Stochastic modeling of power system faults

• The proposed fault description is a useful tool for power system planning and design, when a stochastic approach of the power system faults characteristics is adopted.
• Fault rate and individual fault characteristics, namely fault location, type and resistance, are modeled.
• The model is able to reproduce atypical years, as happen in real transmission networks.
• The fault resistance model is based on Weibull distribution, with parameters per voltage level.
• The model is applied to short-circuit current computation and results are prone to specify network circuit breakers and current transformers probabilistically.

Correct modeling of power system faults is a key issue in a diversity of power system studies, such as in network planning, equipment specification and protection systems coordination. The present paper addresses the probabilistic description of faults, based on available data collected by transmission system operators for different voltage levels. Fault rate and individual fault characteristics are stochastically modeled, namely fault location, type and resistance. A fault resistance model is suggested, based on Weibull distribution, which parameters are set per voltage level. The proposed fault description is a useful tool for power system planning and design, when a stochastic approach of the power system faults characteristics is adopted. Time series of fault input data and simulation results are presented in a common format, so to allow using the same statistical tools as used in power system monitoring and field data reporting. The model is able to reproduce atypical years, as happen in real transmission networks. The developed fault model is used to generate stochastic short-circuit events, which are then used for short-circuit current computation. The methodology is applied to the IEEE RTS and simulation results are shown for the probability of amplitude and time constant values. These results are prone to be used to specify network circuit breakers and current transformers using a probabilistic approach.