Data-driven modeling of power system dynamics: Challenges, state of the art, and future work

With the continual deployment of power-electronics-interfaced renewable energy resources, increasing privacy concerns due to deregulation of electricity markets, and the diversification of demand-side activities, traditional knowledge-based power system dynamic modeling methods are faced with unprec...

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Bibliographic Details
Published in:iEnergy (Piscataway, N.J.) N.J.), 2023-09, Vol.2 (3), p.200-221
Main Authors: Huang, Heqing, Lin, Yuzhang, Zhou, Yifan, Zhao, Yue, Zhang, Peng, Fan, Lingling
Format: Article
Language:English
Subjects:
data-driven modeling
machine learning
model construction
parameter identification
power system dynamics
system identification
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Summary:With the continual deployment of power-electronics-interfaced renewable energy resources, increasing privacy concerns due to deregulation of electricity markets, and the diversification of demand-side activities, traditional knowledge-based power system dynamic modeling methods are faced with unprecedented challenges. Data-driven modeling has been increasingly studied in recent years because of its lesser need for prior knowledge, higher capability of handling large-scale systems, and better adaptability to variations of system operating conditions. This paper discusses about the motivations and the generalized process of data-driven modeling, and provides a comprehensive overview of various state-of-the-art techniques and applications. It also comparatively presents the advantages and disadvantages of these methods and provides insight into outstanding challenges and possible research directions for the future.
ISSN:2771-9197
2771-9197
DOI:10.23919/IEN.2023.0023