The Influence of Generator Scheduling and Time-Varying Fault Rates on Voltage Sag Prediction

This paper discusses the influence of generator scheduling and time-varying fault rates on the stochastic prediction of voltage sags. Typically, in the stochastic prediction of voltage sags, the annual expected sag frequencies (ESFs) at sensitive load points are calculated by assuming that the operating conditions and topology of the power system remain unchanged and fault rates of system components are constant throughout a year. In this paper, in order to obtain reasonable accuracy in predicting the annual ESFs at sensitive load points, the variation of fault rates due to adverse weather and the effect of generator scheduling are considered. The study was performed on the IEEE 30-bus test system. Two buses were randomly selected and the ESFs at the selected buses were calculated for different cases (i.e., with and without incorporation of time-varying fault rates and the operation schedule of generators in the system).

[1]  T.T. Lie,et al.  System voltage sag performance estimation , 2005, IEEE Transactions on Power Delivery.

[2]  R. Billinton,et al.  Reliability Cost/Worth Assessment of Distribution Systems Incorporating Time Varying Weather Conditions and Restoration Resources , 2001, IEEE Power Engineering Review.

[3]  Math Bollen,et al.  Understanding Power Quality Problems: Voltage Sags and Interruptions , 1999 .

[4]  J.V. Milanovic,et al.  The influence of fault distribution on stochastic prediction of voltage sags , 2005, IEEE Transactions on Power Delivery.

[5]  S. M. Shahidehpour,et al.  Coordination between long-term and short-term generation scheduling with network constraints , 2000 .

[6]  Math Bollen Effects of adverse weather and aging on power system reliability , 2000, 2000 IEEE Industrial and Commercial Power Systems Technical Conference. Conference Record (Cat. No.00CH37053).

[7]  Math Bollen,et al.  Fast assessment methods for voltage sags in distribution systems , 1995, IAS '95. Conference Record of the 1995 IEEE Industry Applications Conference Thirtieth IAS Annual Meeting.

[8]  N.D. Hatziargyriou,et al.  Analytical calculation and stochastic assessment of voltage sags , 2006, IEEE Transactions on Power Delivery.

[9]  Hong-Tzer Yang,et al.  Effective algorithm for handling constraints in generator maintenance scheduling , 2002 .

[10]  J.V. Milanovic,et al.  Stochastic prediction of voltage sags by considering the probability of the failure of the protection system , 2006, IEEE Transactions on Power Delivery.

[11]  E.E. Juarez,et al.  An analytical approach for stochastic assessment of balanced and unbalanced voltage sags in large systems , 2006, IEEE Transactions on Power Delivery.

[12]  Gabriel Olguin,et al.  Voltage Dip (Sag) Estimation in Power Systems based on Stochastic Assessment and Optimal Monitoring , 2005 .

[13]  J.V. Milanovic,et al.  Propagation of asymmetrical sags and the influence of boundary crossing lines on voltage sag prediction , 2004, IEEE Transactions on Power Delivery.

[14]  Math Bollen,et al.  Stochastic prediction of voltage sags in a large transmission system , 1998, 1998 IEEE Industrial and Commercial Power Systems Technical Conference. Conference Record. Papers Presented at the 1998 Annual Meeting (Cat. No.98CH36202).

[15]  Matti Lehtonen,et al.  A dynamic fault model for a power system component , 2003, 2003 IEEE Bologna Power Tech Conference Proceedings,.

[16]  Chang-Hyun Park,et al.  Stochastic Estimation of Voltage Sags in a Large Meshed Network , 2007, IEEE Transactions on Power Delivery.