Due to the gigantic power quality (PQ) demands for
modern systems, power systems fault detection and diagnosis
have become a significant issue. For this aim, it’s very important
to detect the fault at early time and determine its location through
any signal. Several methods and techniques are applied to solve
this problem such as discrete wavelet transform (DWT).
Although DWT has the ability of fast time detection of the fault,
it has a problem to discriminate between faulty and noisy signals.
DWT succeeds to extract features from altered transient
disturbances, but it fails to differentiate between transient
disturbances due to healthy or noisy signals. Fusion between
DWT for its speed and radial basis function for its accuracy is
done. The fusion technique used has a major disadvantage of its
delay time as the fault can be detected after the exact location
with several samples.
In this paper new technique will be proposed to overcome the
DWT and data fusion method problems, to achieve the
classification between noisy and faulty signals with high
accuracy. The proposed method is executed to classify the signals
premised on weights of them, complex tree classifier uses the
energy of the signal as a feature. All simulations are achieved
and done on IEEE standard 14 bus system to confirm the ability
and capacity of new suggested technique. Simulation results
show a better performance of the proposed system in comparison
with other methods, and that it capable to differentiate between
faulty and noisy signals and precisely locate the fault position. |
