High impedance fault detection on radial distribution and utilization systems

Sharaf, A.Μ.; El-Sharkawy, R.M.; Al-Fatih, R.; Al-Ketbi, M.

doi:10.1109/ccece.1996.548326

Cited by 6 publications

(4 citation statements)

References 3 publications

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“…One resistance represents the arc resistance, and the other represents the earth resistance. Subsequently, in [74], HIF is modeled by a resistance and an inductance, both non-linear as a function of the current, subject to a second-order polynomial, as can be observed in Figure 5 and the model "b" of Table 2. Similarly, in [75], HIF is simulated in two ways: (a) constant resistance and reactance; and (b) non-linear resistance as a function of current and constant reactance, as can be observed in Figure 5 and model "d" of Table 2.…”

Section: Models Based On Passive Circuit Elementsmentioning

confidence: 99%

High Impedance Fault Models for Overhead Distribution Networks: A Review and Comparison with MV Lab Experiments

Huaquisaca Paye,

Vieira,

Tabora

et al. 2024

Energies

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Detecting and locating high impedance faults (HIF) in overhead distribution networks (ODN) remains one of the biggest challenges for manufacturers and researchers due to the complexity of this phenomenon, where the electrical current magnitude is similar to that of the loads. To simulate HIF, the selection of the HIF model is important, because it has to correctly reproduce the characteristics of this phenomenon, so that it does not negatively influence the simulations results. Therefore, HIF models play a fundamental role in proposing solutions and validating the effectiveness of the proposed methods to detect and localize HIF in ODN. This paper presents a systematic review of HIF models. It is intended to facilitate the selection of the HIF model to be considered. The models are validated based on experimental data from medium voltage (MV) laboratories, specifically, recorded waveforms from two HIF tests conducted in an MV lab were analyzed and compared with three established HIF models. The efficacy of these models was assessed against MV lab test data to ensure a precise representation of both transient and steady-state conditions for fault conductance and current waveforms. The findings show that the two nonlinear resistor models better approximate the waveforms obtained in the experimental tests performed in this study.

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Section: Models Based On Passive Circuit Elementsmentioning

confidence: 99%

High Impedance Fault Models for Overhead Distribution Networks: A Review and Comparison with MV Lab Experiments

Huaquisaca Paye,

Vieira,

Tabora

et al. 2024

Energies

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“…Mamishev et al (1996) suggested using fractal geometry to analyze the chaotic properties of high-impedance defects and RMS current values are used to classify the behavior of the temporal system, resulting in a relatively short time series usable for study. Sharaf et al (1996) used a relay-based mechanism for HIF detection. The scheme utilizes the ripple frequencies and sub-and superharmonics usually associated with the HIF phenomenon.…”

Section: Miscellaneous Hif Detection Schemesmentioning

confidence: 99%

Application of signal processing techniques and intelligent classifiers for high-impedance fault detection in ensuring the reliable operation of power distribution systems

Subathra

George

et al. 2023

Front. Energy Res.

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High-impedance fault (HIF) is always a threat and the biggest challenge in the power transmission and distribution system (PTDS). For a PTDS to operate effectively, HIF diagnosis is essential. However, given the HIF’s nature and the involved complexity, detection, identification, and fault location are difficult. This will be even more complicated in conventional PTDSs as they are inefficient and highly vulnerable. Given the importance and urgent need for HIF diagnosis in PTDS, this study reviews state-of-the-art HIF phenomenon and detection techniques and proposes the use of “various signal processing techniques for fault feature extraction” and “ different classifiers for identifying HIF.” First, HIF current/voltage signals are analyzed using signal processing techniques, which include the discrete wavelet transform (DWT), pattern recognition, Kalman filtering, TT transform, mathematical morphology (MM), S transform (ST), fast Fourier transform (FFT), principal component analysis (PCA), linear discriminant analysis (LDA), and wavelet transforms, such as dual-tree, maximum overlap discrete wavelet transform (MODWT), and lifting wavelet transform (LWT). Second, the various HIF and non-HIF faults are classified using intelligent classifiers. The intelligent classifiers include artificial neural networks (ANNs), probabilistic neural networks (PNNs), genetic algorithms (GAs), fuzzy logic, adaptive neuro-fuzzy interface system, support vector machine (SVM), extreme learning machine (ELM), adaptive resonance theory, random forests (RFs), decision trees (DTs), and convolution neural networks (CNNs). In addition to the comparative discussion of various classifier techniques, their evaluation criterion and performance are prioritized. Third, this review also studied different test systems, such as radial distribution network, mesh distribution network, IEEE 4 node, IEEE 13 node feeder, IEEE 34 node feeder, IEEE 39 node feeder, IEEE 123 node feeder, Palash feeder, and test microgrid systems, to assess the pertinence of various HIF detection schemes and the behavior along with methods to locate the HIF. Overall, we believe this review would serve as a comprehensive compendium of advanced techniques for HIF diagnosis in different test systems.

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“…The combination of the energy and the random nature of the arc phase current signature was embedded into an algorithm [69]. Moreover, the in-depth usage of harmonics for fault detection was presented in [70], where quasi-ripples and sub/super harmonic states were involved in developing a detection scheme.…”

Section: Classical Detection System For Hifsmentioning

confidence: 99%

“…Alternatively, some less utilized algorithms are also mentioned in the literature, such as complex neural fuzzy networks [70,102,103], the fuzzy logic method [104], genetic algorithms [105], and a combination of fuzzy and genetic algorithms [106]. These algorithms require appropriate measurement and analysis to perform efficiently, however, and are comparatively difficult to implement in actual setups.…”

Section: Modern Detection Approaches For Hifsmentioning

confidence: 99%

Application of GMR Sensors to Non-Contact Current Monitoring, Fault Detection and Classification in Electricity Distribution Networks

Sifat¹

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<p> A reliable uninterrupted electricity supply is essential to a functioning society and a growing economy. Traditionally, low voltage distribution networks (LVDN) are less well monitored, despite being more extensive. The reliability, cost, and compatibility of sensors are the primary constraints to implementing a large-scale monitoring system at LVDN. Faults at this level have the potential to damage public property and cause fatal accidents, but their detection is complicated by the characteristics of some faults. Therefore, this thesis’s overall goal is to evaluate the feasibility of non-contact giant magneto resistive (GMR) sensors for LVDN overhead line monitoring. Two applications were evaluated: 1) non-contact current sensing and 2) fault detection and classification. A review of low and high impedance faults, their characteristics, and the existing detection methods indicates that fault detection schemes and characterization have mainly focused on the high voltage levels. In this thesis, a novel 400-volt (3φ-neutral) physical test facility is developed with proper personnel safety and system security following the industry guidelines, to generate fault and system event data for analysis. Cross-coupled alternating current (AC) magnetic fields from the overhead line arrangement were captured via a GMR sensor. Three typical off the shelf single-axis GMR sensors were used to develop a 3-axis GMR sensor head to capture the spatial magnetic fields. The sensors were individually calibrated with respect to one single true calibrated sensor. This thesis describes the procedure and for achieving the correct alignment of the sensor output with the AC phase direction. A method for fault detection and classification using GMR sensor measurements is also presented in this thesis. This was developed using deep learning algorithms. Two supervised deep learning algorithms were evaluated to classify the fault and system event signatures from the magnetic field measurements. The analysis shows a hybrid model designed with a convolution neural network and gate recurrent unit performed best. This followed by a proposed decision framework is shown to detect and classify faults and normal system events with high security and reliability. In addition to the detection of faults, this thesis also confirms the feasibility of using non-contact calibrated GMR sensors for overhead line current sensing. The analysis shows a minimum of two vertically placed 2D sensor heads are required to calculate the individual phase current data from magnetic field measurements. Compared to measured data, this calculation was found to be more than 90% accurate. In summary, this thesis presents the differentiating characteristics of faults and system events observed in experiments in a purpose-built 400-volt network simulation facility. Non-contact GMR sensors are shown to be an alternative to traditional current sensors for LVDN current sensing, and for fault detection and classification via deep learning (DL)-based methods. </p>

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High impedance fault detection on radial distribution and utilization systems

Cited by 6 publications

References 3 publications

High Impedance Fault Models for Overhead Distribution Networks: A Review and Comparison with MV Lab Experiments

High Impedance Fault Models for Overhead Distribution Networks: A Review and Comparison with MV Lab Experiments

Application of signal processing techniques and intelligent classifiers for high-impedance fault detection in ensuring the reliable operation of power distribution systems

Application of GMR Sensors to Non-Contact Current Monitoring, Fault Detection and Classification in Electricity Distribution Networks

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