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Open-Neutral Fault Detection in Underground Space Based on Genetic Support Vector Machine

机译:基于遗传支持向量机的地下空间中的开放中性故障检测

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When an open-neutral fault occurs in an underground space low-voltage distribution system, the neutral-point voltage will offset, which will make a lot of equipment unable to work normally. In order to solve such problem, the changes of third harmonic current and neutral-point offset voltage were studied and analyzed, the parameters of Support Vector Machine (SVM) was optimized by using a genetic algorithm. The output of SVM whether normal or fault state can be distinguished by taking the variation of harmonic current and neutral offset voltage as the input of SVM. The experimental result shows that the method of optimizing the parameters of SVM based on the genetic algorithm can effectively detect the fault.
机译:在地下空间低压分布系统中发生开放中性故障时,中性点电压将偏移,这将使大量设备无法正常工作。为了解决此类问题,研究并分析了第三谐波电流和中性点偏移电压的变化,通过使用遗传算法优化了支持向量机(SVM)的参数。 SVM的输出可以通过将谐波电流和中性偏移电压的变化作为SVM的输入来区分正常或故障状态。实验结果表明,基于遗传算法优化SVM参数的方法可以有效地检测故障。

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