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Generator Out-of-Step Prediction Based on Faster-Than-Real-Time Analysis: Concepts and Applications
This paper introduces a new approach, based on faster-than-real-time (FTRT) analysis, to predict out-of-step (OOS) condition of a turbine-generator (T-G) unit subsequent to a fault scenario. The proposed FTRT analysis is based on 1) solving a state-space model of the T-G electromechanical system and...
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Published in: | IEEE transactions on power systems 2018-07, Vol.33 (4), p.4563-4573 |
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Main Authors: | , , , , |
Format: | Article |
Language: | English |
Subjects: | |
Citations: | Items that this one cites Items that cite this one |
Online Access: | Get full text |
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Summary: | This paper introduces a new approach, based on faster-than-real-time (FTRT) analysis, to predict out-of-step (OOS) condition of a turbine-generator (T-G) unit subsequent to a fault scenario. The proposed FTRT analysis is based on 1) solving a state-space model of the T-G electromechanical system and corresponding controllers without the need for online measurements, and 2) an estimated Thevenin equivalent of the rest of the power system. The measurement-free FTRT analysis is carried out after the Thevenin parameters are estimated subsequent to the fault clearance instant. Equal area criterion is applied on the predicated variables to detect OOS condition. A reliable decision can be made based on this approach, since it does not involve simplifying assumptions contrary to the existing approaches. The FTRT approach accurately considers the impacts of the detailed model of the generator, automatic voltage regulator, prime mover, governor system, and the host power system. This paper provides detailed mathematical formulation for the proposed OOS detection method and verifies its feasibility and accuracy based on single machine and large test systems. The proposed approach is also implemented on an industrial platform and evaluated. The results confirm that the proposed approach can accurately identify OOS conditions, earlier than the rotor angle noticeably increases. |
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ISSN: | 0885-8950 1558-0679 |
DOI: | 10.1109/TPWRS.2017.2778253 |