorithm for Photovoltaic Systems;cheng;Int J Electr Robot Electron Commun Eng,2014

3. A deep reinforcement learning-based MPPT control for PV systems under partial shading condition;phan;Sensors (Switzerland),2020

4. Application of PSO method for maximum power point extraction in photovoltaic systems under partial shading conditions

5. Genetic based algorithm for maximum power point tracking (MPPT) for grid-connected PV systems operating under partial shaded conditions;ben smida;Proc 2015 7th Int Conf Model Identify Control ICMIC 2015,2016

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