Abstract
The single-phase grounding fault of power systems is influenced by a variety of factors, resulting from the developing sizes and increasing complexity of power systems. In order to take advantage of big data in power systems, we propose a revised method with the use of synchronized phasor measurement. The data-driven method is designed to detect and localize the single-phase grounding fault, which reveals the correlation between eigenvalues and status of power systems. First, it calculates the contribution of the fault to each node; it then combines with a split window to monitor power systems in real time and to detect fault more efficiently. Based on the correlation between the elements of the matrix, it is robust against bad data and highly sensitive to weak signals. In general, the proposed method is applicable to various faults and well-functioning with real-time analysis. We test the proposed method with case studies from a distribution network with 80 nodes.
Highlights
The single-phase grounding fault is the default method in power systems [1]
Based on the Marchenko-Pastur law (M-P law), this paper proposes a novel data-driven method to detect and localize the single-phase grounding fault, where the outliers in the spectrum of a zero-sequence matrix represent the signal of the fault, and the fault localization indices of each node are calculated based on the outliers
To compare the time consumed of different methods, four methods are implemented for case 1, that is, M1: the method proposed in this paper, M2: the correlation analysis [38], M3: wavelet transform method [42], and M4: a method based on random matrix theory (RMT) and Hausdorff distance
Summary
The single-phase grounding fault is the default method in power systems [1]. the access to highpermeability new energy has gradually evolved power systems into multi-source dynamic networks [2–6], which makes the single-phase grounding fault difficult to detect and eliminate, especially when the fault signal is weak, such as grounded by large resistance. Random matrix theory (RMT) is a mathematical statistical method which can directly mine the intrinsic connection of a system from data; it can be applied to extract fault features and increase the accuracy of fault detection and localization. Based on bat algorithm and random matrix theory, Kai introduces fault line selection, and Weibiao et al argues for determining the fault area by constructing augmented matrix [38] The accuracy of these methods cannot be guaranteed when the distribution network is complex. Based on the M-P law, this paper proposes a novel data-driven method to detect and localize the single-phase grounding fault, where the outliers in the spectrum of a zero-sequence matrix represent the signal of the fault, and the fault localization indices of each node are calculated based on the outliers. The M-P law is used to analyze the operation of power systems and detect and localize the fault with high speed and sensitivity
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