Electromagnetic Time Reversal Localization Method for Distribution Cables Based on Reflection Coefficient Modification

IF 7.2 1区 工程技术 Q1 AUTOMATION & CONTROL SYSTEMS IEEE Transactions on Industrial Electronics Pub Date : 2024-11-08 DOI:10.1109/TIE.2024.3485718
Guangya Zhu;Songkun Pan;Lu Lu;Pengfei Meng;Zhaogui Liu;Xinyi Wang;Kai Zhou
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Abstract

Accurate and rapid localization of cable faults is crucial for enhancing the power supply reliability of distribution cable systems. The traditional electromagnetic time reversal (EMTR) method was utilized to accurately localize cable faults. However, due to the nonlinear characteristics of fault development, unknown and varying fault resistances lead to significant deviations in the EMTR localization results. To address this issue, this article proposes an improved EMTR method based on reflection coefficient modification (EMTR-RCM). The mechanism by which the fault resistance affects the localization accuracy of EMTR is analyzed in detail. The reflection coefficients during the EMTR backward-time process are modified, and a new transfer function is designed. Experimental validation and simulation analysis demonstrate that the proposed method is robust against cable operating conditions and fault types.
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基于反射系数修正的配电电缆电磁时间逆转定位方法
准确、快速地定位电缆故障对提高配电电缆系统的供电可靠性至关重要。利用传统的电磁时间反转(EMTR)方法对电缆故障进行精确定位。然而,由于故障发展的非线性特性,未知和变化的故障电阻导致EMTR定位结果存在较大偏差。针对这一问题,本文提出了一种基于反射系数修正的改进EMTR方法(EMTR- rcm)。详细分析了故障电阻影响EMTR定位精度的机理。对EMTR反时过程中的反射系数进行了修正,设计了新的传递函数。实验验证和仿真分析表明,该方法对电缆运行工况和故障类型具有较强的鲁棒性。
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来源期刊
IEEE Transactions on Industrial Electronics
IEEE Transactions on Industrial Electronics 工程技术-工程:电子与电气
CiteScore
16.80
自引率
9.10%
发文量
1396
审稿时长
6.3 months
期刊介绍: Journal Name: IEEE Transactions on Industrial Electronics Publication Frequency: Monthly Scope: The scope of IEEE Transactions on Industrial Electronics encompasses the following areas: Applications of electronics, controls, and communications in industrial and manufacturing systems and processes. Power electronics and drive control techniques. System control and signal processing. Fault detection and diagnosis. Power systems. Instrumentation, measurement, and testing. Modeling and simulation. Motion control. Robotics. Sensors and actuators. Implementation of neural networks, fuzzy logic, and artificial intelligence in industrial systems. Factory automation. Communication and computer networks.
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