Data-Driven Dynamic Stability Assessment in Large-Scale Power Grid Based on Deep Transfer Learning

Weijia Wen (), Xiao Ling, Jianxin Sui and Junjie Lin
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Weijia Wen: State Grid Hunan Information & Telecommunication Company, Changsha 410004, China
Xiao Ling: State Grid Hunan Information & Telecommunication Company, Changsha 410004, China
Jianxin Sui: State Grid Hunan Information & Telecommunication Company, Changsha 410004, China
Junjie Lin: School of Electrical Engineering and Automation, Fuzhou University, Fuzhou 350108, China

Energies, 2023, vol. 16, issue 3, 1-15

Abstract: For data-driven dynamic stability assessment (DSA) in modern power grids, DSA models generally have to be learned from scratch when faced with new grids, resulting in high offline computational costs. To tackle this undesirable yet often overlooked problem, this work develops a light-weight framework for DSA-oriented stability knowledge transfer from off-the-shelf test systems to practical power grids. A scale-free system feature learner is proposed to characterize system-wide features of various systems in a unified manner. Given a real-world power grid for DSA, selective stability knowledge transfer is intelligently carried out by comparing system similarities between it and the available test systems. Afterward, DSA model fine-tuning is performed to make the transferred knowledge adapt well to practical DSA contexts. Numerical test results on a realistic system, i.e., the provincial GD Power Grid in China, verify the effectiveness of the proposed framework.

Keywords: autoencoder; deep learning; dynamic stability assessment; time series; transfer learning; transient stability (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
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