高精度故障电弧检测多传感器数据融合算法

 由于故障电弧的物理特性复杂, 且电路中存在与故障电弧波形相似的负载, 因此传统 检测故障电弧的方法误判率较高. 提出了一种多传感器数据融合算法, 用于提高故障电弧的检测精度. 该算法包括自适应加权融合算法和神经网络融合算法, 实现了对温度传感器、声音传 感器和弧光强度传感器所获取的传感信号的数据融合. 自适应加权融合算法克服了单个传感 器的不确定性, 实现了同质传感器中故障电弧特征的提取, 为神经网络融合算法提供了精确的测试样本数据; 神经网络融合算法可自行调整各类异质传感器的权重, 使故障电弧的辨识率更高. 实验结果表明, 该算法可有效提取故障电弧的特征, 辨识精度超过98%, 实现了高精度的故障电弧检测.

Multi-sensor Data Fusion Algorithm for High Accuracy Arc Fault Detection

Since physical characteristics of arc fault are complex and there are loads with waveforms similar to arc fault in circuits, misjudgment of traditional arc fault detection methods is high. To improve the arc fault detection accuracy, a multi-sensor data fusion algorithm that includes an adaptive weighted fusion algorithm and a neural network fusion algorithm is proposed. Date is acquired with a temperature sensor, an acoustic sensor and a light sensor. The adaptive weighted fusion algorithm can overcome uncertain of single sensor, extract the arc fault characteristics of homogeneous sensors, and provide accurate test sample date for neural network fusion, making the probability of identifying arc fault more accurately by automatically adjusting the weights for all kinds of heterogeneous sensors. Experimental results show that the algorithm can effectively extract the arc fault characteristics and improve identification accuracy up to 98%, meeting the high accuracy arc fault detection requirement.