Optimized Extreme Learning Machine for Power System Transient Stability Prediction Using Synchrophasors

Yanjun Zhang, Tie Li, Guangyu Na, Guoqing Li and Yang Li

Mathematical Problems in Engineering, 2015, vol. 2015, 1-8

Abstract:

A new optimized extreme learning machine- (ELM-) based method for power system transient stability prediction (TSP) using synchrophasors is presented in this paper. First, the input features symbolizing the transient stability of power systems are extracted from synchronized measurements. Then, an ELM classifier is employed to build the TSP model. And finally, the optimal parameters of the model are optimized by using the improved particle swarm optimization (IPSO) algorithm. The novelty of the proposal is in the fact that it improves the prediction performance of the ELM-based TSP model by using IPSO to optimize the parameters of the model with synchrophasors. And finally, based on the test results on both IEEE 39-bus system and a large-scale real power system, the correctness and validity of the presented approach are verified.

Date: 2015
References: Add references at CitEc
Citations: View citations in EconPapers (4)

Downloads: (external link)
http://downloads.hindawi.com/journals/MPE/2015/529724.pdf (application/pdf)
http://downloads.hindawi.com/journals/MPE/2015/529724.xml (text/xml)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:hin:jnlmpe:529724

DOI: 10.1155/2015/529724

Access Statistics for this article

More articles in Mathematical Problems in Engineering from Hindawi
Bibliographic data for series maintained by Mohamed Abdelhakeem ().