| Reference : Online Fault Diagnosis for Nonlinear Power Systems |
| Scientific journals : Article | |||
| Engineering, computing & technology : Multidisciplinary, general & others | |||
| http://hdl.handle.net/10993/20226 | |||
| Online Fault Diagnosis for Nonlinear Power Systems | |
| English | |
Pan, Wei  [] | |
| Yuan, Ye [University of Cambridge] | |
| Sandberg, Henrik [] | |
| Goncalves, Jorge [University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > >] | |
| Stan, Guy-Bart [] | |
| May-2015 | |
| Automatica | |
| Pergamon Press - An Imprint of Elsevier Science | |
| 55 | |
| 27-36 | |
| Yes (verified by ORBilu) | |
| 0005-1098 | |
| Oxford | |
| United Kingdom | |
| [en] In this paper, automatic fault diagnosis in large
scale power networks described by second-order nonlinear swing equations is studied. This work focuses on a class of faults that occur in the transmission lines. Transmission line protection is an important issue in power system engineering because a large portion of power system faults is occurring in transmission lines. This paper presents a novel technique to detect, isolate and identify the faults on transmissions using only a small number of observations. We formulate the problem of fault diagnosis of nonlinear power network into a compressive sensing framework and derive an optimisationbased formulation of the fault identification problem. An iterative reweighted `1-minimisation algorithm is finally derived to solve the detection problem efficiently. Under the proposed framework, a real-time fault monitoring scheme can be built using only measurements of phase angles of nonlinear power networks. | |
| Luxembourg Centre for Systems Biomedicine (LCSB): Systems Control (Goncalves Group) | |
| http://hdl.handle.net/10993/20226 |
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