Neural Network Based Indexing and Recognition of Power Quality Disturbances
Abstract: Power quality (PQ)
analysis has become imperative for utilities as well as for consumers due to
huge cost burden of poor power quality. Accurate recognition of PQ disturbances
is still a challenging task, whereas methods for its indexing are not much
investigated yet. This paper expounds a system, which includes generation of
unique patterns called signatures of various PQ disturbances using continuous
wavelet transform (CWT) and recognition of these signatures using feed-forward
neural network. It is also corroborated that the size of signatures of PQ disturbances
are proportional to its magnitude, so this feature of the signature is used for
indexing the level of PQ disturbance in three sub-classes’ viz. high, medium,
and low. Further, the effect of number of neurons used by neural network on the
performance of recognition is also analyzed. Almost 100% accuracy of
recognition substantiates the effectiveness of the proposed system.
Author: Manoj Gupta, Rajesh
Kumar, Ram Awtar Gupta
Journal Code: jptkomputergg110030
