High-Impedance Fault Detection Method Based on Stochastic Resonance For a Distribution Network With Strong Background Noise

Wang, Xiaowei; Wei, Xiangxiang; Gao, Jie; Song, Guobing; Kheshti, Mostafa; Guo, Liang

doi:10.1109/tpwrd.2021.3075472

Cited by 37 publications

(16 citation statements)

References 34 publications

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“…where, S(t) is the voltage signal extracted. The average error 𝜓 cp is calculated and shown in Equation (15).…”

Section: Choice Of Sampling Frequencymentioning

confidence: 99%

“…The average error 𝜓 cp is calculated and shown in Equation (15). In Table 3, the average error is calculated for different sampling frequencies at different levels of decomposition keeping the limit of 2N as consideration, where N is number of samples.…”

Section: Choice Of Sampling Frequencymentioning

confidence: 99%

“…Ledesma et al proposed a method to locate HIF by using neural networks and it is discussed in Reference 14. Time‐domain and frequency‐domain combination algorithms are discussed in Reference 15, whereas time‐scale and frequency domain algorithm is given in Reference 16. Most research works discussed use either artificial intelligent based classifiers or machine learning based classifiers for pattern recognition.…”

Section: Introductionmentioning

confidence: 99%

“…A new method based on piecewise linear fitting technique to solve state equations for detecting arc HIFs and it is discussed in Reference 25. Wang et al proposed a new method to detect HIF in distribution network based on stochastic resonance and with combination of variational mode decomposition method and it is discussed in Reference 15. An empirical WT based detection of HIF is discussed in Reference 26.…”

Section: Introductionmentioning

confidence: 99%

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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

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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.

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“…where, S(t) is the voltage signal extracted. The average error 𝜓 cp is calculated and shown in Equation (15).…”

Section: Choice Of Sampling Frequencymentioning

confidence: 99%

Section: Choice Of Sampling Frequencymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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

show abstract

“…HILP events are relatively rare, so the data distribution characteristics in multiple extreme scenarios should be drawn attention and used as the basis for constructing metrics that can give prominence to the HILP events better. Recently, in the field of electric power research, kurtosis and skewness have been introduced in fault detection [15], wind power curve modelling [16], network expansion planning [17] etc., which are usually associated with wind power uncertainties. However, they have not been widely introduced into the studies related to resilience.…”

Section: Introductionmentioning

confidence: 99%

Resilience assessment for natural gas systems and electrical power systems without complete integration

Xie

Jiang

et al. 2022

IET Generation Trans & Dist

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Considering the limited information sharing between natural gas systems (NGS) and electrical power systems (EPS), this paper proposes a distributed resilience assessment method for NGS and EPS. First, a coordinated restoration framework is presented for incompletely integrated energy systems, and the collaborative benefit redistribution mechanism is proposed to motivate the participation of the cooperative restoration. Then fragility and recovery models for NGS and EPS components are developed, involving the impact of wind and rainfall during typhoons. An emergency load shedding model considering the conditional value at risk under typhoons is built, where the dynamic constraints of gas storage in NGS's pipelines is considered, and a linear approximation method is adopted to make the solution feasible. The modified alternating direction method of multipliers algorithm is used to solve the problem, and herein the self-adaptive penalty factor is leveraged to boost the efficiency. Based on the valley-shaped resilience model, the concept of skewness and kurtosis are introduced to give risk-adverse metrics more systematically to the impact of extreme events. The proposed model and algorithm are validated by case studies. Except for the performance exploration, the weakness and enhancement suggestions are also concluded from the case analysis.

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High-impedance fault detection method based on sparse data divergence discrimination in distribution networks

Cui

Yang

Wang

et al. 2023

Electric Power Systems Research

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High-Impedance Fault Detection Method Based on Stochastic Resonance For a Distribution Network With Strong Background Noise

Cited by 37 publications

References 34 publications

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

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

Resilience assessment for natural gas systems and electrical power systems without complete integration

High-impedance fault detection method based on sparse data divergence discrimination in distribution networks

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