Modeling and Experimental Verification of a High Impedance Arcing Fault in MV Networks

Elkalashy, Nagy I.; Lehtonen, Matti; Darwish, H.A.; Izzularab, Mohamed A.; Taalab, Abdel-Maksoud I.

doi:10.1109/psce.2006.296225

Cited by 18 publications

(27 citation statements)

References 12 publications

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“…However, a threshold value equal to 1.0 is suggested to discriminate between this fault case and the measurement noise. This is evident with the aid of the experimental waveforms of this fault current and voltage illustrated in Reference [1]. In this reference, the experimental waveforms were processed using DWT with the same mother wavelet db14 for comparison purposes.…”

Section: Fault Detectionmentioning

confidence: 99%

“…For the considered case study, the resistance is equal to 140 kV and the arc is modeled according to the thermal equilibrium that is adapted as follows [1,11]: where g is the time-varying arc conductance, G is the stationary arc conductance, i j j is the absolute value of the arc current, U arc is a constant arc voltage parameter, t is the arc time constant, and A and B are constants. In Reference [1], the parameters U arc , A, and B were found to be 2520V, 5.6E-7, and 395917, respectively. Considering the conductance at each zero crossing, the dielectric is represented by a variable resistance until the instant of reignition.…”

Section: Simulated Systemmentioning

confidence: 99%

“…It was found that the fault features are extracted using the experimental data using details d3 and d4 and there is good agreement with the simulation results. By applying the discriminator S U Equation (5) on the DWT details d3 of the experimental fault voltages in Reference [1], it was found that a threshold value of 1.0 can discriminate between the fault features and noise.…”

Section: Fault Detectionmentioning

confidence: 99%

“…These faults cannot be detected by conventional relays due to the high resistances of the trees [1].…”

Section: Introductionmentioning

confidence: 99%

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A novel selectivity technique for high impedance arcing fault detection in compensated MV networks

Elkalashy¹,

Lehtonen²,

Darwish

et al. 2007

Int Trans Elec Energy Syst

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SUMMARYIn this paper, the initial transients due to arc reignitions associated with high impedance faults caused by leaning trees are extracted using discrete wavelet transform (DWT). In this way, the fault occurrence is localized. The feature extraction is carried out for the phase quantities corresponding to a band frequency 12.5-6.25 kHz. The detection security is enhanced because the DWT corresponds to the periodicity of these transients. The selectivity term of the faulty feeder is based on a novel technique, in which the power polarity is examined. This power is mathematically processed by multiplying the DWT detail coefficients of the phase voltage and current for each feeder. Its polarity identifies the faulty feeder. In order to reduce the computational burden of the technique, the extraction of the fault features from the residual components is examined. The same methodology of computing the power is considered by taking into account the residual voltage and current detail coefficients where the proposed algorithm performs best. Test cases provide evidence of the efficacy of the proposed technique.

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Section: Fault Detectionmentioning

confidence: 99%

Section: Simulated Systemmentioning

confidence: 99%

Section: Fault Detectionmentioning

confidence: 99%

“…These faults cannot be detected by conventional relays due to the high resistances of the trees [1].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A novel selectivity technique for high impedance arcing fault detection in compensated MV networks

Elkalashy¹,

Lehtonen²,

Darwish

et al. 2007

Int Trans Elec Energy Syst

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“…Discrete wavelet transformer technique in [2], [6] and [7], artificial neural network in [4] is used to detection of HIF. Besides, some others such as [1] and [8] used low frequency harmonic analysis to detection.…”

Section: Introductionmentioning

confidence: 99%

A New Approach to High Impedance Fault Detection Based on Correlation Functions

Faridnia

Samet

Dezfuli

2012

IFIP Advances in Information and Communication Technology

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Abstract. Detection of High Impedance Fault (HIF) that is mainly occurred in MV power systems has been a challenge due to its low current magnitude. A new HIF detection approach based on correlation functions is applied on the voltage and current signals in this paper. Twelve indices based on correlation functions are implemented and tried on a lot of cases. Some of them which have clearly different values for no-fault and HIF conditions are chosen. Simulation results prove the efficiency of proposed method for HIF detection.

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DWT‐based extraction of residual currents throughout unearthed MV networks for detecting high‐impedance faults due to leaning trees

Elkalashy¹,

Lehtonen²,

Darwish

et al. 2007

Int Trans Elec Energy Syst

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Modelling of a high-impedance arcing fault due to a leaning tree in medium voltage (MV) networks was experimentally verified and the network transients due to this fault were also investigated. Even though the tree had a very high resistance value, the initial transients were periodically caused by the arc reignitions after each zero-crossing. In this paper, these features are extracted from residual currents using discrete wavelet transform (DWT) to localise this fault event. The DWT performance at different measuring nodes throughout an unearthed 20 kV network can be gathered at the base station using wireless sensors concept. So, the DWT is evaluated for a wide area of the network and the fault detection is confirmed by numerous DWT extractors. Due to the periodicity of arc reignitions, the initial transients are localised not only at fault starting instant but also during the fault period that will enhance the detection security. The term of locating the faulty section is determined based on ratios of the residual current amplitudes. The fault cases are simulated by ATP/EMTP and the arc model is implemented using the universal arc representation. the residual waveforms is obvious and can be used for detecting the fault. The most suitable signal processing technique for localising these initial transients is DWT. DWT-BASED FAULT DETECTIONWavelets are families of functions generated from one single function, called the mother wavelet, by means of scaling and translating operations. The scaling operation is used to dilate and compress the mother wavelet to obtain the respective high and low frequency information of the function to be analysed. Then the translation is used to obtain the time information. In this way a family of scaled and translated wavelets is created and it serves as the base for representing the function to be analysed [19]. Figure 4. Enlarged view of residual current waveforms (i r ) when the fault occurred in section EF.Figure 7. The detector S di of the details shown in Figure 6. (a) S di of the residual current details in section AB (i r(AB) ), (b) S di of the residual current details in section Bc (i r(BC) ), (c) S di of the residual current details in section BD (i r(BD) ), (d) S di of the residual current details in section BE (i r(EB) ), (e) S di of the residual current details in section EK (i r(EK) ), (f) S di of the residual current details in section EF (i r(EF) ).

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Modeling and Experimental Verification of a High Impedance Arcing Fault in MV Networks

Cited by 18 publications

References 12 publications

A novel selectivity technique for high impedance arcing fault detection in compensated MV networks

A novel selectivity technique for high impedance arcing fault detection in compensated MV networks

A New Approach to High Impedance Fault Detection Based on Correlation Functions

DWT‐based extraction of residual currents throughout unearthed MV networks for detecting high‐impedance faults due to leaning trees

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