基于CNN与特征周期变化率的故障电弧检测嵌入式系统实现研究

Research on embedded system of arc fault detection based on CNN and characteristic period change rate

针对现有电弧故障检测装置精准度低、实时性不足以及高负载持续运行等问题,提出基于卷积神经网络(convolutional neural networks,CNN)与特征周期变化率的故障电弧检测算法,该算法分为上电和过程两种检测模式。上电模式采用多特征量导入CNN进行检测,过程模式提出电流的不同状态特征值周期变化率的概念,并把特征值变化率进行分区,联合神经网络进行递进检测,在保证准确率的同时降低算法复杂度。该算法以STM32H743为处理器,搭配调理、数据采集等电路形成实时电弧故障诊断系统。经过实验测试,本装置对交流故障电弧检测平均正确率达到97.43%,最快检测时间低至0.045 s,可为电弧故障检测装置的研制提供理论支撑和可靠参考。

Aiming at the problems of low accuracy,insufficient real-time performance and continuous operation of high load of existing arc fault detection devices,a fault arc detection algorithm based on convolutional neural networks(CNN)and characteristic period change rate is proposed.The algorithm is divided into two detection modes:Power-on and process.In the power-on mode,multiple features are imported into CNN for detection.In the process mode,the concept of periodic change rate of different state eigenvalues of current is proposed,and the change rate of eigenvalues is partitioned.CNN is combined for progressive detection,which reduces the complexity of the algorithm while ensuring the accuracy.The algorithm uses STM32H743 as the processor,with conditioning,data acquisition and other circuits to form a real-time arc fault diagnosis system.After experimental testing,the average accuracy of the device for AC arc fault detection is 97.43%,and the fastest detection time is as low as 0.045 s.It can provide theoretical support and reliable reference for the development of arc fault detection device.