Power system fault diagnosis with a weighted fuzzy time Petri net

Author(s):  
Wenke Wu ◽  
Yusheng Xue ◽  
Xia Yin ◽  
Fushuan Wen
2019 ◽  
Vol 64 (12) ◽  
pp. 5253-5259 ◽  
Author(s):  
Zhou He ◽  
Zhiwu Li ◽  
Alessandro Giua ◽  
Francesco Basile ◽  
Carla Seatzu

2015 ◽  
Vol 60 (4) ◽  
pp. 997-1009 ◽  
Author(s):  
Francesco Basile ◽  
Maria Paola Cabasino ◽  
Carla Seatzu

2014 ◽  
Vol 556-562 ◽  
pp. 2157-2160
Author(s):  
Lei Wang ◽  
Qing Chen ◽  
Zhan Jun Gao ◽  
Bin Chen ◽  
Min Wei Chen ◽  
...  

A new power system fault diagnosis model is proposed in this paper and it will be used in the UNIX platform. It includes the Petri net system's structure, software service and hardware integration method. Though technology of Petri net is quite successful, its application to power system fault diagnosis is deficient. According to the UNIX platform, a new power system fault diagnosis model is designed and it takes an example to explain how to use its high performance computing method (or parallel computing method) and UNIX environment to design a new intelligent algorithm with information fusion and multi-data resources in power system fault diagnosis.


2012 ◽  
Vol 2012 ◽  
pp. 1-12 ◽  
Author(s):  
Alireza Tavakholi Ghainani ◽  
Abdullah Asuhaimi Mohd Zin ◽  
Nur ‘Ain Maiza Ismail

A model-based system for fault diagnosis in power system is presented in this paper. It is based on fuzzy timing Petri net (FTPN). The ordinary Petri net (PN) tool is used to model the protective components, relays, and circuit breakers. In addition, fuzzy timing is associated with places (token)/transition to handle the uncertain information of relays and circuits breakers. The received delay time information of relays and breakers is mapped to fuzzy timestamps,π(τ), as initial marking of the backward FTPN. The diagnosis process starts by marking the backward sub-FTPNs. The final marking is found by going through the firing sequence,σ, of each sub-FTPN and updating fuzzy timestamp in each state ofσ. The final marking indicates the estimated fault section. This information is then in turn used in forward FTPN to evaluate the fault hypothesis. The FTPN will increase the speed of the inference engine because of the ability of Petri net to describe parallel processing, and the use of time-tag data will cause the inference procedure to be more accurate.


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