Design and implementation of an adapative single pole autoreclosure technique for transmission lines using artificial neural networks

Fitton, D.S.; Dunn, R.W.; Aggarwal, R.K.; Johns, A.T.; Bennett, A.

doi:10.1109/61.489331

Cited by 86 publications

(34 citation statements)

References 3 publications

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“…References [9][10][11] present different adaptive auto-reclosure approaches based on artificial neural networks (ANN). In Reference [9], by use of Fourier transform, various components of the faulted phase voltage is extracted and applied to train ANN by more than 25 000 permanent and transient single-phase-to-earth faults.…”

Section: Introductionmentioning

confidence: 99%

“…In Reference [9], by use of Fourier transform, various components of the faulted phase voltage is extracted and applied to train ANN by more than 25 000 permanent and transient single-phase-to-earth faults. In Reference [10] the short time Fourier transform (STFT) is employed to extract proper feature vectors, which are the energy of voltage waveform in five frequency bands and are applied in training the ANN. The network has five inputs, one hidden layer and an output layer with one node.…”

Section: Introductionmentioning

confidence: 99%

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Adaptive single pole auto-reclosing using discrete wavelet transform

Jamali

Ghaffarzadeh

2010

Euro. Trans. Electr. Power

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SUMMARYThis paper proposes a new algorithm for adaptive single pole auto-reclosing (SPAR) using discrete wavelet transform (DWT). The faulted phase voltage signal is captured at a sampling rate of 2.5 kHz. The sym8 wavelet function is used to decompose the signal into level 1 and to define an index function (INDF), which is obtained from the sum of absolute values of detail coefficients of the captured signal at the level 1 resolution. Finally, a decision function (DEF) is defined by computing the second derivative of the INDF. By evaluating the sign of the DEF, transient (arcing) faults are distinguished from permanent faults and also the secondary arc extinction instant can be identified. The significant advantage of the proposed algorithm is that it does not require any threshold level and therefore its performance is independent of fault location, line parameters and operating conditions. Furthermore, it can be used in transmission lines with reactor compensation. The proposed method has been successfully tested under a variety of fault location and load angle on a 400 kV overhead line, with and without reactor compensation, using the Electro-Magnetic Transient Program. The test results validate the algorithm ability in distinguishing between arcing and permanent faults and determining the instant of secondary arc extinction.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Adaptive single pole auto-reclosing using discrete wavelet transform

Jamali

Ghaffarzadeh

2010

Euro. Trans. Electr. Power

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“…These methods, firstly, determine whether a fault is permanent or transient, and if the fault is in transient nature, its secondary arc extinction time is estimated. Generally, voltage waveforms at the sending and receiving ends of TLs are used for the analyses [4][5][6][7][8][9][10][11][12]. In some studies, faulted phase currents are also used for successful operation [6].…”

Section: Introductionmentioning

confidence: 99%

An Adaline based arcing fault detection algorithm for single-pole autoreclosers

Karacasu

Hocaoğlu

2011

Electric Power Systems Research

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“…Various applications of neural networks were used in the past to improve some of the standard functions used in protection of transmission lines. They have been related to fault classification [3]- [9], fault direction discrimination [10]- [12], fault section estimation [8], [13], [14], adaptive relaying [15], [16], autoreclosing [17], [18], and fault diagnosis [19], [20]. The applications are mainly based on widely used Multilayer Perceptron (MLP) feed-forward networks [1]- [8], [11], [13], [15], [17]- [19], [21], and in recent years on Radial Basis Function (RBF) [5], [6], [9], [14], [22], Self-Organizing Maps (SOM) [5], [16], [23], Learning Vector Quantization (LVQ) [5], [23], Adaptive Resonance Theory (ART) [20], [24], Recurrent [12], [25], Counterpropagation [5], [26], and Finite Impulse Response neural networks [10], [27].…”

Section: Introductionmentioning

confidence: 99%

Fuzzy ART Neural Network Algorithm for Classifying the Power System Faults

Vasilic

Kezunovic

2005

IEEE Trans. Power Delivery

119

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Abstract-This paper introduces advanced pattern recognition algorithm for classifying the transmission line faults, based on combined use of neural network and fuzzy logic. The approach utilizes self-organized, supervised Adaptive Resonance Theory (ART) neural network with fuzzy decision rule applied on neural network outputs to improve algorithm selectivity for a variety of real events not necessarily anticipated during training. Tuning of input signal preprocessing steps and enhanced supervised learning are implemented, and their influence on the algorithm classification capability is investigated. Simulation results show improved algorithm recognition capabilities when compared to a previous version of ART algorithm for each of the implemented scenarios.

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Design and implementation of an adapative single pole autoreclosure technique for transmission lines using artificial neural networks

Cited by 86 publications

References 3 publications

Adaptive single pole auto-reclosing using discrete wavelet transform

Adaptive single pole auto-reclosing using discrete wavelet transform

An Adaline based arcing fault detection algorithm for single-pole autoreclosers

Fuzzy ART Neural Network Algorithm for Classifying the Power System Faults

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