Novel alpha-transform distance relaying scheme

Sharaf, A.Μ.; El-Sharkawy, R.M.; Talaat, H.; Badr, M.A.

doi:10.1109/ccece.1996.548262

Cited by 5 publications

(2 citation statements)

References 6 publications

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“…This is notable when remote source loading, fault resistance non-linearity, capacitive line currents, mutual coupling and back-feed effects are taken into consideration. HIF faults [3,4] are usually characterized by the ripple rich current harmonic content due to non-linearity and thus are abnormal events that frequently occur in distribution feeders.…”

Section: Introductionmentioning

confidence: 99%

Ensemble decision trees for high impedance fault detection in power distribution network

Samantaray

2012

International Journal of Electrical Power & Energy Systems

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Section: Introductionmentioning

confidence: 99%

Ensemble decision trees for high impedance fault detection in power distribution network

Samantaray

2012

International Journal of Electrical Power & Energy Systems

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“…This is notable when remote source loading, fault resistance nonlinearity, capacitive line currents, mutual coupling and back-feed effects are taken into consideration. HIF faults [3,4] are usually characterised by the ripple-rich current harmonic content because of nonlinearity and are thus abnormal events that frequently occur in distribution feeders. There are two types of HIFs: the active faults and the passive ones.…”

Section: Introductionmentioning

confidence: 99%

High impedance fault detection in power distribution networks using time–frequency transform and probabilistic neural network

Samantaray

Panigrahi

Dash³

2008

IET Gener. Transm. Distrib.

117

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An intelligent approach for high impedance fault (HIF) detection in power distribution feeders using advanced signal-processing techniques such as time-time and time-frequency transforms combined with neural network is presented. As the detection of HIFs is generally difficult by the conventional over-current relays, both time and frequency information are required to be extracted to detect and classify HIF from no fault (NF). In the proposed approach, Sand TT-transforms are used to extract time-frequency and time-time distributions of the HIF and NF signals, respectively. The features extracted using Sand TT-transforms are used to train and test the probabilistic neural network (PNN) for an accurate classification of HIF from NF. A qualitative comparison is made between the HIF classification results obtained from feed forward neural network and PNN with same features as inputs. As the combined signal-processing techniques and PNN take one cycle for HIF identification from the fault inception, the proposed approach was found to be the most suitable for HIF classification in power distribution networks with wide variations in operating conditions.

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Adaptive Kalman filter and neural network based high impedance fault detection in power distribution networks

Samantaray¹,

Dash²,

Upadhyay³

2009

International Journal of Electrical Power & Energy Systems

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Novel alpha-transform distance relaying scheme

Cited by 5 publications

References 6 publications

Ensemble decision trees for high impedance fault detection in power distribution network

Ensemble decision trees for high impedance fault detection in power distribution network

High impedance fault detection in power distribution networks using time–frequency transform and probabilistic neural network

Adaptive Kalman filter and neural network based high impedance fault detection in power distribution networks

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