Expert System for Power Quality Disturbance Classifier

Reaz, Mamun Bin Ibne; Choong, F.; Sulaiman, M.S.; Mohd-Yasin, F.; Kamada, Masaru

doi:10.1109/tpwrd.2007.899774

Cited by 140 publications

(77 citation statements)

References 19 publications

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“…This property supports the WT analysis of signals with transient response due to PQ disturbances present in voltage, current and/or frequency [6], [7].…”

Section: Introductionsupporting

confidence: 77%

Adaptive Threshold for Electrical Disturbances Segmentation

Andrade

Oleskovicz

Fernandes

et al. 2024

RE&PQJ

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Abstract. The detection of the electrical signal discontinuities in the oscillographies recorded in substations and/or points of common coupling allows their segmentation, which is crucial for implementation of automated methods for detection, classification, location and storage by classes of disturbances in electric power systems. In this context, this study provides a way of determining an adaptive threshold that allows the segmentation of voltage or current signals based on Wavelets decomposition. The disturbances considered in this work were the short-duration voltage variations, impulsive and oscillatory transients, and harmonic distortions. The signals were synthetically generated. Moreover, white noise was added on the signals. Thus, a Symlet Wavelet was applied to the signals in order to denoise them. In the sequence, a Daubechies Wavelet was used to decompose the filtered signals. So, to determine the initial and final points of each segment, an adaptive threshold was established based on the energy and entropy of energy for the second level of decomposition. Thus, the number and position of each segment were determined according to the intersections of the detail curves and the thresholds found.

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“…This property supports the WT analysis of signals with transient response due to PQ disturbances present in voltage, current and/or frequency [6], [7].…”

Section: Introductionsupporting

confidence: 77%

Adaptive Threshold for Electrical Disturbances Segmentation

Andrade

Oleskovicz

Fernandes

et al. 2024

RE&PQJ

View full text Add to dashboard Cite

show abstract

“…Perunicic et al, 1998;Santoso et al, 2000c;Gaouda et al, 2002a;Giang, 2004;He et al, 2006;Mishra et al, 2008. Expert System Based Classifier Santoso et al, 2000b;Styvaktakis et al, 2001;Styvaktakis et al, 2002;Chung et al, 2002;Reaz et al, 2007. Fuzzy Expert System Based Classifiers Dash et al, 2000Thapar et al, 2003;Zhu et al, 2004;Chilukuri et al, 2004;Ortiz et al, 2006;Bizjak et al, 2006.…”

Section: Summary Of Pq Classification Methodsmentioning

confidence: 99%

“…Reference (Styvaktakis et al, 2002) has also proposed an experts system for the classification and analysis of a number of power system events. Beside, few more papers (Kazibwe et al, 1992;Schlabbach 1994;Kezunovic et al, 1999;Reaz et al, 2007) have used the expert system as the PQ classifier. As the number of events or features increases, the complexity of the expert systems also increases.…”

Section: Expert System Based Classifiermentioning

confidence: 99%

Power quality event classification: an overview and key issues

Saxena¹,

Verma²,

Singh³

2010

Int. J. Eng. Sci. Tech

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Power quality (PQ) issue has attained considerable attention in the last decade due to large penetration of power electronics based loads and/or microprocessor based controlled loads. On one hand these devices introduce power quality problem and on other hand these mal-operate due to the induced power quality problems. PQ disturbances/events cover a broad frequency range with significantly different magnitude variations and can be non-stationary, thus, accurate techniques are required to identify and classify these events/disturbances. This paper presents a comprehensive overview of different techniques used for PQ events' classifications. Various artificial intelligent techniques which are used in PQ event classification are also discussed. Major Key issues and challenges in classifying PQ events are critically examined and outlined.

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“…The wavelet transform has been applied to the wide range of PQ signals analysis: feature extraction [2], noise reduction [3], and data compression [4]. Recently, The identification of PQ disturbances is often based on artificial neural network (ANN) [5], fuzzy method (FL) [6], expert system (ES) [7], support vector machines (SVM) [8], and hidden Markov model (HMM) [9]. Many of the studies proposed in the literature present that these techniques can use feature vectors derived from disturbance waveforms to classify PQ disturbances.…”

Section: Introductionmentioning

confidence: 99%

Classification of Power Quality Disturbances Using Wavelet Packet Energy Entropy and LS-SVM

Zhang¹,

Li²,

Hu³

2010

EPE

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The power quality (PQ) signals are traditionally analyzed in the time-domain by skilled engineers. However, PQ disturbances may not always be obvious in the original time-domain signal. Fourier analysis transforms signals into frequency domain, but has the disadvantage that time characteristics will become unobvious. Wavelet analysis, which provides both time and frequency information, can overcome this limitation. In this paper, there were two stages in analyzing PQ signals: feature extraction and disturbances classification. To extract features from PQ signals, wavelet packet transform (WPT) was first applied and feature vectors were constructed from wavelet packet log-energy entropy of different nodes. Least square support vector machines (LS-SVM) was applied to these feature vectors to classify PQ disturbances. Simulation results show that the proposed method possesses high recognition rate, so it is suitable to the monitoring and classifying system for PQ disturbances.

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Expert System for Power Quality Disturbance Classifier

Cited by 140 publications

References 19 publications

Adaptive Threshold for Electrical Disturbances Segmentation

Adaptive Threshold for Electrical Disturbances Segmentation

Power quality event classification: an overview and key issues

Classification of Power Quality Disturbances Using Wavelet Packet Energy Entropy and LS-SVM

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