V.V. Terzija

Publications (2)2.68 Total impact

• Article: Rotor Angle Instability Prediction Using Post-Disturbance Voltage Trajectories

  A.D. Rajapakse, F. Gomez, K. Nanayakkara, P.A. Crossley, V.V. Terzija
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  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.
  IEEE Transactions on Power Systems 06/2010; · 2.68 Impact Factor
• Conference Proceeding: Rotor angle stability prediction using post-disturbance voltage trajectory patterns

  A.D. Rajapakse, F. Gomez, O.M.K.K. Nanayakkara, P.A. Crossley, V.V. Terzija
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  ABSTRACT: A novel method for predicting the rotor angle stability condition of a large power system immediately after a large disturbance is presented. The proposed two stage method involves a) estimation of the proximity of post-fault bus voltage trajectories after the disturbance to some pre-identified templates and b) prediction of the stability status using a classifier, which uses the proximity values calculated at different 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 the fuzzy c-means clustering algorithm. The same database is used to train the support vector machine classifier used in the second stage of the prediction process. Development of the transient stability prediction scheme and its performance were demonstrated using a case study carried out on the IEEE-39 bus system.
  Power & Energy Society General Meeting, 2009. PES '09. IEEE; 08/2009