Testing Machine Learned Fault Detection and Classification on a DC Microgrid
Testing Machine Learned Fault Detection and Classification on a DC Microgrid
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在直流微电网上测试机器学习故障检测和分类
DOI：
10.1109/isgt50606.2022.9817517
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发表时间：
2022
期刊：
影响因子：
--
通讯作者：
David Stoltzfuz
中科院分区：
文献类型：
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作者：
Samuel T. Ojetola;M. Reno;J. Flicker;Daniel Bauer;David Stoltzfuz
可再生能源的持续增长和对直流负荷的依赖，激发了人们对直流微电网在配电系统中应用的兴趣。然而，与交流系统相比，直流微电网中缺乏自然过零使得用熔断器和断路器中断故障电流更加困难。直流故障会在几毫秒内对电压源型变流器造成严重损害，因此需要快速检测和隔离故障。在本文中，五种不同的机器学习（ML）分类器，以识别故障类型和故障电阻在直流微电网的潜力进行了探讨。ML算法的训练使用模拟故障数据记录从750 VDC微电网建模PSCAD/EMTDC。训练算法的性能进行了测试，使用真实的故障数据收集从位于克特兰空军基地的操作直流微电网。在五种ML算法中，三种可以检测故障并以至少99%的准确度确定故障类型，并且只有一种可以以至少99%的准确度估计故障电阻。通过执行自学习监测和决策分析，配备ML算法的保护继电器可以快速检测和隔离故障，以改善DC微电网上的保护操作。
Interest in the application of DC Microgrids to distribution systems have been spurred by the continued rise of renewable energy resources and the dependence on DC loads. However, in comparison to AC systems, the lack of natural zero crossing in DC Microgrids makes the interruption of fault currents with fuses and circuit breakers more difficult. DC faults can cause severe damage to voltage-source converters within few milliseconds, hence, the need to quickly detect and isolate the fault. In this paper, the potential for five different Machine Learning (ML) classifiers to identify fault type and fault resistance in a DC Microgrid is explored. The ML algorithms are trained using simulated fault data recorded from a 750 VDC Microgrid modeled in PSCAD/EMTDC. The performance of the trained algorithms are tested using real fault data gathered from an operational DC Microgrid located on the Kirtland Air Force Base. Of the five ML algorithms, three could detect the fault and determine the fault type with at least 99% accuracy, and only one could estimate the fault resistance with at least 99% accuracy. By performing a self-learning monitoring and decision making analysis, protection relays equipped with ML algorithms can quickly detect and isolate faults to improve the protection operations on DC Microgrids.