TITLE:
Application of Slantlet Transform Based Support Vector Machine for Power Quality Detection and Classification
AUTHORS:
Faridah Hanim M. Noh, Hajime Miyauchi, M. Faizal Yaakub
KEYWORDS:
Features Extraction, Power Quality Disturbances, Slantlet Transform, Support Vector Machine
JOURNAL NAME:
Journal of Power and Energy Engineering,
Vol.3 No.4,
April
14,
2015
ABSTRACT: Concern towards power quality (PQ) has
increased immensely due to the growing usage of high technology devices which
are very sensitive towards voltage and current variations and the de-regulation
of the electricity market. The impact of these voltage and current variations
can lead to devices malfunction and production stoppages which lead to huge
financial loss for the production company. The deregulation of electricity
markets has made the industry become more competitive and distributed. Thus, a
higher demand on reliability and quality of services will be required by the
end customers. To ensure the power supply is at the highest quality, an
automatic system for detection and localization of PQ activities in power
system network is required. This paper proposed to use Slantlet Transform (SLT)
with Support Vector Machine (SVM) to detect and localize several PQ
disturbance, i.e. voltage sag, voltage swell, oscillatory-transient,
odd-harmonics, interruption, voltage sag plus odd-harmonics, voltage swell plus
odd-harmonics, voltage sag plus transient and pure sinewave signal were
studied. The analysis on PQ disturbances signals was performed in two steps,
which are extraction of feature disturbance and classification of the dis- turbance
based on its type. To take on the characteristics of PQ signals, feature vector
was constructed from the statistical value of the SLT signal coefficient and
wavelets entropy at different nodes. The feature vectors of the PQ disturbances
are then applied to SVM for the classification process. The result shows that
the proposed method can detect and localize different type of single and
multiple power quality signals. Finally, sensitivity of the proposed algorithm
under noisy condition is investigated in this paper.