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Question

故障电弧的物理特征复杂，而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。本文提出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进行数据融合。其中自适应加权融合算法克服单个传感器的不确定性，实现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，使故障电弧的辨识概率更加精确。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与预警。

Answer 1

The physical characteristics of fault arc complex, and circuits in the presence of waveform and the fault arc is similar to the load, so the traditional method to detect arc fault error rate. This paper presents a multi sensor data fusion algorithm, is used to improve the detection accuracy of fault arc. The algorithm includes the adaptive weighted fusion algorithm and neural network fusion algorithm of data fusion, sensor signal acquisition of temperature sensor, sound sensor and arc light intensity sensor. The adaptive weighted fusion algorithm to overcome the uncertainty of single sensor, extract the fault arc characteristics of homogeneous sensor in the test sample, provide accurate data for neural network fusion algorithm; neural network fusion algorithm can adjust the weight of all kinds of heterogeneous sensors, so that the identification probability of fault arc more accurately. The experimental results show that, the algorithm can effectively extract the characteristics of fault arc, the identification accuracy of more than 98%, the fault arc detection and warning of high precision.

Answer 2

英语专业研究生，我可以帮你追问

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