A De-noising Scheme for Enhancing Power Quality Problem Classification System Based on Wavelet Transform and Rule-Based Method

A Power quality Classification system can easily
extract features from the second detail signal obtained after
Discrete Wavelet Transform and using these features to construct
a Rule Based Algorithm for identifying types of disturbances that
exist in the captured power signal. Unfortunately, the signal
under investigation is often polluted by noises, rendering the
extraction of features a difficult task, especially if the noises have
high frequency spectrum which overlaps with the frequency of
the disturbances. The performance of the Classification system
would be greatly degraded, due to the difficulty in distinguishing
the noises and the disturbances. To overcome this difficulty and
to improve the performance of the system, this paper proposes a
suitable de-noising scheme to be integrated into the system so
that the classification system is still workable in a noisy
environment. In the proposed de-nosing scheme, a noise
shrinkage threshold used to minimize or eliminate the noise
coefficient in the 2nd compressed detail obtained after discrete
wavelet transform is determined adoptively according to the
background noises of the signal. The ability of the Classification
system can then be restored. To test the effectiveness of the denoising
scheme, the system is tested with noise-added disturbance
signals generated by MATLAB programming language and some
field data obtained from the PQDIF resource centre. Using the
de-noising scheme proposed in this paper, a higher tolerance to
noise can be achieved by the Power Quality Problem
Classification system.