摘要
In this paper, a mathematical model consisting of forward and backward models is built on parallel genetic algorithms (PGAs) for fault diagnosis in a transmission power system. A new method to reduce the scale of fault sections is developed in the forward model and the message passing interface (MPI) approach is chosen to parallel the genetic algorithms by global sin-gle-population master-slave method (GPGAs). The proposed approach is applied to a sample system consisting of 28 sections, 84 protective relays and 40 circuit breakers. Simulation results show that the new model based on GPGAs can achieve very fast computation in online applications of large-scale power systems.
In this paper, a mathematical model consisting of forward and backward models is built on parallel genetic algorithms （PGAs） for fault diagnosis in a transmission power system. A new method to reduce the scale of fault sections is developed in the forward model and the message passing interface （MPI） approach is chosen to parallel the genetic algorithms by global single-population master-slave method （GPGAs）. The proposed approach is applied to a sample system consisting of 28 sections, 84 protective relays and 40 circuit breakers. Simulation results show that the new model based on GPGAs can achieve very fast computation in online applications of large-scale power systems.
基金
the National Natural Science Foundation of China (No. 50677062)
the New Century Excellent Talents in Uni-versity of China (No. NCET-07-0745)
the Natural Science Foundation of Zhejiang Province, China (No. R107062)
