Abdulwahid, Ali Hadi (2023) Artificial Intelligence-based Control Techniques for HVDC Systems. Emerging Science Journal, 7 (2). pp. 643-653. ISSN 2610-9182
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Abstract
The electrical energy industry depends, among other things, on the ability of networks to deal with uncertainties from several directions. Smart-grid systems in high-voltage direct current (HVDC) networks, being an application of artificial intelligence (AI), are a reliable way to achieve this goal as they solve complex problems in power system engineering using AI algorithms. Due to their distinctive characteristics, they are usually effective approaches for optimization problems. They have been successfully applied to HVDC systems. This paper presents a number of issues in HVDC transmission systems. It reviews AI applications such as HVDC transmission system controllers and power flow control within DC grids in multi-terminal HVDC systems. Advancements in HVDC systems enable better performance under varying conditions to obtain the optimal dynamic response in practical settings. However, they also pose difficulties in mathematical modeling as they are non-linear and complex. ANN-based controllers have replaced traditional PI controllers in the rectifier of the HVDC link. Moreover, the combination of ANN and fuzzy logic has proven to be a powerful strategy for controlling excessively non-linear loads. Future research can focus on developing AI algorithms for an advanced control scheme for UPFC devices. Also, there is a need for a comprehensive analysis of power fluctuations or steady-state errors that can be eliminated by the quick response of this control scheme. This survey was informed by the need to develop adaptive AI controllers to enhance the performance of HVDC systems based on their promising results in the control of power systems.
Item Type: | Article |
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Subjects: | Library Keep > Multidisciplinary |
Depositing User: | Unnamed user with email support@librarykeep.com |
Date Deposited: | 12 Oct 2023 07:03 |
Last Modified: | 12 Oct 2023 07:03 |
URI: | http://archive.jibiology.com/id/eprint/1425 |