Automatic fault location for underground low voltage distribution networks

Navaneethan, S.; Soraghan, John J.; Siew, W.H.; McPherson, F.; Gale, P.

doi:10.1109/61.915506

Cited by 45 publications

(15 citation statements)

References 10 publications

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“…These types of fault in wiring harnesses are not detectable by standard TDR techniques. Other techniques have been investigated for finding faults in aircraft harnesses [1], communication lines [2] and power cables [3].…”

Section: Introductionmentioning

confidence: 99%

Modelling of fault detection in electrical wiring

Shirkoohi¹

2014

9th IET International Conference on Computation in Electromagnetics (CEM 2014)

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Electrical wiring and cables in many cases buried deep in many systems and environments suffer damage. In many cases the damage only affects wire insulation and is not usually detected under normal operating conditions. This paper discusses computer modelling of electrical wires based on TDR using trains of successive pulses which were used in order to develop techniques and test procedures that would lead to identification of these types of fault.

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Section: Introductionmentioning

confidence: 99%

Modelling of fault detection in electrical wiring

Shirkoohi¹

2014

9th IET International Conference on Computation in Electromagnetics (CEM 2014)

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“…These types of faults in wiring harnesses are not detectable by standard TDR techniques. Other techniques have been investigated for finding faults in aircraft harnesses [1,2], communication lines [3] and power cables [4]. More advanced techniques and elaborate models were also developed in order to detect and characterise defects in cables [5][6][7][8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Modelling of fault detection in electrical wiring

Shirkoohi¹

2015

IET Science, Measurement & Technology

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Electrical wiring and cables, in many cases buried deep, in many systems and environments suffer damage. In some cases the damage only affects wire insulation and is not usually detected under normal operating conditions. This study discusses computer modelling of electrical wires, based on time-domain reflectometry using trains of successive pulses which were used in order to develop techniques and test procedures that would lead to identification of these types of faults. This study shows detailed simulations of defects in insulation of electrical wires and in cable shields, using dedicated electromagnetic modelling computation software. The results of simulations were compared with those obtained in experimental measurement for wires and cables of several metres in length. Comparison of the analysed measurement results for this novel technique shows clear indication of its relevance to flaw detection in electrical wires, guided through simulation.

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“…Time-domain reflectometry (TDR) is one of the most common travelling wave-based methods used for locating faults on transmission lines [9], [10]. In distribution systems TDR recorded signals are not easy to interpret due to reflections from the many tee connections in the network [19]. Recently a new approach by mapping TDR in frequency domain using continuous wavelet transform (CWT) showed reduction in the complexity of TDR-based fault location methods in distribution systems [16], [17].…”

Section: Introductionmentioning

confidence: 99%

“…This minimizes the interruption time, restores power and reduces costs. Several fault-locating methods have been proposed based on various techniques of power frequency wave analysis [1]- [8] and travelling wave theory [9]- [19]. The application of traditional techniques that estimate the line impedance using fundamental voltages and currents at main feeder is impractical for distribution systems with complex nature.…”

Section: Introductionmentioning

confidence: 99%

Path Characteristic Frequency-Based Fault Locating in Radial Distribution Systems Using Wavelets and Neural Networks

Pourahmadi-Nakhli

Safavi

2011

IEEE Trans. Power Delivery

141

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This paper proposes a new algorithm for localizing phase to ground faults in an electric power distribution system. Fault-originated traveling waves propagate along the distribution paths in both directions away from the fault point and are reflected at line terminations, junctions between feeders, laterals, and the fault location. Depending on the paths which traveling waves reciprocate through, the transient signal at each node contains certain path characteristic frequencies (PCFs). Energy spectrum of the transient signal has high density around the path characteristic frequencies. On this basis, the transient voltage is decomposed by a wavelet filter and its energy spectrum is decomposed into different levels. Depending on the bandwidth of the wavelet filter and the path characteristic frequencies, the decomposed signal in each level contains a certain percentage of energy. Then, a neural network-based methodology is proposed as a power distribution line fault locator. Energy percentage in each level is the candidate feature for training the artificial neural network. Simulations and the training process for the neural network are performed respectively using ATP/EMTP and MATLAB. It is shown that the results of the proposed algorithm are quite satisfactory. Index Terms-Artificial neural network, fault location, path characteristic frequency, wavelet transform.

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Automatic fault location for underground low voltage distribution networks

Cited by 45 publications

References 10 publications

Modelling of fault detection in electrical wiring

Modelling of fault detection in electrical wiring

Modelling of fault detection in electrical wiring

Path Characteristic Frequency-Based Fault Locating in Radial Distribution Systems Using Wavelets and Neural Networks

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