Power System Fault Diagnosis Based on protection coordination and Petri net theory

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.

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Fault diagnosis of power system using neural Petri net and fuzzy neural Petri net

2006 IEEE Power India Conference ◽

10.1109/poweri.2006.1632569 ◽

2006 ◽

Cited By ~ 4

Author(s):

P.T.T. Binh ◽

N.D. Tuyen

Keyword(s):

Fault Diagnosis ◽

Power System ◽

Petri Net ◽

Fuzzy Neural

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Fuzzy Timing Petri Net for Fault Diagnosis in Power System

Mathematical Problems in Engineering ◽

10.1155/2012/717195 ◽

2012 ◽

Vol 2012 ◽

pp. 1-12 ◽

Cited By ~ 10

Author(s):

Alireza Tavakholi Ghainani ◽

Abdullah Asuhaimi Mohd Zin ◽

Nur ‘Ain Maiza Ismail

Keyword(s):

Fault Diagnosis ◽

Power System ◽

Petri Net ◽

Delay Time ◽

Uncertain Information ◽

Circuit Breakers ◽

Inference Procedure ◽

Use Of Time ◽

Time Information ◽

Firing Sequence

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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