基于自动故障分析的输电线路雷电与架空线路故障分析,马来西亚TNB

Chung Yoke Wai, N. S. Hudi, Muhammad Shahmi Shokri, Ir. Noradlina Abdullah
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摘要

架空电力线的故障会对线路造成重大损害,导致停电和其他中断。及时检测和诊断故障对于最大限度地减少停机时间和降低与输电线路资产维修和维护相关的成本至关重要。本文介绍了一种基于网络的自动故障分析工具——自动故障分析(AFA),该工具由Tenaga国家电力公司(TNB)电网部和TNB研究中心(TNBR)共同开发。该系统能够自动提供(1)快速准确的故障定位,协助设备恢复;(2)雷电和故障相关性分析;(3)继电器和断路器性能监测。通过利用变电站RTAP网关和tnb拥有的光纤网络与数字故障记录仪和数字保护继电器的内联网,使AFA自动在线分析成为可能。为了丰富故障信息,将雷电探测系统、地理信息系统和CAPE™线路参数数据进行数字化集成,并与gps系统同步。AFA在TNB-Grid服务近2年,在准确的故障定位估计和雷电与故障关联分析方面取得了令人鼓舞的成果。本文以实际工程师的解决方案为出发点,结合资产绩效管理系统,介绍了AFA照明分析模块。并分享了该故障分析平台目前的开发成果和近期的一些改进成果,包括人工智能
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Lightning and Overhead Line Fault Analysis using Automated Fault Analysis Application On Transmission Line in Grid Division, TNB Malaysia
A fault on overhead power lines can cause significant damage to the lines, resulting in outages and other disruptions. Detecting and diagnosing faults promptly is critical to minimize downtime and reduce the costs associated with repairs and maintenance on the transmission line assets. In this paper, we present a web-based automated fault analysis tool known as Automated Fault Analysis (AFA) jointly developed by Tenaga Nasional Berhad (TNB) Grid Division and TNB Research Center (TNBR). This system can automatically provide (1) fast and accurate fault location to assist in equipment restoration, (2) lightning and fault correlation analysis, and (3) relay and circuit breaker performance monitoring. The AFA automatic online analysis is made possible with intranet connection to digital fault recorders and digital protective relays utilizing substation RTAP gateway and TNB-owned fiber-optic network. For fault information enrichment, a Lightning Detection System, a Geographical Information System and CAPE™ Line Parameter Data were digitally integrated and are synchronized with GPS-system. AFA has been serving TNB-Grid for almost 2 years and the results are very encouraging with accurate fault location estimation and lightning and fault correlation analysis. This paper describes AFA lighting analysis module with practical engineer’s solution in mind and in-line with asset performance management system. Results of the current development and some near future enhancement including artificial intelligence using this fault analytic platform are shared
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