电压电流能量信息融合的低压交流电弧故障检测

针对串联电弧故障检测判据选择难、阈值设置难的问题，本文在传统基于电流检测方法的基础上融合使用电压信息，提出了一种电压电流能量信息融合的交流电弧故障检测方法。以分析开关电源和非开关电源类负荷下的各自故障特征为基础，提出了利用电压半波总能量的开关电源类电弧故障直接判定方法，并融合使用电压电流特征能量波形相关性实现故障线路的选择；提出了基于敏感域电压电流最大瞬时特征能量相位匹配的适用于非开关电源类负荷下的故障检测方法，以特征能量相位信息构建判据，克服了传统检测方法的阈值设定困难问题。本文检测方法判据虽利用了负荷分类思想，但由于开关电源类负荷下的故障检测可利用电压半波总能量幅值实现故障直接判定，因此实际应用中无需辨识负荷类型。相较传统利用电流特征的检测方法，本文方法具有判据简单、易于阈值设定的优势。试验结果表明，本文方法可有效用于多种类型负荷的电弧故障检测，检测时间满足相关标准规定。

Low voltage AC arc fault detection based on energy information fusion of voltage and current

Aiming at the problems of difficult selection of criteria and threshold setting for series arc fault detection, this paper proposes a voltage and current energy information fusion method for AC arc fault detection based on the traditional current detection method by integrating the use of voltage information. Based on the analysis of the respective fault characteristics of switching power supply and non-switching power supply loads, a direct determination method of Arc fault of switching power supply using the total voltage half wave energy is proposed, and the fault line is selected based on the correlation of voltage and current characteristic energy waveform. A fault detection method suitable for non-switching power supply loads is proposed based on the phase matching of maximum instantaneous characteristic energy of voltage and current in the sensitive domain. The criterion is constructed with characteristic energy phase information, and the difficulty of threshold setting in traditional detection methods is overcome. Although the detection method in this paper uses the idea of load classification, it is not necessary to identify the type of load in practical application because the fault detection under switching power supply loads can be directly determined by using the total energy amplitude of the voltage half-wave to determine the fault directly. Compared to traditional detection methods based on current characteristics, this method has the advantages of simple criteria and easy threshold setting. Experimental results confirm the effectiveness of the proposed method in detecting arc faults in various load types, with detection time meeting industry standards.