An improved Hausdorff distance method for locating single phase to ground fault in neutral non‐effectively grounded system

Wu, Wenhao; Zhang, Xinhui; Zhang, Jun; He, Yu; Bai, Wenyuan

doi:10.1049/gtd2.12212

Cited by 7 publications

(3 citation statements)

References 16 publications

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“…According to the same derivation principle, the steadystate projection component of the ZSC of downstream at the fault point, down I  can be obtained [26,27,28].…”

Section: Zsv and Zsc After Small Resistor Inputmentioning

confidence: 99%

Flexible grounding system fault location method based on steady state projection

Liu,

et al. 2024

IEICE Electron. Express

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At present, the grounding protection of flexible grounding system mostly directly adopts the zero-sequence time-limit overcurrent protection of low-resistance grounding system, which has problems such as insufficient protection sensitivity when grounding via high-resistance. The changes in zero-sequence voltage (ZSV) and zero-sequence current (ZSC) upstream and downstream of the fault point before and after the small resistor is put into operation are analyzed. The fault information collected by the synchronous phasor measurement unit (PMU) are orthogonalized. Finally, the correlation coefficient method is utilized to compare the numerical values and polarity of projection amounts between adjacent PMUs. Based on this, a fault location method based on steady-state projection is proposed in this paper. Simulation and on-site test data show that the proposed method can accurately identify the fault section under different fault conditions.

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“…According to the same derivation principle, the steadystate projection component of the ZSC of downstream at the fault point, down I  can be obtained [26,27,28].…”

Section: Zsv and Zsc After Small Resistor Inputmentioning

confidence: 99%

Flexible grounding system fault location method based on steady state projection

Liu,

et al. 2024

IEICE Electron. Express

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“…The formula of Hausdorff distance is described in ref. [25] The only fixed point P ∈ H (X ) in affine transformation W satisfies the following equation:…”

Section: Partitioning and Fractal Analysis Based On The Grg Series Fo...mentioning

confidence: 99%

An improved spatial upscaling method for producing day‐ahead power forecasts for wind farm clusters

Yang

Yan

Dai

et al. 2022

IET Generation Trans & Dist

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Large‐scale day‐ahead wind power forecasting (WPF) for wind farm clusters (WFCs) can enable dispatching agencies to formulate scientifically sound power generation plans and enhance the robustness of power grids. Most available WPF methods for WFCs only involve mathematical models and rarely consider spatial correlation factors. This necessitates further improvements to forecasting systems. In this study, to increase the day‐ahead WPF accuracy for WFCs, fractal transform theory is introduced to optimize the process of WPF for WFCs through spatial upscaling and establish a day‐ahead WPF model for WFCs based on an improved spatial upscaling method. First, a WFC is partitioned into subclusters. Then, using fractal transform theory, an affine relation is established between the local output of each subcluster and the overall output of the WFC. Finally, a day‐ahead power forecast is produced for the WFC through spatial upscaling of the forecast for the subcluster with the highest grey relational grade with the output of the WFC. The applicability of the proposed forecasting model is examined using historical measured data for a large WFC in north‐eastern China. The case study results show that the proposed forecasting model outperforms the summation method and the statistical upscaling method in terms of forecasting accuracy.

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“…Because the first half-wave signal reflecting the ground fault characteristics has passed, the mainstream approach is to increase the waveform data window and trace back to compare clear characteristic signals [7][8][9]. However, how to effectively estimate the fault occurrence time lacks targeted research [4,10].…”

Section: Introductionmentioning

confidence: 99%

Time Estimation Algorithm of Single-Phase-to-Ground Fault Based on Two-Step Dimensionality Reduction

Lin

Chen

et al. 2023

Energies

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The fault detection time identified by relying on the over-voltage criterion of zero-sequence voltage often lags behind the actual occurrence time of ground faults, which may cause fault protection methods based on transient quantity principles to miss fault characteristics and lose their protection capability. To accurately estimate the time of occurrence of a single-phase-to-ground fault, this paper proposes a two-step dimensionality reduction algorithm for estimating the time of occurrence of a single-phase-to-ground fault in a distribution network. This algorithm constructs a filter based on Empirical Mode Decomposition (EMD) to establish a high-dimensional feature dataset based on the zero-sequence current of all feeders. After Principal Component Analysis and Hilbert Mapping Algorithm, the high-dimensional data are reduced to two dimensions to construct a two-dimensional feature dataset. The density-based clustering method is used to adaptively divide the data into two categories, fault data and non-fault data, so as to estimate the time of occurrence of the fault. The paper designs 11 sets of experiments including 7 common high-resistance grounding mediums to verify the accuracy of the fault time recognition of this algorithm. The accuracy of this algorithm is within 7.3 ms and it exhibits better detection performance compared to the threshold detection method.

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An improved Hausdorff distance method for locating single phase to ground fault in neutral non‐effectively grounded system

Cited by 7 publications

References 16 publications

Flexible grounding system fault location method based on steady state projection

Flexible grounding system fault location method based on steady state projection

An improved spatial upscaling method for producing day‐ahead power forecasts for wind farm clusters

Time Estimation Algorithm of Single-Phase-to-Ground Fault Based on Two-Step Dimensionality Reduction

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