Fault location in underground cables using ANFIS nets and discrete wavelet transform

Barakat, Shimaa; Eteiba, Magdy B.; Wahba, Wael Ismael

doi:10.1016/j.jesit.2014.12.003

Cited by 33 publications

(6 citation statements)

References 5 publications

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“…ANFIS bulanık çıkarım sisteminin geliştirilmesinden oluşan bir Sugeno-tip bulanık çıkarım modelidir (Jang,1993). Sugeno-tip ANFIS Sugeno bulanık çıkarım sisteminde sıfırıncı derece ile yer değiştirmek için çıkış sistemine birinci dereceden polinom uygular [5]. ANFIS metodu, insan düşünce sistematiğinde çok kullanılan eğer-ise (IF-THEN) kurallarından yararlanarak oluşturulmuş bir sinirsel-bulanık sistemidir.…”

Section: Uyarlanabilir Sinirsel-bulanık çıKarım Sistemi (Anfis)unclassified

Prediction of bearing fault size by using model of adaptive neuro-fuzzy inference system

Kaplan

Kuncan

Ertunç

2015

2015 23nd Signal Processing and Communications Applications Conference (SIU)

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Özetçe-Dönme işlemine sahip makinelerde, hayati öneme sahip olan rulmanların arıza durumlarının izlenmesi ve daha önceden hataların tespit edilmesi, prosesin aksamaması açısından büyük önem taşımaktadır. Bu çalışmada, rulman bileşenlerinden olan iç bilezik üzerine lazer ışını ile belirli boyutlarda yapay hatalar oluşturulmuş ve rulman-mil düzeneğinde titreşim sinyalleri elde edilmiştir. Çalışmada rulmanlarda meydana gelen arızaların boyutunu uyarlanabilir sinirsel-bulanık mantık çıkarım sistemi modelini kullanarak teşhis etmek amaçlanmıştır. Toplanan titreşim verilerinin gerçek zamanda özellikleri çıkarıldıktan sonra belirli ağırlıklarla çarpılmış ve oluşturulan sınıflandırma modeline giriş olarak verilmiştir. Lazer ile oluşturulan 0,15cm, 0,5cm, 0,9cm çaptaki iç bilezik hatalı rulmanların çıkarılan özelliklerinin hatanın boyutuna bağlı olarak farklı olduğu gözlemlenmiştir. Bu özellikler kullanılarak ANFIS sınıflandırma modeli geliştirilmiş ve rulmanın iç bileziğinde meydana gelen hatanın büyüklüğü, gerçek hata değerinden %2.40 bir farkla bulunmuştur. Daha sonra ANFIS çıkışında bulunan hata değerlerine uygulanan 0.1mm eşik değer ile hata bandı oluşturulmuş ve bütün tahmin değerlerinin bu hata bandı içerisinde çıktığı görülmüştür. Anahtar kelimeler-Arıza teşhis, ANFIS, rulman hataları, sınıflandırma Abstract-Condition monitoring of bearings faults which have vital importance in machines and detection of faults earlier have very big importance in terms of disruption of process.In this study, certain sizes artificial faults are generated by the laser beam on inner rings of bearing and vibration signals are obtained from these bearings in a shaftbearing setup. It is aimed to diagnose the size of the defects occurring in the bearings by using adaptive neuro-fuzzy inference system (ANFIS) model in the study. After extracting the real-time features of obtained vibration data, they are multiplied by the specific weight and they are given as input to the generated classification model. It has been observed difference of features extracted from of 0.15 cm, 0.5 cm, 0.9 cm diameter inner ring faulty bearings created by the laser depending on size of faults. ANFIS classification model is developed by using these features and the size of the faults occurring in these bearings were calculated with an actual error 2.40 %. Then a error band are created with 0.1mm threshold value and it is observed that all the predicted values are inside this error band.

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Section: Uyarlanabilir Sinirsel-bulanık çıKarım Sistemi (Anfis)unclassified

Prediction of bearing fault size by using model of adaptive neuro-fuzzy inference system

Kaplan

Kuncan

Ertunç

2015

2015 23nd Signal Processing and Communications Applications Conference (SIU)

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“…However, identifying the HIF location in an underground system is difficult due to the low fault current and non-visibility [1]. Therefore, a fast and accurate HIF fault localization in an underground system is crucial to reduce the risk of damage proliferation, outage time and accelerate the restoration process [2]. Estimating the impedance and location of HIF is a challenging task due to the similarity of the operating current magnitude under normal and faulty conditions.…”

Section: Introductionmentioning

confidence: 99%

“…In [20], the discrete Fourier transform (DFT) is used instead of WT and EMD for the same purpose. Similarly, several AI methods have also been proposed such as artificial neural network (ANN) [21,22], fuzzy logic system (FLS) [23,24], support vector regression (SVR) [10,[25][26][27], core vector regression (CVR) [19] and adaptive neuro-fuzzy inference system (ANFIS) [2,20]. Some researchers have also utilized more than one AI method as proposed in [23][24][25].…”

Section: Introductionmentioning

confidence: 99%

Optimized artificial neural network to improve the accuracy of estimated fault impedances and distances for underground distribution system

et al. 2020

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This paper proposes an approach to accurately estimate the impedance value of a high impedance fault (HIF) and the distance from its fault location for a distribution system. Based on the three-phase voltage and current waveforms which are monitored through a single measurement in the network, several features are extracted using discrete wavelet transform (DWT). The extracted features are then fed into the optimized artificial neural network (ANN) to estimate the HIF impedance and its distance. The particle swarm optimization (PSO) technique is employed to optimize the parameters of the ANN to enhance the performance of fault impedance and distance estimations. Based on the simulation results, the proposed method records encouraging results compared to other methods of similar complexity for both HIF impedance values and estimated distances.

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“…For locating arc faults, many works have been developed algorithms using the impedance method [3,4,5,6], traveling wave methods [7,8,9,10] and intelligent methods [11,12,13]. However, these methods mainly locate parallel faults on high power distribution systems.…”

Section: Introductionmentioning

confidence: 99%

A series arc fault location algorithm based on an impedance method for a domestic AC system

Calderon-Mendoza

Schweitzer

Weber

2017

2017 IEEE Holm Conference on Electrical Contacts

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 This article describes a fault location method developed within our laboratory. The method is based on the electric line physical parameters to estimate a serial arc fault distance in an experimental short transmission line. Using recorded data at two ends of the transmission line, the method estimates currents in many hypothetic fault points of the line, looking for a current difference minimization. The test bench used for our purpose is composed of a 49 m transmission line used in domestic networks (220V-50Hz) and a 47 ohms power resistor load. Across the line, an arc fault (carbonized path) can be inserted at many different points (40 points of insertion are available).

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Fault location in underground cables using ANFIS nets and discrete wavelet transform

Cited by 33 publications

References 5 publications

Prediction of bearing fault size by using model of adaptive neuro-fuzzy inference system

Prediction of bearing fault size by using model of adaptive neuro-fuzzy inference system

Optimized artificial neural network to improve the accuracy of estimated fault impedances and distances for underground distribution system

A series arc fault location algorithm based on an impedance method for a domestic AC system

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