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

• Published in: IEEE transactions on industrial electronics (1982) Vol. 71; no. 3; pp. 2614 - 2625
• Main Authors: Hosseinzadehtaher, Mohsen, Zare, Alireza, Khan, Ahmad, Umar, Muhammad F., D'silva, Silvanus, Shadmand, Mohammad B.
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.03.2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: 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
• DOI: 10.1109/TIE.2023.3265067

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.