Sensitivity of voltage sags to network failure rate improvement

In this paper, a stochastic approach for predicting voltage sags is applied. The methodology, developed by the authors, allows to characterize network sites by time series of voltage sags, described by amplitude, duration and type. A responsibility sharing curve is used to establish an adequate voltage sag index. The developed methodology is based on Monte Carlo simulation and considers the stochastic nature of power system faults, as well as the probabilistic nature of successful operation of transmission line and busbar primary protection systems. An application example is presented, considering the IEEE Reliability Test System. The sensitivity of the voltage sag index with regard to the transmission lines failure rate is assessed. Results allow to establish a linear dependence, which is used for site characterization, showing that the methodology is an important tool for network planning, design, operation and maintenance. It allows to quantify the benefits obtained, at each network site, from reducing the transmission lines failure rate.

[1]  Maria Teresa Correia de Barros,et al.  Comparative Analysis of Busbar Protection Architectures , 2016 .

[2]  V.R.C. Fonseca,et al.  A dedicated software for voltage sag stochastic estimate , 2002, 10th International Conference on Harmonics and Quality of Power. Proceedings (Cat. No.02EX630).

[3]  Pedro Correia,et al.  Transmission line protection systems with aided communication channels—Part I: Performance analysis methodology , 2015 .

[4]  Probability Subcommittee,et al.  IEEE Reliability Test System , 1979, IEEE Transactions on Power Apparatus and Systems.

[5]  Jovica V. Milanovic,et al.  Identification of Weak Areas of Network Based on Exposure to Voltage Sags—Part II: Assessment of Network Performance Using Sag Severity Index , 2015, IEEE Transactions on Power Delivery.

[6]  Math Bollen Reliability analysis of industrial power systems taking into account voltage sags , 1993, Conference Record of the 1993 IEEE Industry Applications Conference Twenty-Eighth IAS Annual Meeting.

[7]  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).

[8]  Andre dos Santos,et al.  Stochastic modeling of power system faults , 2015 .

[9]  J.A. Martinez,et al.  Voltage sag stochastic prediction using an electromagnetic transients program , 2004, IEEE Transactions on Power Delivery.

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

[11]  J.M. de Carvalho Filho,et al.  Voltage Sags: Validating Short-Term Monitoring by Using Long-Term Stochastic Simulation , 2009, IEEE Transactions on Power Delivery.

[12]  G. Olguin,et al.  A Monte Carlo Simulation Approach to the Method of Fault Positions for Stochastic Assessment of Voltage Dips (Sags) , 2005, 2005 IEEE/PES Transmission & Distribution Conference & Exposition: Asia and Pacific.

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

[14]  Andre dos Santos,et al.  Reliability and availability analysis methodology for power system protection schemes , 2014, 2014 Power Systems Computation Conference.

[15]  Gilsoo Jang,et al.  Assessment of system voltage sag performance based on the concept of area of severity , 2010 .

[16]  C. Grigg,et al.  Predicting and preventing problems associated with remote fault clearing voltage dips , 1989, Conference Record. Industrial and Commercial Power Systems Technical Conference.

[17]  Math Bollen,et al.  Reliability Analysis Of Industrial Power Systems Taking Into Account Voltage Sags , 1993, Proceedings. Joint International Power Conference Athens Power Tech,.