Genetic algorithm and decision tree-based oscillatory stability assessment

This paper deals with a new method for eigenvalue region prediction of critical stability modes of power systems based on decision trees. The critical stability modes result from inter-area oscillations in large-scale interconnected power systems. The existing methods for eigenvalue computation are...

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Bibliographic Details
Published in:IEEE transactions on power systems 2006-05, Vol.21 (2), p.746-753
Main Authors: Teeuwsen, S.P., Erlich, I., El-Sharkawi, M.A., Bachmann, U.
Format: Article
Language:English
Subjects:
Assessments
Decision tree (DT)
Decision trees
Eigenvalues
Eigenvalues and eigenfunctions
Europe
feature selection
genetic algorithm (GA)
Genetic algorithms
large power systems
Large-scale systems
Mathematical models
Oscillations
oscillatory stability assessment (OSA)
Power system dynamics
Power system interconnection
Power system modeling
Power system stability
Searching
Stability
System testing
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Description
Summary:This paper deals with a new method for eigenvalue region prediction of critical stability modes of power systems based on decision trees. The critical stability modes result from inter-area oscillations in large-scale interconnected power systems. The existing methods for eigenvalue computation are time-consuming and require the entire system model that includes an extensive number of states. However, using decision trees, the oscillatory stability can be predicted based on a few selected inputs. Decision trees are fast, easy to grow, and provide high accuracy for eigenvalue region prediction. Special emphasis is hereby focused on the selection process for the decision tree inputs. In this paper, a genetic algorithm is implemented to search for the best set of inputs providing the highest performance in stability assessment.
ISSN:0885-8950
1558-0679
DOI:10.1109/TPWRS.2006.873408