| Accurate diagnosis of power system failure has important significance for accelerating accident treatment and restoring safe-economical operation of power system.In order to narrow the searching range of power system failure and avoid fault judge of all components successively,an identification method of fault area in power system base don colored self-modifying petri nets is proposed.Through this method,the fault diagnosis problem is limited to one or more passive network.First,a colored self-modifying petri nets model used to detect the fault area is built based on petri nets.This model can interpret the power network topology change,a discrete event dynamic process,caused by the breaker strip.All components are assigned with color attributes and the weights of some directed arcs are variable in the model.Then,searching of the fault area is realized by VB in the CPNTOOLS,the developing environment of the software.In order to reduce error caused by artificial subjective factors in power system fault diagnosis process and improve fault tolerance and accuracy of fuzzy petri net models,a power system fault diagnosis method based on improved dynamic adaptive fuzzy petri nets and back propagation algorithm is proposed.First,the general petri net models are built by introducing complementary arcs in dynamic adaptive fuzzy petri nets,Second,the back propagation algorithm of neural network is used to train model parameters.Finally,the adaptability and the fault tolerance of the algorithm are analyzed.It is indicated that the proposed method can make full use of the parallel processing capabilities of petri nets by the simulation results on 8-bus testing system and Si ping actual power system in Jilin province.Simple and clear in derivation,satisfying diagnosis results against single fault,multiple fault and the complex fault with malfunctions of several protections and breakers can be obtained by the proposed algorithm in this paper,also with fault tolerance. |
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