A Novel Intuitionistic Fuzzy Inhibitor Arc Petri Net With Error Back Propagation Algorithm and Application in Fault Diagnosis

The setting and adjustment of the weight parameters in the traditional fault diagnosis method depend entirely on personal experience, and the parameter setting lacks regularity. To reduce the fault diagnosis errors caused by human subjective factors and improve the speed and accuracy of power grid fault diagnosis, we propose a method for power grid fault diagnosis using intuitionistic fuzzy inhibitor arc Petri net (IFIAPN) with error back propagation (BP) algorithm. Firstly, according to the network topology analysis and relay protection configuration setting rules, the inhibitor arc (IA) tuple is introduced into the model structure of the intuitionistic fuzzy Petri net to reduce the ambiguity of protection and circuit breaker action. Then, the weight parameters in the model are trained using a BP neural network algorithm to enhance the objectivity of the parameters. Finally, a simulation of an IEEE-39 node system and a real case study using the Hou-zhong line local grid were used to verify the effectiveness of the fault diagnosis method. The results show that the method can effectively deal with the refusal and mis-operation of multiple circuit breakers and improve the diagnostic efficiency under complex data environment.

Abstract licence: CC BY-NC-ND

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