Arc flash fault detection in wind farm collection feeders based on current waveform analysis

Wang, Bin; Ni, Jiang; Geng, Jianzhao; Lu, Yuanyuan; Xiang, Dong

doi:10.1007/s40565-016-0233-4

Cited by 10 publications

(5 citation statements)

References 23 publications

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“…ΔZ im = ΔZ in ΔZ jm = ΔZ jn ΔZ km = ΔZ kn (24) where i, j, and k are respectively upstream, downstream and parallel nodes of m-n. The visual representation of Laws 5 to 7 is shown in Figure 7.…”

Section: Addition Of Link-branchesmentioning

confidence: 99%

“…In practical engineering, the grounding method through resistance is widely adopted [20]. Therefore, an effective and complete current flow path can be formed when asymmetric phase‐to‐ground faults occur, which makes the zero‐sequence characteristic of wind farms more significant and thus enables that some protection and fault diagnosis schemes in wind farms can be achieved based on zero‐sequence current [23, 24]. In addition, in order to isolate the harmonic and zero‐sequence current from WTGs, BTs at WTGs adopt the delta‐grounded‐wye winding configuration as shown in Figure 1 [23, 25].…”

Section: Typical Structure Of Onshore Wind Farmsmentioning

confidence: 99%

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A novel fault localization method for asymmetric phase‐to‐ground faults on onshore wind farm collector lines without phasor measurement

Ding,

Zhu

2023

IET Generation Trans & Dist

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Existing fault localization methods for power lines are not applicable to the collector line in onshore wind farms due to the dense access of complex power supplies. Therefore, a fault localization method based on equivalent zero‐sequence injection current (ZIC) is proposed for asymmetric phase‐to‐ground faults on collector lines. Firstly, the idea of load shifting is adopted to generalize the theory of equivalent injection current (EIC) to multi‐terminal and meshed lines. Then, considering features of the zero‐sequence network of wind farms, the EIC theory on two‐terminal and multi‐terminal lines is applied to zero‐sequence networks, and thus faulty areas, faulty sub‐lines, and fault distance can be determined. Attractively, the element correlation in node impedance matrices of zero‐sequence networks is derived from the network branch‐addition process, and the numerical characteristic of matrix elements is further revealed from the particularity of wind farms, based on which root‐mean‐square (RMS) calculation of ZIC can be improved to enable the localization without phasor measurement. Verification results prove that this proposed method can accurately realize the fault localization independent of wind turbine generators with a few measurement points, and the applicability of collector lines with line branches is also considered; additionally, only voltage RMS needs to be measured.

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Section: Addition Of Link-branchesmentioning

confidence: 99%

Section: Typical Structure Of Onshore Wind Farmsmentioning

confidence: 99%

A novel fault localization method for asymmetric phase‐to‐ground faults on onshore wind farm collector lines without phasor measurement

Ding,

Zhu

2023

IET Generation Trans & Dist

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“…3. Back-to-back converters of a wind turbine [16]. Supercapacitors or ultra-capacitors give the possibility of oversizing the DC link.…”

Section: Grid-following With Inertia Emulationmentioning

confidence: 99%

Analysis of Synthetic Inertia Strategies from Wind Turbines for Large System Stability

Berizzi

Bosisio

Ilea

et al. 2022

IEEE Trans. on Ind. Applicat.

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In this paper, two different strategies to enhance power system stability exploiting synthetic inertia are presented and compared to assess the possibility of increasing the penetration of converter-based generation in power systems, keeping a suitable stability margin. Synthetic inertia is the emulation, carried out by converted-based generation, of the inertial response provided by rotating generators during frequency events. Two approaches are hence described, focusing on wind power. In the first approach, the primary power source is connected to the grid by a grid-following converter, and it exploits the energy content of the direct current link, suitably sized (or, with the same concept, of a suitable energy buffer available). The second approach uses a grid-forming converter model, whose features resemble more a traditional synchronous machine. The paper aims at assessing the contribution to frequency stability that can be provided by converter-based generation. The simulation results demonstrate that converterbased generation can efficiently guarantee frequency stability, thus proving that future power systems will be able to manage ever-increasing RES converter-based generation.

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“…Nevertheless, the arcing ignition caused by an HIF is a safety hazard [4,6]. Moreover, undetected HIFs have been reported to cause instability of renewable energy systems [7]. As a result, reliably detecting and clearing HIFs in a timely manner are crucial to ensure the safety of personnel and maintain the power system integrity [4][5][6].…”

Section: Introductionmentioning

confidence: 99%

Deep Learning for High-Impedance Fault Detection: Convolutional Autoencoders

et al. 2021

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High-impedance faults (HIF) are difficult to detect because of their low current amplitude and highly diverse characteristics. In recent years, machine learning (ML) has been gaining popularity in HIF detection because ML techniques learn patterns from data and successfully detect HIFs. However, as these methods are based on supervised learning, they fail to reliably detect any scenario, fault or non-fault, not present in the training data. Consequently, this paper takes advantage of unsupervised learning and proposes a convolutional autoencoder framework for HIF detection (CAE-HIFD). Contrary to the conventional autoencoders that learn from normal behavior, the convolutional autoencoder (CAE) in CAE-HIFD learns only from the HIF signals eliminating the need for presence of diverse non-HIF scenarios in the CAE training. CAE distinguishes HIFs from non-HIF operating conditions by employing cross-correlation. To discriminate HIFs from transient disturbances such as capacitor or load switching, CAE-HIFD uses kurtosis, a statistical measure of the probability distribution shape. The performance evaluation studies conducted using the IEEE 13-node test feeder indicate that the CAE-HIFD reliably detects HIFs, outperforms the state-of-the-art HIF detection techniques, and is robust against noise.

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Arc flash fault detection in wind farm collection feeders based on current waveform analysis

Cited by 10 publications

References 23 publications

A novel fault localization method for asymmetric phase‐to‐ground faults on onshore wind farm collector lines without phasor measurement

A novel fault localization method for asymmetric phase‐to‐ground faults on onshore wind farm collector lines without phasor measurement

Analysis of Synthetic Inertia Strategies from Wind Turbines for Large System Stability

Deep Learning for High-Impedance Fault Detection: Convolutional Autoencoders

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