Rotor angle instability prediction using post-disturbance voltage trajectories

Athula D. Rajapakse, Francisco Gomez, Kasun Nanayakkara, Peter A. Crossley, Vladimir V. Terzija

Research output: Contribution to journalArticlepeer-review

152 Citations (Scopus)

Abstract

A new method for predicting the rotor angle stability status of a power system immediately after a large disturbance is presented. The proposed two-stage method involves estimation of the similarity of post-fault voltage trajectories of the generator buses after the disturbance to some pre-identified templates and then prediction of the stability status using a classifier which takes the similarity values calculated at the different generator buses as inputs. The typical bus voltage variation patterns after a disturbance for both stable and unstable situations are identified from a database of simulations using fuzzy C-means clustering algorithm. The same database is used to train a support vector machine classifier which takes proximity of the actual voltage variations to the identified templates as features. Development of the system and its performance were demonstrated using a case study carried out on the IEEE 39-bus system. Investigations showed that the proposed method can accurately predict the stability status six cycles after the clearance of a fault. Further, the robustness of the proposed method was examined by analyzing its performance in predicting the instability when the network configuration is altered.

Original languageEnglish
Article number5357461
Pages (from-to)947-956
Number of pages10
JournalIEEE Transactions on Power Systems
Volume25
Issue number2
DOIs
Publication statusPublished - May 2010
Externally publishedYes

Keywords

  • Fuzzy C-means clustering
  • Instability prediction
  • Pattern recognition
  • Support vector machines classifiers
  • Transient instability
  • Wide area protection

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