Fault location in unearthed 60 kV subtransmission network based on the travelling wave method

Benato, Roberto; Sessa, Sebastian Dambone; Rinzo, Giovanni; Poli, Michele; Quaciari, Cristiano

doi:10.23919/aeit.2017.8240494

Cited by 2 publications

(1 citation statement)

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“…Therefore, in order to locate the faulty section, the method, currently applied by the Italian TSO (Terna), consists of visual inspections by a helicopter or by a ground vehicle: alternatively, Terna carries out an outage of the entire faulted line, with a consequent repowering span by span until the fault section is identified [2,3]. In some previous works, the authors have proposed a travelling wave-based algorithm to overcome such limitations [4,5]. Even if the accuracy is very high, the requested sample frequency appears to be an issue for practical installations, because of the high cost of the fault location systems.…”

Section: Introductionmentioning

confidence: 99%

Overcoming the Limits of the Charge Transient Fault Location Algorithm by the Artificial Neural Network

2019

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In this paper, two algorithms for single-ended fault location are presented with reference to the unearthed sub-transmission Italian grid (with a voltage level of 60 kV). Both algorithms deal with the correlation between the ground capacitance charging frequency of sound phases and the fault position. In the former, the frequency response of a lumped parameter circuit in the Laplace domain is linked to the fault distance. With such a simplified lumped parameter circuit, the average error in locating a phase-to-ground (PtG) short circuit is 5.18% (total overhead line length equal to 60 km). Since this error is too high, another approach is presented. In this second algorithm, the frequency spectra of the transient current waveforms are used as a database for the training of an Artificial Neural Network (ANN). With this approach, the average error decreases significantly up to 0.36%. The fault location accuracies of the two proposed methods are compared in order to reveal their strengths and weaknesses. The developed procedures are applied to a single-circuit overhead line and to a double-circuit one, both modelled in the EMTP-rv environment, whereas the fault location algorithms are implemented in the MATLAB environment (for the ANN-based algorithm, the Deep Learning toolbox is used).

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Section: Introductionmentioning

confidence: 99%