基于OD-LTP的振动条件下串联型故障电弧检测方法研究

Study on the detection method of series fault arc under vibration condition based on OD-LTP

当三相异步电动机发生机械振动时,主回路中接触不良的电气接触点在振动作用下会产生串联型故障电弧,进而影响电路安全甚至引发电气火灾。而振动条件会加剧了故障电弧信号的复杂性,因此本文以回路电流信号为研究对象,提出了一种振动条件下的高实时性串联型故障电弧检测方法。首先通过构建滑动记忆矩阵对实验电流数据进行动态保存,其次通过正交方向改进局部三值化模式(orthogonality direction local ternary pattern,OD-LTP)提取滑动记忆矩阵的纹理特征,最终将统计的OD-LTP图像的灰度分布直方图幅值作为特征向量,通过基于沙猫群优化(sand cat swarm optimization,SCSO)的支持向量机(support vector machine,SVM)建立振动串联型故障电弧检测模型。本文通过对比不同矩阵参数,得到最佳的滑动记忆矩阵尺寸,最终所提方法对故障电弧识别的准确率达到99.2%。通过对不同工况、不同特征提取方法对比分析,表明本文提出方法不仅适用于不同工况运行的工业电机变频器系统,其相对于其他特征提取方法也具有较高的实时性。

When mechanical vibration occurs in a three-phase asynchronous motor,the poor electrical contact points in the main circuit will generate a series of fault arcs under the influence of vibration,which will compromise circuit safety and potentially lead to electrical fires.The vibration condition complicates the fault arc signal,so this paper proposes a highly real-time series fault arc detection method under vibration conditions.First,experimental current data is dynamically preserved by constructing a sliding memory matrix.Secondly,the texture features of the sliding memory matrix are extracted using orthogonality direction local ternary pattern(OD-LTP).Finally,the amplitude of the grayscale distribution histogram of the statistical OD-LTP images is taken as the feature vector.A vibrating series fault arc detection model is established using support vector machine(SVM)optimized by sand cat swarm optimization(SCSO).By comparing different matrix parameters,the proposed method achieves an accuracy of 99.2%.Through a comparative analysis of different feature extraction methods under various working conditions,it is shown that the proposed method is not only suitable for industrial motor inverter systems under different working conditions,but also exhibits higher real-time performance compared to other feature extraction methods.