A detection method of high impedance arcing fault for distribution network with distributed generation based on
            <scp>CEEMDAN</scp>
            and
            <scp>TEO</scp>
            algorithm

Shu, Hongchun; Deng, Yaqi; Dong, Jun; Cao, Ping; Yang, Bo; Bo, Zhiqian

doi:10.1002/2050-7038.12926

Cited by 3 publications

(2 citation statements)

References 30 publications

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“…A methodology for HIF detecting was presented in [ 18 ]. The technique was based on EMD with adaptive noise applied to decompose the zero-sequence current signals to obtain the intrinsic mode function (IMF).…”

Section: Introductionmentioning

confidence: 99%

Efficient Methodology for Detection and Classification of Short-Circuit Faults in Distribution Systems with Distributed Generation

Santos

Faria

Lopes

et al. 2022

Sensors

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Fault detection and classification are crucial procedures for electric power distribution systems because they can minimize the occurrence of faults. The methods for fault detection and classification have become more problematic because of the significant expansion of distributed energy resources in distribution systems and the change in their currents due to the action of short-circuiting. In this context, to fill this gap, this study presents a robust methodology for short-circuit fault detection and classification with the insertion of distributed generation units. The proposal methodology progresses in two stages: in the former stage, the detection is based on the continuous analysis of three-phase currents, whose characteristics are extracted through maximal overlap discrete wavelet transform. In the latter stage, the classification is based on three fuzzy inference systems to identify the phases with disturbance. The short-circuit type is identified by counting the shorted phases. The algorithm for short-circuit fault detection and classification is developed in MATLAB programming environment. The methodology is implemented in a modified IEEE 34-bus test system and modeled in ATPDraw with three scenarios with and without distributed generation units and considering the following parameters: fault type (single-phase, two-phase, and three-phase), angle of incidence, fault resistance (high impedance fault and low impedance fault), fault location bus, and distributed generation units (synchronous generators and photovoltaic panels). The accuracy is greater than 94.9% for the detection and classification of short-circuit faults for more than 20,000 simulated cases.

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Section: Introductionmentioning

confidence: 99%

Efficient Methodology for Detection and Classification of Short-Circuit Faults in Distribution Systems with Distributed Generation

Santos

Faria

Lopes

et al. 2022

Sensors

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show abstract

“…▪ Non-linearity: the characteristic curve of voltage and current is non-linear ▪ Buildup: The size of the current progressively grows until it reaches its maximum value. ▪ Randomness: The instability of the fault circuit, induced by the oscillation of the tree branch, results in the fault current displaying stochastic characteristics [6,20]. To emulate these characteristics, researchers developed various HIF models.…”

mentioning

confidence: 99%

High Impedance Fault Detection in Distribution Feeder Based on Spectrum Analysis and ANN with Non-Linear Load

Allawi,

Hussain,

Wali

et al. 2024

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High Impedance Fault (HIF) detection in distribution networks is challenging for protection engineers, mainly because HIFs possess unique characteristics, including non-linearity, asymmetry, randomness, and relatively low fault current levels compared to the feeder load current. In this regard, the study proposes an approach to detect HIFs in a radial distribution feeder based on the spectrum analysis of current signals at the substation bus. The proposed method comprises two stages: signal decomposition and feature extraction. Fast Fourier Transform (FFT) is utilized for signal decomposition, followed by feature extraction. These features are subsequently used as input to an artificial neural network (ANN) to distinguish HIF from non-HIF events, such as linear and non-linear load switching, capacitor bank switching, and transformer energization. The proposed method's efficacy is rigorously evaluated under various dynamic conditions, demonstrating that the method can detect and differentiate HIFs from non-fault events with a high detection rate and high accuracy of 99.3%, irrespective of the HIF location and fault resistance.

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High impedance fault detection method in distribution network based on improved Emanuel model and DenseNet

Bai

Tang²,

Cheng

et al. 2022

Energy Reports

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A detection method of high impedance arcing fault for distribution network with distributed generation based on CEEMDAN and TEO algorithm

Cited by 3 publications

References 30 publications

Efficient Methodology for Detection and Classification of Short-Circuit Faults in Distribution Systems with Distributed Generation

Efficient Methodology for Detection and Classification of Short-Circuit Faults in Distribution Systems with Distributed Generation

High Impedance Fault Detection in Distribution Feeder Based on Spectrum Analysis and ANN with Non-Linear Load

High impedance fault detection method in distribution network based on improved Emanuel model and DenseNet

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