- Title
- Assessing short-term voltage stability of electric power systems by a hierarchical intelligent system
- Creator
- Xu, Yan; Zhang, Rui; Zhao, Junhua; Dong, Zhao Yang; Wang, Dianhui; Yang, Hongming; Wong, Kit Po
- Relation
- IEEE Transactions on Neural Networks and Learning Systems Vol. 27, Issue 8, p. 1686-1696
- Publisher Link
- http://dx.doi.org/10.1109/TNNLS.2015.2441706
- Publisher
- Institute of Electrical and Electronics Engineers (IEEE)
- Resource Type
- journal article
- Date
- 2016
- Description
- 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.
- Subject
- ensemble learning; intelligent system (IS); power system; smart grid; voltage stability
- Identifier
- http://hdl.handle.net/1959.13/1345189
- Identifier
- uon:29584
- Identifier
- ISSN:2162-237X
- Language
- eng
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