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Lock-out Algorithm of Active-Intervention-Type Arc Suppression Device Based on ELM

机译:基于ELM的主动干预式消弧装置的闭锁算法

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The active-intervention-type arc suppression device can effectively prevent arc re-ignition and arc-overvoltage at the fault point in the distribution network. However, under some unfavorable working conditions, it is difficult for the device to extinguish the arc, so the corresponding equivalent circuit is established to derive the expression of the fault current. The expression shows that when low resistance ground fault occurs at the end of a long transmission line with a heavy load, the fault current may increase, so the device needs to be locked in these working conditions. The impact factors of fault current are used as the input characteristics to train the extreme learning machine (ELM) neural network, and then the performance of the neural network is verified by the test set. Finally, the simulation results show that the proposed device locking algorithm based on ELM has a high accuracy, which makes the device locked under unfavorable working conditions.
机译:主动干预式消弧装置可以有效地防止配电网故障点处的电弧重燃和电弧过电压。但是,在某些不利的工作条件下,设备很难熄灭电弧,因此建立了相应的等效电路以导出故障电流的表达式。该表达式表明,在长负载的长输电线路末端发生低电阻接地故障时,故障电流可能会增加,因此需要将设备锁定在这些工作条件下。将故障电流的影响因素作为输入特征来训练极限学习机(ELM)神经网络,然后通过测试集验证神经网络的性能。最后,仿真结果表明,所提出的基于ELM的设备锁定算法具有较高的精度,使得该设备在不利的工作条件下被锁定。

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