AI-Based Technique to Enhance Transient Response and Resiliency of Power Electronic Dominated Grids via Grid-Following Inverters

This article presents a frequency restoration method to enhance power electronic dominated grid (PEDG) resiliency and transient response via redefining grid following inverters (GFLIs) role at the grid-edge. An artificial intelligence-based power reference correction (AI-PRC) module is developed for...

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Vydáno v:IEEE transactions on industrial electronics (1982) Ročník 71; číslo 3; s. 2614 - 2625
Hlavní autoři: Hosseinzadehtaher, Mohsen, Zare, Alireza, Khan, Ahmad, Umar, Muhammad F., D'silva, Silvanus, Shadmand, Mohammad B.
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York IEEE 01.03.2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Algorithms
Artificial intelligence
Artificial neural network (ANN)
Bayesian regularization algorithm (BRA)
Case studies
Circuit stability
Disturbances
Dynamic response
Dynamic stability
Frequency control
Inverters
Neural networks
power electronic dominated grid (PEDG)
Power system stability
Regularization
Resilience
resiliency
Restoration
Thermal stability
Transient analysis
Transient response
transient stability
ISSN:0278-0046, 1557-9948
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Shrnutí:This article presents a frequency restoration method to enhance power electronic dominated grid (PEDG) resiliency and transient response via redefining grid following inverters (GFLIs) role at the grid-edge. An artificial intelligence-based power reference correction (AI-PRC) module is developed for GFLIs to autonomously adjust their power setpoints during transient disturbances. A detailed analytical validation is provided that shows control rules in PEDG intrinsically follow the underlying dynamic of the swing-based machines to extend its stability boundary. Considering this fact, comprehensive transient and steady state-based mathematical models are used for constructing the learning database of the proposed AI-PRC. The proposed training approach can deal with grid's characteristics alterations and uncertainties. Thus, this approach incorporates PEDG's effective variables that shapes its dynamic response during transient disturbances. Subsequently, a neural network is trained by Bayesian regularization algorithm to realize the proposed AI-PRC scheme for frequency support via GFLIs. Several simulation and experimental case studies results validate the functionality of the proposed AI-PRC toward enhancing the PEDG's transient response and resiliency via GFLIs. The provided case studies demonstrate significant improvement in frequency restoration in response to transient disturbances.
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ISSN:0278-0046
1557-9948
DOI:10.1109/TIE.2023.3265067