- Title
- Advanced pattern discovery-based fuzzy classification method for power system dynamic security assessment
- Creator
- Luo, FengJi; Dong, Zhao Yang; Chen, Guo; Xu, Yan; Meng, Ke; Chen, Ying Ying; Wong, KitPo
- Relation
- IEEE Transactions on Industrial Informatics Vol. 11, Issue 2, p. 416-426
- Publisher Link
- http://dx.doi.org/10.1109/TII.2015.2399698
- Publisher
- Institute of Electrical and Electronics Engineers (IEEE)
- Resource Type
- journal article
- Date
- 2015
- Description
- Dynamic security assessment (DSA) is an important issue in modern power system security analysis. This paper proposes a novel pattern discovery (PD)-based fuzzy classification scheme for the DSA. First, the PD algorithm is improved by integrating the proposed centroid deviation analysis technique and the prior knowledge of the training data set. This improvement can enhance the performance when it is applied to extract the patterns of data from a training data set. Secondly, based on the results of the improved PD algorithm, a fuzzy logic-based classification method is developed to predict the security index of a given power system operating point. In addition, the proposed scheme is tested on the IEEE 50-machine system and is compared with other state-of-the-art classification techniques. The comparison demonstrates that the proposed model is more effective in the DSA of a power system.
- Subject
- data mining; dynamic security assessment; fuzzy control; pattern discovery
- Identifier
- http://hdl.handle.net/1959.13/1323993
- Identifier
- uon:24934
- Identifier
- ISSN:1551-3203
- Language
- eng
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