DC-Microgrid Fault Detection & Classification Using ANN Enabled BAT Algorithm

S. B. Pati, S. K. Barik, Subhasri Kundu, Ritesh Dash, Adithya Ballajhi
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Abstract

Power electronic best components are most sensitive to variation in voltage and current. therefore these components when connected to a DC microgrid needs more attention and protection against a different type of circuit faults like short circuit and open circuit condition. During short circuit condition the flow of heavy current will damage the electronic devices and thereby in order to achieve the protection these electronic devices may trip themselves. sudden tripping of the devices will have an adverse negative impact on the DC microgrid. This paper presents a new ANN-enabled bat algorithm to detect the DC fault and to isolate the fault from the rest part of the system. Matlab simulink based model has been developed to test the prototype and to compare the ANN enabled bat algorithm with other algorithm for comparing the efficiency of the proposed algorithm.
基于神经网络的BAT算法的直流微电网故障检测与分类
电力电子的最佳元件对电压和电流的变化最为敏感。因此,当这些组件连接到直流微电网时,需要更多的关注和保护,以防止不同类型的电路故障,如短路和开路情况。在短路状态下,大电流的流动会损坏电子设备,从而实现对电子设备的跳闸保护。设备的突然跳闸将对直流微电网产生不利的负面影响。本文提出了一种新的基于神经网络的蝙蝠算法来检测直流故障,并将故障与系统其余部分隔离开来。建立了基于Matlab simulink的模型对原型进行测试,并将基于人工神经网络的蝙蝠算法与其他算法进行比较,以比较所提出算法的效率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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