Bayesian Networks for Fault Diagnosis of a Large Power Station and its Transmission Lines

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Nov 7, 2013 (3 years and 9 months ago)

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Bayesian Networks for Fault Diagnosis of a
Large Power Station and its Transmission Lines

Research Area:

Faculty

of

Engineering

Year:

2012

Type of
Publication:

Article


Authors:



M. M. Mansour



Mohamed A. A. Wahab



Wael M. Soliman


Journal:

ELECTRIC POWER
COMPONENTS AND
SYSTEMS

Volume:

40

Number:

8

Pages:

845
-
863

ISSN:

1532
-
5008


Abstract:

This article proposes a simplified fault
-
diagnosis system based on Bayesian
networks with noisy
-
OR/AND nodes to estimate the faulty item/section(s) of a
large power station and its transmission lines. The proposed method utilizes
the final information of p
rotective relays and corresponding circuit breakers to
construct the Bayesian fault diagnosis model for each section. The learning
algorithm for Bayesian network parameters takes the sum of the mean
-
squared error between the expected values and the compute
d values of
certain target variables as the minimizing optimization function to adjust the
network parameters continuously. By comparing the result beliefs of possible
faulty sections, the faulty item/section(s) becomes a candidate. In order to
test the va
lidity and feasibility of that method, a computer simulation of the
High Dam power station and its 500
-
kV transmission lines is used. It is shown
that the proposed diagnosis method has many merits, such as rapid
reasoning, less storage memory and processin
g time, easy correctness of
diagnosing results, flexibility, and application into a large power station and its
transmission lines for real
-
time fault diagnosis. Finally, it assists and supports
the operator of the control room to make the right decision,
especially in case
of communication loss.