Wavelet-based neural network for power quality disturbance recognition and classification

Kaewarsa, Suriya; Attakitmongcol, Kitti

doi:10.1080/1448837x.2006.11464146

Cited by 6 publications

(3 citation statements)

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“…This situation changed somewhat in 2016 and 2017, when the government installed power generation capacity in the form of generator diesels rent out at a substantial cost [8]. Moreover, the poor quality of the electrical power can be attributed to power disturbances such as overvoltages, capacitor switching transients, interruptions, harmonic distortions and impulse transients [9]. In electric power system, short-circuit or ground defects may cause voltage sags or momentary interruptions whereas switching off large load or energizing of a large capacitor bank may lead to voltage swell.…”

Section: Introductionmentioning

confidence: 99%

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Statistical Investigation of the Disturbances Affecting the Power Distribution Networks (HTB/HTA) of a Few Source Substations in South-Benin

Acclassato¹,

Houékpohéha²,

Madogni³

et al. 2022

JEEE

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Power quality is a significant issue that has become increasingly important to both electric power utilities and their customers because of financial losses caused by insufficient. In this work, the Fourier transform (FT) has been exploited for the statistical investigation of the disturbances affecting the electrical distribution networks (HTA) in South-Benin. The data coming from the overhead line disturbances and underground cables, especially from the source substations of MARIA-GLETA, VEDOKO, AKPAKPA, GBEGAMEY and SEME have been treated. These data have been collected and made in our disposal by the Beninese Electricity Energy Company (SBEE) over the period from 2010 to 2017. Harmonic Distortion Rate (TDH) and the Disturbance Rate (D R ) have been used to evaluate the variation coefficient of the different disturbances registered at the each source substation, in order to characterize the harmonic pollution and the reactive power consumption. According to the EN 50160 N standard, the results obtained show that VEDOKO registered 500 to 2000 interruptions of duration between 0.4x10 5 and 1.8x10 5 minutes, between 2013 and 2016 especially due to a very critical load shedding. The dominance of the disturbances observed has been due exclusively to the incidents setting off and the load shedding incidents in 2017. 2013 and 2015 have been characterized by the frequent blackouts of long living, and occasion considerable damages, and consequently slow down the economic activities, social and cultural life of consumers.

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Section: Introductionmentioning

confidence: 99%

“…Ferroresonance, transformer energization, or capacitor switching may cause transients. Ferroresonance, transformer energization, or capacitor switching may cause transients [8][9][10][11]. The term of power quality covers several types of problems of electricity supply and power system disturbances.…”

Section: Introductionmentioning

confidence: 99%

Statistical Investigation of the Disturbances Affecting the Power Distribution Networks (HTB/HTA) of a Few Source Substations in South-Benin

Acclassato¹,

Houékpohéha²,

Madogni³

et al. 2022

JEEE

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“…Sentoso et al [1,2] developed a PQ classification method based on wavelet analysis and a set of multiple artificial neural networks (ANN) to yield classification results with degrees of belief. 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.…”

Section: Introductionmentioning

confidence: 99%

Multiple disturbances classifier for electric signals using adaptive structuring neural networks

Chuang

Fahn

et al. 2008

Meas. Sci. Technol.

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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.

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Recognition of power quality events by using multiwavelet-based neural networks

Kaewarsa

Attakitmongcol

Kulworawanichpong

2008

International Journal of Electrical Power & Energy Systems

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Wavelet-based neural network for power quality disturbance recognition and classification

Cited by 6 publications

References 1 publication

Statistical Investigation of the Disturbances Affecting the Power Distribution Networks (HTB/HTA) of a Few Source Substations in South-Benin

Statistical Investigation of the Disturbances Affecting the Power Distribution Networks (HTB/HTA) of a Few Source Substations in South-Benin

Multiple disturbances classifier for electric signals using adaptive structuring neural networks

Recognition of power quality events by using multiwavelet-based neural networks

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