Author(s): Manoj Gupta | Rajesh Kumar | Ram Awtar Gupta
Journal: TELKOMNIKA
ISSN 1693-6930
Volume: 09;
Issue: 2;
Start page: 227;
Date: 2011;
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Keywords: continuous wavelet transform | feed forward neural network power quality recognition
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.
Journal: TELKOMNIKA
ISSN 1693-6930
Volume: 09;
Issue: 2;
Start page: 227;
Date: 2011;
VIEW PDF
DOWNLOAD PDF Original pageKeywords: continuous wavelet transform | feed forward neural network power quality recognition
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.