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Assessing Short-Term Voltage Stability of Electric Power Systems by a Hierarchical Intelligent System.
- Source :
-
IEEE Transactions on Neural Networks & Learning Systems . Aug2016, Vol. 27 Issue 8, p1686-1696. 11p. - Publication Year :
- 2016
-
Abstract
- In the smart grid paradigm, growing integration of large-scale intermittent renewable energies has introduced significant uncertainties to the operations of an electric power system. This makes real-time dynamic security assessment (DSA) a necessity to enable enhanced situational-awareness against the risk of blackouts. Conventional DSA methods are mainly based on the time-domain simulation, which are insufficiently fast and knowledge-poor. In recent years, the intelligent system (IS) strategy has been identified as a promising approach to facilitate real-time DSA. While previous works mainly concentrate on the rotor angle stability, this paper focuses on another yet increasingly important dynamic insecurity phenomenon—the short-term voltage instability, which involves fast and complex load dynamics. The problem is modeled as a classification subproblem for transient voltage collapse and a prediction subproblem for unacceptable dynamic voltage deviation. A hierarchical IS is developed to address the two subproblems sequentially. The IS is based on ensemble learning of random-weights neural networks and is implemented in an offline training, a real-time application, and an online updating pattern. The simulation results on the New England 39-bus system verify its superiority in both learning speed and accuracy over some state-of-the-art learning algorithms. [ABSTRACT FROM PUBLISHER]
Details
- Language :
- English
- ISSN :
- 2162237X
- Volume :
- 27
- Issue :
- 8
- Database :
- Academic Search Index
- Journal :
- IEEE Transactions on Neural Networks & Learning Systems
- Publication Type :
- Periodical
- Accession number :
- 116872474
- Full Text :
- https://doi.org/10.1109/TNNLS.2015.2441706