High Impedance Faults Location in the Distribution Networks using Adaptive Neuro-Fuzzy Inference System

Bouricha, Abdelhakim; Bouthiba, Tahar; Boukhari, Rebiha; Seghir, Samira

doi:10.1109/cistem.2018.8613583

Cited by 7 publications

(3 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Nevertheless, using this signal requires specific devices for the measurements, as it is not usual in protection schemes. V and I 128 samples per cycle EMTP b -RV [20] V and I 64 samples per cycle ATP [11] V and I 256 samples per cycle ATP [10] V and I 8000 samples per second ATP/Matlab [23] V and I 192 samples per cycle ATP/Matlab [21] V and I * Simulink/Matlab [12] V and I * ATP/Matlab [14] V and I 0 to 100 kHz * [7] V and I * * [13] V Table 2 also shows different sampling frequencies used in HIFLMs. When using a low sampling rate, only the HIF characteristics that impact the signal's low-frequency components can be analyzed.…”

Section: Hif Representation Referencementioning

confidence: 99%

“…V magnitude V measurements [21] I and V magnitude Measurement relay [12] R-squared of the fitted lines I and V-lines modeling [7] Voltage and current RMS e and phase PMU i [11] LSE f and steps descent I and V [14] Impulse between devices Power line comunication [16] Low V unbalance Smart meters [24] Magnetic field's high-frequency phase Magnetic field sensor WT due to the narrow high-frequency support and subjective selection of the mother wavelet. Also, there is a resolution loss since the signal's samples are divided by two at each decomposition level [25], [36].…”

Section: Stage Iii: Metrics Used In Hiflmsmentioning

confidence: 99%

“…Distance Training [15] ANN and Gaussian process regression Distance Training [21] Adaptive neurofuzzy inference Distance Training [7] Artificial Neural Network Distance Training a IEDs-Intelligent Electronic Devices; b Comparison with a database; c Depends on curve fitting the total phase shifts at different distances obtained from finite element analysis; d Threshold pre-determined for the system, but it adapts; e WLS-Weighted least square; f ZSC-Zero-sequence current Additionally, Table 6 reveals that most techniques use an adaptive threshold (the threshold varies according to the situation/system without requiring direct intervention). The extensive use of adaptive thresholds occurs because most techniques compare measurements in the same system to determine the fault location.…”

Section: Ref Decision Algorithmmentioning

confidence: 99%

See 2 more Smart Citations

High Impedance Fault Location Methods: Review and Harmonic Selection-Based Analysis

Lopes

Menezes

Gomes

et al. 2023

IEEE Open J. Power Energy

View full text Add to dashboard Cite

High Impedance Faults (HIFs) are recurring events in electrical Distribution Systems (DSs) and occur by the contact between energized conductors and high impedance surfaces. HIFs may pose hazards to living beings and cause bushfires. However, the HIF protection has not been completely solved due to the small fault current and varying impedance, inhibiting traditional protection techniques from functioning correctly. In the literature, researchers have mainly focused on detection techniques. Thus, the development of HIF Location Methods (HIFLMs) is recent, and evidences for conclusive solutions are still lacking. Moreover, to this date, no existing study reviews the main challenges concerning HIFLMs in DSs. This paper proposes a systematic analysis of the common stages to design the main existing HIFLMs. The strategy is evaluating the similar characteristics that pose a common research path regarding challenges faced in real-world conditions. Additionally, this paper proposes a case study to assess the best input signals, metrics, and machine learning-based decision algorithms of a new HIFLM. The results are promising, with high identification rates, even in noisy conditions. The methodology can help to select the datasets for supervised learning-based HIFLM. Highlighting the state-of-art of current methods and support development of HIFLMs are this paper's main contributions.

show abstract

Section: Hif Representation Referencementioning

confidence: 99%

Section: Stage Iii: Metrics Used In Hiflmsmentioning

confidence: 99%

Section: Ref Decision Algorithmmentioning

confidence: 99%

See 1 more Smart Citation

High Impedance Fault Location Methods: Review and Harmonic Selection-Based Analysis

Lopes

Menezes

Gomes

et al. 2023

IEEE Open J. Power Energy

View full text Add to dashboard Cite

show abstract

A novel graph search and machine learning method to detect and locate high impedance fault zone in distribution system

Joga

Sinha

Maharana

2022

Engineering Reports

View full text Add to dashboard Cite

High impedance fault (HIF) is difficult to detect by conventional overcurrent protection relays due to the lower fault current values, which are normally lower than the normal current. A fast and reliable algorithm is required to detect this type of fault. This paper proposes a novel method for detecting the location of HIF fault zone in a distribution system by using a novel graph theory‐based zone detection technique along with a Random Search Multilevel Support Vector Machine (RSMSVM) algorithm to classify the faulted zone. Due to shift in‐variance property of “Dual Tree Complex Wavelet Transform (DTCWT),” which has been used, in this paper, to decompose the voltage/current waveform to collect the signature of the signals and feed to the optimized RSMSVM model for classifying fault zone. The proposed method is evaluated on the IEEE 33‐bus system and also IEEE 39 bus test system under normal and noisy conditions. The proposed method is also evaluated for distribution network with the integration of distributed generation.

show abstract

A Data-Driven-Based High Impedance Fault Location Method Considering Traveling Waves in Branched Distribution Networks

Baharozu,

Ilhan,

Soykan

2024

IEEE Access

View full text Add to dashboard Cite

High Impedance Faults Location in the Distribution Networks using Adaptive Neuro-Fuzzy Inference System

Cited by 7 publications

References 15 publications

High Impedance Fault Location Methods: Review and Harmonic Selection-Based Analysis

High Impedance Fault Location Methods: Review and Harmonic Selection-Based Analysis

A novel graph search and machine learning method to detect and locate high impedance fault zone in distribution system

A Data-Driven-Based High Impedance Fault Location Method Considering Traveling Waves in Branched Distribution Networks

Contact Info

Product

Resources

About