With the development of large-scale complicated modern power systems, the requirement for the associated protection scheme tends to be more stringent and its combination more complex. However, it is very difficult to ...With the development of large-scale complicated modern power systems, the requirement for the associated protection scheme tends to be more stringent and its combination more complex. However, it is very difficult to figure out the factors of failure of such systems. This paper proposes a Petri net model of a transmission line protection relaying system, including three types of relays as well as an automatic reclosing device, and shows how to diagnose serial failure of the system by analyzing invariant sets of the model. Furthermore, it gives four basic types of failure sequences and its execution is much more intuitive and effective than the traditional method.展开更多
To diagnose the aeroengine faults accurately,the supervised Kohonen(S-Kohonen)network is proposed for fault diagnosis.Via adding the output layer behind competitive layer,the network was modified from the unsupervised...To diagnose the aeroengine faults accurately,the supervised Kohonen(S-Kohonen)network is proposed for fault diagnosis.Via adding the output layer behind competitive layer,the network was modified from the unsupervised structure to the supervised structure.Meanwhile,the hybrid particle swarm optimization(H-PSO)was used to optimize the connection weights,after using adaptive inheritance mode(AIM)based on the elite strategy,and adaptive detecting response mechanism(ADRM),HPSO could guide the particles adaptively jumping out of the local solution space,and ensure obtaining the global optimal solution with higher probability.So the optimized S-Kohonen network could overcome the problems of non-identifiability for recognizing the unknown samples,and the non-uniqueness for classification results existing in traditional Kohonen(T-Kohonen)network.The comparison study on the GE90 engine borescope image texture feature recognition is carried out,the research results show that:the optimized S-Kohonen network has a strong ability of practical application in the classification fault diagnosis;the classification accuracy is higher than the common neural network model.展开更多
文摘With the development of large-scale complicated modern power systems, the requirement for the associated protection scheme tends to be more stringent and its combination more complex. However, it is very difficult to figure out the factors of failure of such systems. This paper proposes a Petri net model of a transmission line protection relaying system, including three types of relays as well as an automatic reclosing device, and shows how to diagnose serial failure of the system by analyzing invariant sets of the model. Furthermore, it gives four basic types of failure sequences and its execution is much more intuitive and effective than the traditional method.
基金Joint Funds of the National Natural Science Foundation of China(NSAF)(No.U1330130)General Program of Civil Aviation Flight University of China(No.J2015-39)
文摘To diagnose the aeroengine faults accurately,the supervised Kohonen(S-Kohonen)network is proposed for fault diagnosis.Via adding the output layer behind competitive layer,the network was modified from the unsupervised structure to the supervised structure.Meanwhile,the hybrid particle swarm optimization(H-PSO)was used to optimize the connection weights,after using adaptive inheritance mode(AIM)based on the elite strategy,and adaptive detecting response mechanism(ADRM),HPSO could guide the particles adaptively jumping out of the local solution space,and ensure obtaining the global optimal solution with higher probability.So the optimized S-Kohonen network could overcome the problems of non-identifiability for recognizing the unknown samples,and the non-uniqueness for classification results existing in traditional Kohonen(T-Kohonen)network.The comparison study on the GE90 engine borescope image texture feature recognition is carried out,the research results show that:the optimized S-Kohonen network has a strong ability of practical application in the classification fault diagnosis;the classification accuracy is higher than the common neural network model.
