On the Use of Artificial Intelligence for High Impedance Fault Detection and Electrical Safety
On the Use of Artificial Intelligence for High Impedance Fault Detection and Electrical Safety
复制标题
人工智能在高阻抗故障检测和电气安全中的应用
DOI：
10.1109/tia.2020.3017698
复制
发表时间：
2020-11-01
影响因子：
4.4
通讯作者：
Dehghanian, Payman
中科院分区：
文献类型：
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作者：
Wang, Shiyuan;Dehghanian, Payman
在极端天气条件下，由架空电力线路故障引起的事故已经越来越频繁地被报道，并且可能强烈地威胁电网的安全和稳定，例如，由于电弧或线路接触植被、继电器误动等引起的大规模野火。人们普遍认识到，必须及时妥善解决架空线路故障引发的电气安全问题，以最大限度地减少后续风险和损失。现有的监测设备和保护继电器几乎不能检测高阻抗故障（HIF），并且在观察到严重异常或损坏之前无法警告系统操作员。为了避免HIF的破坏性后果，提出了一种嵌入机器学习分析的在线监测系统，以确保快速准确地检测电力系统中的HIF。所提出的人工智能引擎的性能进行了测试，在各种模拟条件下，数值结果表明其有效性和优越性的国家的最先进的进步。
Accidents caused by faults on overhead power lines have been more frequently reported under extreme weather conditions and may strongly threaten the safety and stability of the power grids, e.g., massive wildfires caused by the electrical arcs or lines getting in touch with vegetation, relay miss-operations, etc. It has been widely recognized that the electric safety concerns engendered by overhead line faults have to be timely and properly addressed to minimize the subsequent risks and damages. The existing monitoring devices and protective relays can barely detect high impedance faults (HIFs) and are unable to warn the system operators until serious abnormalities or damages are observed. Aiming at avoiding the damaging consequences of HIFs, an online monitoring system embedded with machine learning analytics is proposed that ensures a fast and accurate detection of HIFs in power systems. The performance of the proposed artificial intelligence engine is tested under a variety of simulated conditions and the numerical results demonstrate its efficacy and superiority over the state-of-the-art advancements.