journal article·2025-03-24Open access

A Single-End Location Method for Small Current Grounding System Based on the Minimum Comprehensive Entropy Kurtosis Ratio and Morphological Gradient

Jiyuan Cao, Yanwen Wang, Lingjie Wu, Yongmei Zhao, Le Wang

Applied Sciences· Volume 15· Issue 7

Abstract

Fault location technology is crucial for enhancing the efficiency of fault maintenance and ensuring the safety of the power supply in small current grounding systems. To address the challenge that traditional single-end positioning methods experience when identifying the reflected wave head and that the adaptability of wave head calibration methods is typically limited, a single-end location method of modulus wave velocity differences based on marine predator algorithm optimized multivariate variational mode decomposition (MVMD) and morphological gradient is proposed. Firstly, the minimum comprehensive entropy kurtosis ratio is used as the fitness function, and the marine predator algorithm is used to realize the automatic optimization of the mode number and penalty factor of the multivariate variational mode decomposition. Therefore, with the goal of decomposing the traveling wave characteristic signals with the most significant traveling wave characteristic information and the lowest noise component, the line-mode traveling wave and the zero-mode traveling wave are accurately decomposed. Secondly, the intrinsic mode function component with the smallest entropy kurtosis ratio is selected as the line-mode traveling wave characteristic signal and the zero-mode traveling wave characteristic signal, respectively, and the arrival time of the wave head is accurately calibrated by combining the morphological gradient value. Finally, the fault distance is calculated by the modulus wave velocity difference location formula and compared with the variational mode decomposition-Teager energy operator (VMD-TEO) method and the empirical mode decomposition _first-order difference method. The results show that the proposed method has the highest accuracy of positioning results, and the algorithm time is significantly reduced compared with the VMD-TEO method, and it has strong adaptability to different line types of faults, different fault initial conditions, and noise interference.
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Research context

OpenAlex topics: Smart Grid and Power Systems · Computational Physics and Python Applications · Advanced Decision-Making Techniques

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

School of Mechanical and Electrical Engineering, China University of Mining and Technology-Beijing, Beijing 100083, China

Petrochina Planning and Engineering Institute, Beijing 100083, China

Technical and Economic Research Institute, The CHN Energy, Beijing 100083, China

Datang North China Electric Power Test and Research Institute, China Datang Corporation Science and Technology General Research Institute Co., Ltd., Beijing 100043, China

Funding

the Fundamental Research Funds for the Central Universities (Ph.D. Top Innovative Talents Fund of CUMTB)

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