Recognition of Power Quality Events by Using Multiwavelet-Based Neural Network

Kaewarsa, Suriya; Attakitmongcol, Kitti; Kulworawanichpong, Thanatchai

doi:10.1109/icis.2007.153

Cited by 2 publications

(1 citation statement)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Kaewarsa and Attakitmongcol [3] also utilized wavelet-transform-based neural network to recognize PQ events. Kaewarsa et al [4] used multiwavelet prefilters [5] to perform analysis on PQ disturbance waveforms. Chung et al [6] proposed a power disturbance classifier using a wavelet-based hidden Markov chain model.…”

Section: Introductionmentioning

confidence: 99%

Multiple disturbances classifier for electric signals using adaptive structuring neural networks

Chuang

Fahn

et al. 2008

Meas. Sci. Technol.

View full text Add to dashboard Cite

This work proposes a novel classifier to recognize multiple disturbances for electric signals of power systems. The proposed classifier consists of a series of pipeline-based processing components, including amplitude estimator, transient disturbance detector, transient impulsive detector, wavelet transform and a brand-new neural network for recognizing multiple disturbances in a power quality (PQ) event. Most of the previously proposed methods usually treated a PQ event as a single disturbance at a time. In practice, however, a PQ event often consists of various types of disturbances at the same time. Therefore, the performances of those methods might be limited in real power systems. This work considers the PQ event as a combination of several disturbances, including steady-state and transient disturbances, which is more analogous to the real status of a power system. Six types of commonly encountered power quality disturbances are considered for training and testing the proposed classifier. The proposed classifier has been tested on electric signals that contain single disturbance or several disturbances at a time. Experimental results indicate that the proposed PQ disturbance classification algorithm can achieve a high accuracy of more than 97% in various complex testing cases.

show abstract