Comparison of fourier &amp;amp; wavelet transform methods for transmission line fault classification

Abdollahi, A.; Seyedtabaii, S.

doi:10.1109/peoco.2010.5559232

Cited by 30 publications

(9 citation statements)

References 14 publications

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“…The Neural Network Toolbox (nntool) for MATLAB is utilized in this paper. A three-layer feed-forward backpropagation network is applied for this study [1,9]. Although the number of neurons in the input and output layers depends on the number of input and output data, the number of neurons in the hidden layer is determined by the training performance of the network.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

“…Entropy values of DWT components obtained from current data were preferred in the study of El Safty and El-Zonkoly [7]. Upendar et al [8] used nine-level DWT coefficients and a perceptron neural network as classifiers, whereas Abdollahi and Seyedtabaii [9] employed an ANN with three-level approximation and detail coefficients. In addition, Samantaray [10] applied S-transform to current signals and obtained a feature vector with energy, standard deviation, variance, and autocorrelation components calculated from these signals.…”

Section: Introductionmentioning

confidence: 99%

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Classification of short-circuit faults in high-voltage energy transmission line using energy of instantaneous active power components-based common vector approach

Yumurtacı¹,

Gökmen²,

Kocaman³

et al. 2016

Turk J Elec Eng & Comp Sci

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The majority of power system faults occur in transmission lines. The classification of these faults in power systems is an important issue. In this paper, the real parameters of a 28 km, 154 kV transmission line between Simav and Demirci in Turkey's electricity transmission network is simulated in MATLAB/Simulink. Wavelet packet transform (WPT) is applied to instantaneous voltage signals. Instantaneous active power components are obtained by multiplying instantaneous currents obtained from a voltage source side with these WPT-based voltage signal components. A new feature vector extraction scheme is employed by calculating the energies of instantaneous active power components.Constructed feature vectors are treated with a classifier for short-circuit faults that occurred in high-voltage energy transmission lines; this is known as the common vector approach (CVA). This is the first implementation of CVA in the classification of short-circuit faults that occurred in high-voltage energy transmission lines. Furthermore, the same feature vector is applied to a support vector machine and artificial neural network for a comparison with the CVA method regarding classification performance and testing duration issues. Additionally, a graphical user interface is designed in MATLAB/GUI. Various noise levels, source frequencies, fault distances, fault inception angles, and fault exposure durations can be investigated with this interface. Classification of short-circuit faults in high-voltage transmission line is achieved by using an offline monitoring methodology. It is concluded that a combination of the proposed feature extraction scheme with the CVA classifier gives substantially high performance for the classification of short circuit faults in transmission line.

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Section: Artificial Neural Networkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Classification of short-circuit faults in high-voltage energy transmission line using energy of instantaneous active power components-based common vector approach

Yumurtacı¹,

Gökmen²,

Kocaman³

et al. 2016

Turk J Elec Eng & Comp Sci

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“…The mother wavelet determines the filters used to analyze signals. In this paper Db4 (Daubechies 4) wavelet was chosen because of its success in detecting faults [4], [5].…”

Section: Discrete Wavelet Transformmentioning

confidence: 99%

Detection and classification of faults on 220 KV transmission line using wavelet transform and neural network

Hasabe¹,

Vaidya²

2014

ijsgce

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“…Ref. [20] explains the procedure of fault detection and classification comprehensively. The outputs obtained from this section are employed as inputs of fault distance estimation system.…”

Section: A Fault Detection and Classificationmentioning

confidence: 99%

Transmission line fault location estimation by Fourier & wavelet transforms using ANN

Abdollahi

Seyedtabaii

2010

2010 4th International Power Engineering and Optimization Conference (PEOCO)

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Nowadays, power supply has become a business commodity. The quality and reliability of power needs to be maintained in order to obtain optimum performance. Therefore, it is extremely important that transmission line faults from various sources be identified accurately, reliably and be corrected as soon as possible. In this paper, a comparative study of the performance of Fourier transform and wavelet transform based methods combined with Neural Network (NN) for location estimation of faults on high voltage transmission lines is presented. A new location method is proposed for decreasing training time and dimensions of NN. The proposed algorithms are based on Fourier transform analysis of fundamental frequency of current and voltage signals in the event of a short circuit on a transmission line. Similar analysis is performed on transient current and voltage signals using multi-resolution Daubchies-9 wavelet transform, and comparative characteristics of the two methods are discussed.

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Comparison of fourier & wavelet transform methods for transmission line fault classification

Cited by 30 publications

References 14 publications

Classification of short-circuit faults in high-voltage energy transmission line using energy of instantaneous active power components-based common vector approach

Classification of short-circuit faults in high-voltage energy transmission line using energy of instantaneous active power components-based common vector approach

Detection and classification of faults on 220 KV transmission line using wavelet transform and neural network

Transmission line fault location estimation by Fourier & wavelet transforms using ANN

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Comparison of fourier &amp; wavelet transform methods for transmission line fault classification

Cited by 30 publications

References 14 publications

Classification of short-circuit faults in high-voltage energy transmission line using energy of instantaneous active power components-based common vector approach

Classification of short-circuit faults in high-voltage energy transmission line using energy of instantaneous active power components-based common vector approach

Detection and classification of faults on 220 KV transmission line using wavelet transform and neural network

Transmission line fault location estimation by Fourier &amp; wavelet transforms using ANN

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Comparison of fourier & wavelet transform methods for transmission line fault classification

Transmission line fault location estimation by Fourier & wavelet transforms using ANN