An ANN-based multilevel classification approach using decomposed input space for transient stability assessment

This paper proposes an ANN-based multilevel classification approach for fast transient stability assessment of large power systems. Based on input space decomposition, a two-level classifier incorporating two feed-forward ANNs is built to obtain a stability index for security classification using so...

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Bibliographic Details
Published in:Electric power systems research 1998-09, Vol.46 (3), p.259-266
Main Authors: Tso, S.K., Gu, X.P., Zeng, Q.Y., Lo, K.L.
Format: Article
Language:English
Subjects:
Applied sciences
Artificial neural networks
Back-propagation
Disturbances. Regulation. Protection
Electric, optical and optoelectronic circuits
Electrical engineering. Electrical power engineering
Electrical power engineering
Electronics
Exact sciences and technology
Multilevel classification
Neural networks
Operation. Load control. Reliability
Power networks and lines
Semi-supervised learning
Stability index
Transient stability assessment
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Summary:This paper proposes an ANN-based multilevel classification approach for fast transient stability assessment of large power systems. Based on input space decomposition, a two-level classifier incorporating two feed-forward ANNs is built to obtain a stability index for security classification using some general abstract post-fault attributes as its inputs. The ANNs are trained by a newly developed semi-supervised learning algorithm. The proposed approach can not only distinguish whether a power system is stable or unstable based on the specific post-fault attributes, but also provide a relative stability indicator. The numerical results of applying the approach to the ten-unit New England power system demonstrate its validity for transient stability assessment.
ISSN:0378-7796
1873-2046
DOI:10.1016/S0378-7796(98)00076-5