The Modeling And Analysis Methods Of Fuzzy System Based On Probability Petri Net

The fuzzy system is an extension of deterministic system. Unlike the deterministic system, the value of its input and output is fuzzy. Therefore, there is uncertainty in fuzzy system state. It is the focus of study which describes and analyses the uncertainty of fuzzy system, and gives current state of system accurately in the case of incomplete information.The probability Petri net is proposed based on fuzzy set theory, hybrid system theory, Petri net theory and Bayesian networks for uncertainty problems in fuzzy system. Two Petri nets which are uncertainty hybrid Petri net model and time probability Petri net are defined based on the probability Petri net. One is proposed a perceived trend analysis, method based on hybrid system power equipment which aims at predicting state and analyzing problems difficultly. This kind of systems is modeled by uncertain hybrid Petri net. It makes the tokens transfer through transitions triggering. So the states of system are changed. It can deduce the recent state of system by identify the state of system, and thus analyzes the tendency. The other model is time probability Petri net against the large amount of signal in the power system and difficult to model. Time interval in the model represents delay of alarm signal. And probability represents uncertainty of system which is component mal-operation and mis-operation of system component. The final state of the model is obtained by deduction of model. The possible failure system components are confirmed by calculating the final state probability. Using the timing consistency determination function determine whether system states are compatible, and reason out system state.Using the example of transformers and electricity systems simulate two kinds of probability Petri net models which are non-deterministic hybrid Petri net and time probability Petri net. So, fault diagnosis of transformers in the power system as the background, a hybrid Petri net model is built which simulate state detection and process of trend analysis. The results verify the validity of the method. The model provides an effective method to predict the current status and predict, assess and real-time control future state. The time probability Petri net for power system takes the time delay and probability factor of the system components. In the case of incomplete fault information, the model is still able to give more accurate state results of system.