Fault diagnosis method of distributed power distribution network based on advanced Petri net

A DG-based distributed fault diagnosis method based on BP neural network with dynamic adaptive fuzzy Petri nets is proposed to solve the problem that traditional fault diagnosis methods lead to complex matrix and switching functions. In this paper, the general fault diagnosis model is constructed, and the simplified model of protection information is processed in the form of sets. If the operation mode and protection are changed, the model need not be reestablished, and the logic of the protection circuit breaker error correction is used with high fault tolerance. Secondly, BP algorithm is used to train the fuzzy parameters in the model. Finally, simulation test is carried out for the distribution network with DG, which verifies the reliability and fastness of the method.