期刊
AUTOMATICA
卷 55, 期 -, 页码 27-36出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.automatica.2015.02.032
关键词
Fault detection and isolation; Power systems; Machine learning
资金
- Microsoft Research
- EPSRC [EP/I03210X/1, EP/G066477/1]
- Swedish Research Council [2009-4565, 2013-5523]
- Swedish Foundation for Strategic Research through the project ICT-Psi
- EPSRC Centre for Synthetic Biology and Innovation at Imperial College London through the Science and Innovation [EP/G036004/1]
- Engineering and Physical Sciences Research Council [EP/G036004/1, EP/M002187/1, EP/I03210X/1, EP/K020617/1, EP/G066477/1] Funding Source: researchfish
- EPSRC [EP/G066477/1, EP/M002187/1, EP/K020617/1, EP/I03210X/1, EP/G036004/1] Funding Source: UKRI
This paper considers the problem of automatic fault diagnosis for transmission lines in large scale power networks. Since faults in transmission lines threatens stability of the entire power network, fast and reliable fault diagnosis is an important problem in transmission line protection. This work is the first paper exploiting sparse signal recovery for the fault-diagnosis problem in power networks with nonlinear swing-type dynamics. It presents a novel and scalable technique to detect, isolate and identify transmission faults using a relatively small number of observations by exploiting the sparse nature of the faults. Buses in power networks are typically described by second-order nonlinear swing equations. Based on this description, the problem of fault diagnosis for transmission lines is formulated as a compressive sensing or sparse signal recovery problem, which is then solved using a sparse Bayesian formulation. An iterative reweighted l(1)-minimisation algorithm based on the sparse Bayesian learning update is then derived to solve the fault diagnosis problem efficiently. With the proposed framework, a real-time fault monitoring scheme can be built using only measurements of phase angles at the buses. (C) 2015 Published by Elsevier Ltd.
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