Adaptive reclosure using high frequency fault transients

Li, Youyi; Dong, Xiaowen; Bo, Zhiqian; Chin, N.F.; Yaozhang, Ge

doi:10.1049/cp:20010178

Cited by 6 publications

(2 citation statements)

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“…On the other hand, the ASPAR methods are more complicated to be applied. Some ASPAR methods utilise high frequency data requiring high sampling frequency of at least 100 kHz [6–8, 15, 16], which increase the computational burden that needs powerful processors.…”

Section: Introductionmentioning

confidence: 99%

Detection of secondary arc extinction for adaptive single phase auto‐reclosing based on local voltage behaviour

Jamali

Baayeh

2017

IET Generation, Transmission & Distribution

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Section: Introductionmentioning

confidence: 99%

Detection of secondary arc extinction for adaptive single phase auto‐reclosing based on local voltage behaviour

Jamali

Baayeh

2017

IET Generation, Transmission & Distribution

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“…Researchers, recognizing the numerous merits of the adaptive autoreclosure, have proposed a number of adaptive autoreclosure schemes. These include schemes which measure and compare the voltage of the tripped phase to that of the energized phases to initiate or prevent autoreclosing (Faried et al, 1998;Aggarwal et al, 1993), schemes which make use of various components of faulted voltage such as total harmonic distortion, dc component and rms value to achieve successful autoreclosing (Park et al, 2004;Megahed et al, 2003;Kim et al, 2000;Ahn et al, 2001;Ahn et al, 2006) and schemes which employ high frequency current and voltage signals (Bo et al, 1997;Youyi et al;2001, Aggarwal et al;1997, Chen et al;. One significant demerit of the aforementioned schemes is that the many causes of faults and the interplay of several factors such as line configuration, fault position, fault point on wave, prefault loading, source parameters, and atmospheric conditions which influence the actual waveforms of the secondary arc voltage are likely to hinder their effectiveness (Aggarwal et al, 1994).…”

Section: Introductionmentioning

confidence: 99%

Adaptive Single-Pole Autoreclosure Scheme Based on Wavelet Transform and Multilayer Perceptron

Frimpong¹,

Okyere²,

Anto³

2010

Jnl Sci Tech

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Adaptive autoreclosing is a fast emerging technology for improving power system marginal stability during faults. It avoids reclosing unto permanent faults and recloses unto transient faults only after the secondary arc has extinguished. The challenges that come with the application of the adaptive autoreclosing technology are enormous. To come to grips with these challenges, researchers have been proposing autoreclosure schemes which use artificial neural network (ANN), the reason being that ANNs have in recent years clearly demonstrated their ability in solving some long standing problems in power systems where conventional techniques have difficulty. This paper proposes one such scheme for single-pole autoreclosure. The scheme uses multilayer perceptron artificial neural network which decides whether a fault is transient or permanent based on the percentage of energy in detailed coefficients of Daubechies db4 mother wavelet of the power system faulted voltage. In the case of transient faults, the neural network further determines the optimal reclosure time. A multilayer perceptron neural network was developed to suit the input signal adopted for the scheme and trained using the Levenberg-Marquardt back-propagation technique. The scheme was simulated using the Electromagnetics Transient Programme (EMTP) and MATHLAB software. The results of the simulation show that the proposed ANN-based adaptive single-pole autoreclosure (AdSPAR) scheme is capable of distinguishing between permanent and transient faults and in the case of the latter, predict optimal reclosure times.

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