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- W2076280711 abstract "Concern towards power quality (PQ) hasincreased immensely due to the growing usage of high technology devices whichare very sensitive towards voltage and current variations and the de-regulationof the electricity market. The impact of these voltage and current variationscan lead to devices malfunction and production stoppages which lead to hugefinancial loss for the production company. The deregulation of electricitymarkets has made the industry become more competitive and distributed. Thus, ahigher demand on reliability and quality of services will be required by theend customers. To ensure the power supply is at the highest quality, anautomatic system for detection and localization of PQ activities in powersystem network is required. This paper proposed to use Slantlet Transform (SLT)with Support Vector Machine (SVM) to detect and localize several PQdisturbance, i.e. voltage sag, voltage swell, oscillatory-transient,odd-harmonics, interruption, voltage sag plus odd-harmonics, voltage swell plusodd-harmonics, voltage sag plus transient and pure sinewave signal werestudied. The analysis on PQ disturbances signals was performed in two steps,which are extraction of feature disturbance and classification of the dis- turbancebased on its type. To take on the characteristics of PQ signals, feature vectorwas constructed from the statistical value of the SLT signal coefficient andwavelets entropy at different nodes. The feature vectors of the PQ disturbancesare then applied to SVM for the classification process. The result shows thatthe proposed method can detect and localize different type of single andmultiple power quality signals. Finally, sensitivity of the proposed algorithmunder noisy condition is investigated in this paper." @default.
- W2076280711 created "2016-06-24" @default.
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- W2076280711 date "2015-01-01" @default.
- W2076280711 modified "2023-09-30" @default.
- W2076280711 title "Application of Slantlet Transform Based Support Vector Machine for Power Quality Detection and Classification" @default.
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- W2076280711 doi "https://doi.org/10.4236/jpee.2015.34030" @default.
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