An Electric Power System Fault Diagnosis Method Based On Time Series Petri Net

For effectively analyzing electric power faults, exactly identifying failure type, and highly providing disposal measure, depending on PSO (particle swarm optimization) algorithm, a PSO-FCM (particle swarm optimization-fuzzy c-means) algorithm was constructed by the FCM improvement of fuzzy clustering to avoid get in local optimal state. On this basis, an electric power system fault diagnosis method was established by means of PSO and FCM. Finally, this method was validated by an example. Consequently, this method can intellectively diagnose and identify the fault of electric power system, and can provide a new approach to stably operation in electric power system.

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Internet/Intranet Based Remote Condition Monitoring and Fault Diagnosis Scheme and System for Steam Turboset

Key Engineering Materials ◽

10.4028/www.scientific.net/kem.293-294.365 ◽

2005 ◽

Vol 293-294 ◽

pp. 365-372 ◽

Cited By ~ 2

Author(s):

Yong Yong He ◽

Wen Xiu Lu ◽

Fu Lei Chu

Keyword(s):

Fault Diagnosis ◽

Power System ◽

Electric Power ◽

Condition Monitoring ◽

Economic Benefits ◽

Electric Power System ◽

The Internet ◽

Time Data ◽

On Line ◽

Line Condition

The steam turboset is the key equipment of the electric power system. Thus, it is very important and necessary to monitor and diagnose the running condition and the faults of the steam turboset for the safe and normal running of the electric power system. In this paper, the Internet/Intranet based remote condition monitoring and fault diagnosis scheme is proposed. The corresponding technique and methods are discussed in detail. And a real application system is developed for the 300MW steam turboset. In this scheme, the system is built on the Internet/Intranet and the Client/Server construction and Web/Server model are adopted. The proposed scheme can guarantee real-time data acquisition and on-line condition analysis simultaneously. And especially, the remote condition monitoring and fault diagnosis can be implemented effectively. The developed system has been installed in a power plant of China. And the plant has obtained great economic benefits from it.

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Fault Diagnosis Method of Power System Based on the Adaptive Fuzzy Petri Net

2009 IEEE Circuits and Systems International Conference on Testing and Diagnosis ◽

10.1109/cas-ictd.2009.4960820 ◽

2009 ◽

Author(s):

Jingchuan Lan ◽

Min Ma

Keyword(s):

Fault Diagnosis ◽

Power System ◽

Petri Net ◽

Adaptive Fuzzy ◽

Fuzzy Petri Net ◽

Diagnosis Method

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A Modular Neural Network Scheme Applied to Fault Diagnosis in Electric Power Systems

The Scientific World JOURNAL ◽

10.1155/2014/176463 ◽

2014 ◽

Vol 2014 ◽

pp. 1-13

Author(s):

Agustín Flores ◽

Eduardo Quiles ◽

Emilio García ◽

Francisco Morant ◽

Antonio Correcher

Keyword(s):

Fault Diagnosis ◽

Power Systems ◽

Power System ◽

Transmission Line ◽

Electric Power ◽

Diagnostic System ◽

Specific Area ◽

Electric Power Systems ◽

Electric Power System ◽

Power Network

This work proposes a new method for fault diagnosis in electric power systems based on neural modules. With this method the diagnosis is performed by assigning a neural module for each type of component comprising the electric power system, whether it is a transmission line, bus or transformer. The neural modules for buses and transformers comprise two diagnostic levels which take into consideration the logic states of switches and relays, both internal and back-up, with the exception of the neural module for transmission lines which also has a third diagnostic level which takes into account the oscillograms of fault voltages and currents as well as the frequency spectrums of these oscillograms, in order to verify if the transmission line had in fact been subjected to a fault. One important advantage of the diagnostic system proposed is that its implementation does not require the use of a network configurator for the system; it does not depend on the size of the power network nor does it require retraining of the neural modules if the power network increases in size, making its application possible to only one component, a specific area, or the whole context of the power system.

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