This study presents a new technique for fast detecting
and diagnosing of power grids faults. Discrete Wavelet transform
(DWT) has a major disadvantage of noise sensitivity. The
proposed technique solves the problems of DWT, where a highprecision classification of noisy and faulty signals could be
obtained. Fusion between voltage and power readings is done to
provide a more reliable and accurate decision to determine the
exact location of the fault. In this technique, the learner classifier
is used,and the system is trained for multiple situations where
most faults may occur. All simulations were carried out and
performed on the standard IEEE 14 bus system to check the
efficiency and performance of the technique proposed.
Simulation results demonstrate, as will be discussed, a strong
effectiveness of the suggested approach relative to others. The
main feature of the proposed technique is that it can differentiate
between faulty and noisy signals and recognize the fault's
location quickly and with high reliability. |
