Proposed wavelet-neurofuzzy combined system for power quality violations detection and diagnosis

Elmitwally, A.; Farghal, S.A.; Kandil, Magy; Abdelkader, Sobhy M.; Elkateb, M.M.

doi:10.1049/ip-gtd:20010031

Cited by 96 publications

(40 citation statements)

References 7 publications

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“…Electrical noise riding on the PQ waveform data also modulates wavelet domain energy distribution patterns of disturbances which, in turn, degrade performance of the existing classification schemes utilizing wavelets for feature extraction, under practical conditions. Wavelet based denoising techniques to remove the effect of noise on PQ waveform data (Yang et al, 2000;Elmitwally et al, 2001;Yang et al, 2001;Gaouda et al, 2002b) have been proposed in the literature but, their performance degrades with decrease in the signal to noise ratio (SNR). Among these, (Yang et al, 2001) proposed a promising method for denoising of PQ waveform data to improve the performance of wavelet based PQ monitoring systems.…”

Section: Effect Of Noise On Pq Event Classifiersmentioning

confidence: 99%

“…But, there are fundamental problem in the formulation of the proposed technique. Most of the above works generally deal with the denoising of PQ signals from detection and localization point of view (Yang et al, 2000;Gaouda et al, 2002b;Elmitwally et al, 2001). However, the robustness of the energy features, extracted for classification using DWT in the presence of noise and its effect on classification accuracy is rarely addressed.…”

Section: Effect Of Noise On Pq Event Classifiersmentioning

confidence: 99%

See 1 more Smart Citation

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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Section: Effect Of Noise On Pq Event Classifiersmentioning

confidence: 99%

Section: Effect Of Noise On Pq Event Classifiersmentioning

confidence: 99%

Power quality event classification: an overview and key issues

Saxena¹,

Verma²,

Singh³

2010

Int. J. Eng. Sci. Tech

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“…Wavelet multi-resolution technique along with neuro-fuzzy classifier for PQ disturbance detection has been explained [9]. As wavelet transforms cannot be applied for the analysis of non stationary signals, S-transforms were implemented due to their excellent frequency resolution characteristics.…”

Section: Introductionmentioning

confidence: 99%

Automatic Recognition of Power Quality Disturbances using Kalman Filter and Fuzzy Expert System

Sundaram¹,

Neela²

2016

IJCA

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An efficient method for power quality disturbances recognition and classification is presented in this paper. The method used is based on the Kalman filter and fuzzy expert system. Various classes of disturbances are generated using Matlab parametric equations. Kalman filter is used for extracting the input features of various power disturbances. The extracted features such as amplitude and slope are applied as inputs to the fuzzy expert system that uses some rules on these inputs to classify the PQ disturbances. Fuzzy classifier has been implemented and tested for various types of power quality disturbances. The results clearly indicate that the proposed method has the ability to detect and classify PQ disturbances accurately. The performance of the proposed method has been evaluated by comparing the results against Kalman filter based neural classifier.

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“…The approach was based on wavelet transform analysis, particularly the dyadic-orthonormal wavelet transform. Elmitwally et al [11] proposed a two-stage system that employed the potentials of the wavelet transform and the adaptive neurofuzzy networks. Simulation results confirm the aptness and the capability of the proposed system in power quality violations detection and automatic diagnosis.…”

Section: Introductionmentioning

confidence: 99%

Recognition of fault process parameters for diameter uniformity variation in melt spinning

Kuo

Huang

et al. 2014

Fibers Polym

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This study focused on the abnormal condition of diameter variation in melt spinning, and attempted to recognize the machine processing parameter that deviates from the set value. The experiment used polypropylene as the experimental material. The machine processing parameters included nine factors, including temperatures in three extruder barrel sections, temperature of metering pump, die temperature, spinneret temperature, speed of an extruder screw, speed of metering pump and speed of take-up roll. The biaxial laser sensor measured the yarn diameter instantly, and the optimum parameter combination for minimum diameter variation was obtained by Taguchi method and analysis of variance (ANOVA). The degree of influence of various processing parameters on the diameter variation was determined. The optimum parameter combination was used as parameter setting value, and the processing parameters of various factors were changed for experiment, so as to obtain the signals of abnormal condition. Two methods were used for feature extraction. The minimum entropy of wavelet transform signal was used as eigenvector, and two features were selected by analyzing the statistical process control chart, which are skewness and kurtosis. The abnormal processing parameters and normal condition were recognized effectively and accurately by using the three eigenvalues and back-propagation neural network. With enough training samples, the recognition success rate was as high as 100 %. For complete abnormity diagnosis of melt spinning machine, a two-factor classification process was established by using the single-factor classification result and backpropagation neural network. The experimental results proved that the proposed method can recognize various abnormal conditions successfully and effectively.

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Proposed wavelet-neurofuzzy combined system for power quality violations detection and diagnosis

Cited by 96 publications

References 7 publications

Power quality event classification: an overview and key issues

Power quality event classification: an overview and key issues

Automatic Recognition of Power Quality Disturbances using Kalman Filter and Fuzzy Expert System

Recognition of fault process parameters for diameter uniformity variation in melt spinning

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