Transmission line fault location using traveling wave frequencies and extreme learning machine

Akmaz, Düzgün; Mamiş, Mehmet Salih; Arkan, Müslüm; Tağluk, M. Emin

doi:10.1016/j.epsr.2017.09.019

Cited by 76 publications

(32 citation statements)

References 19 publications

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“…Fault location techniques can be classified into impedance measurement, traveling wave, and artificial intelligence. The impedance‐based techniques require data that make them economically advantageous . However, these techniques depend on the characteristics of the transmission lines, influenced by the value of the fault resistance, and it may cause either under‐reach or over‐reach to the relay.…”

Section: Introductionmentioning

confidence: 99%

“…Moreover, the act of such relays was caused deterioration by the interference of the harmonics in the transient period . System distortions and faults at the inception angle close to zero also should be taken into consideration . The traveling wave techniques analyze the position‐time graphs to predict the fault location.…”

Section: Introductionmentioning

confidence: 99%

“…These techniques are uninfluenced by the network configuration, load variance, high earth resistance, and series capacitor, as they analyze the timing diagram of the movements of the wave, yet, they require high sampling frequency. While artificial intelligence‐based techniques apply some artificial intelligence to locate the fault .…”

Section: Introductionmentioning

confidence: 99%

“…Besides, it has the advantages of being not affected by mutual coupling, current transformer (CT) saturation, load switching, unbalanced load switching, fault, and previous fault conditions, close up faults, remote end faults, and low inception time faults. For further verifying the proposed relay performance, a comparison is done with schemes proposed in the novel references .…”

Section: Introductionmentioning

confidence: 99%

“…Reference mentioned that it had the advantages of (i) uninfluenced by fault resistance, fault type, or source inductance; (ii) used the current data only; (iii) used single‐end information; (iv) no need for synchronization equipment; (v) it can be applied to different transmission line characteristics, length, and voltage. The proposed relay has all the aforementioned advantages in addition to having the following advantages compared through reference : (i) there is no need for information concerning the source inductance; (ii) there is no need for the training data; (iii) more accurate, the error of the estimated distance reaches 10.21 km in reference while it is less than 1 km using the proposed fault location…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Ultra‐High‐Speed Non‐Unit Protection and Fault Location for Parallel Transmission Lines Earth Faults

Mourad

Shehab-Eldin

2019

IEEJ Transactions Elec Engng

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This article proposes a powerful ultra-high-speed technique for earth fault distance protection of double-circuit power transmission lines. The proposed technique uses the zero and positive sequence of the current signals. The technique presents a discriminating method based on the appearance of zero sequences, the zero sequence magnitude, and the time arrival instants of zero and positive sequence at the relaying point. The suggested technique consists of three stages. Stage 1; the polarity technique, which is designed to detect and classify faults within the protected zone. Stage 2; the ratio technique, which is designed to identify in which circuit the fault is involved. Stage 3; it is employed to calculate the distance of faults. Extensive numbers of simulations were conducted to test the action of the proposed technique. Results show that the technique provides a speed, secure, and dependable protection technique of double-circuit transmission lines even for remote fault, high resistance fault, and a low inception time fault. They also avoid the unbalanced load-switching effect and the current transformer (CT) saturation problems.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Ultra‐High‐Speed Non‐Unit Protection and Fault Location for Parallel Transmission Lines Earth Faults

Mourad

Shehab-Eldin

2019

IEEJ Transactions Elec Engng

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Effective fault distance estimation and diagnosis in the PV based DC‐ring network using Hilbert Huang transform and weighted online sequential random vector functional network

Anjaiah

Dash

2021

Int Trans Electr Energ Syst

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This paper proposes a novel approach for fault detection, classification, and distance estimation of five kinds of faults (ie, cable and Arc faults) in the PV-based DC-ring microgrid. The proposed approach is based on the Hilbert Huang Transform (HHT) and online sequential random vector functional link network (OS-RVFLN). Initially, differential current signals are passed through empirical mode decomposition (EMD) to yield intrinsic mode functions (IMFs). Among the series of IMFs, more efficient IMFs are selected based on the threshold of maximum weighted kurtosis index (M-WKI) value for fault diagnosis. The selected IMFs are passed through Hilbert transform (HT) for determining the instantaneous frequency, magnitude spectrum, and Hilbert energy. Then, the Teager energy operator (TEO) is applied to the more effective IMF among the selected IMFs of each DC fault for fault detection. The most suitable target features are extracted from the selected efficient IMFs. This feature set data is initialized to the proposed HHT-OSRVFLN algorithm for five kinds of faults classification and its location estimation. The performance of the proposed algorithm in the proposed microgrid is proved in terms of low fault detection time, superior classification accuracy, and low relative error through different case studies by consuming minimum computation time in MATLAB/Simulink environment. Finally, the digital architecture of the proposed method is designed and simulated in Xilinx ISE 14.5 software environment to estimate the fault location. The Chip scope hardware debugging output proves the correctness, simplicity, feasibility, practicability, and robustness of the proposed method for online fault diagnosis.List of Symbols and Abbreviations: R f , fault resistance; Idc 1 , Idc 2 , DC currents; Vdc 1 , Vdc 2 , DC voltages; α 1 , α 2 , attenuation constants; ωd 1 , ωd 2 , damping frequencies; ω n1 , ω n2 , natural frequencies; I 3 , differential current; I avg , average current; SD (k), squared difference; M e (t), mean envelope; E [C(t)], Teager energy of the signal; ω(t), instantaneous frequency; θ(t), phase angle; I b , best IMF; H, hidden layers; β 0 , output weight matrix; Z, output of the signal; d pfd , predicted fault distance; d actl , actual fault distance; t c , computation time; CC, correlation coefficient; EMD, empirical mode decomposition; FPGA, field programmable gate array; HHT, Hilbert Haung transform; ICON, integrator controller; ILA, integrated logic analyzer; IMF, intrinsic mode functions; KI, Kurtosis index; MAC, multiply and accumulate unit; OS-RVFLN, online sequential random vector functional link; TEO, Teager energy operator; VHDL, hardware description language; WKI, weighted kurtosis index.

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Fault detection and classification in overhead transmission lines through comprehensive feature extraction using temporal convolution neural network

Tunio,

Hashmani,

Khokhar

et al. 2024

Engineering Reports

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Faults in transmission lines cause instability of power system and result in degrading end users sophisticated equipment. Therefore, in case of fault and for the quick restoration of problematic phases, reliable and accurate fault detection and classification techniques are required to categorize the faults in a minimum time. In this work, 500 kV transmission line (Jamshoro‐New Karachi), Sindh, Pakistan has been modeled in MATLAB. The discrete wavelet transform (DWT) has been used to extract features from the transient current signal for different faults in 500 kV transmission line under various parameters such as fault location, fault inception angle, ground resistance and fault resistance and time series data has been obtained for fault classification. Moreover, the temporal convolutional neural network (TCN) is used for fault classification in 500 kV transmission network due to its robust framework. From simulation results, it is found that faults in 500 kV transmission line are classified with 99.9% accuracy. Furthermore, the simulation results of the TCN model compared to bidirectional long short‐term memory (BiLSTM) and Gated Recurrent Unit (GRU) and it has been found that TCN model is capable of classifying faults in 500 kV transmission line with high accuracy due to its ability to handle long receptive field size, less memory requirement and parallel processing due to dilated causal convolutions. Through this work, the meantime to repair of 500 kV transmission line can be reduced.

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Transmission line fault location using traveling wave frequencies and extreme learning machine

Cited by 76 publications

References 19 publications

Ultra‐High‐Speed Non‐Unit Protection and Fault Location for Parallel Transmission Lines Earth Faults

Ultra‐High‐Speed Non‐Unit Protection and Fault Location for Parallel Transmission Lines Earth Faults

Effective fault distance estimation and diagnosis in the PV based DC‐ring network using Hilbert Huang transform and weighted online sequential random vector functional network

Fault detection and classification in overhead transmission lines through comprehensive feature extraction using temporal convolution neural network

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