基于SSA-SVM的航空电弧故障检测
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Aviation Arc Fault Detection Based on SSA-SVM
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    摘要:

    针对航空线路系统电弧故障隐蔽性高和难以检测的问题,提出一种基于麻雀搜索算法优化支持向量机(Sparrow Search Algorithm Optimization Support Vector Machine,SSA-SVM)的航空电弧故障检测方法。首先采用小波分解对电弧故障电流数据进行分解,小波分解能有效克服经验模态分解时存在的模态混叠问题。再从信号无序度的角度对电流分量提取能量熵、模糊熵与近似熵,并构造特征向量。然后,使用麻雀搜索算法对支持向量机的权值进行优化,得到最优的权值,最后用训练好的支持向量机对测试样本进行分类。为了验证所提方法的有效性,搭建电弧实验平台,模拟航空线路系统电弧故障的产生,分别采集交流串联正常和电弧故障电流数据,应用本文提出的SSA-SVM算法进行电弧故障检测,结果表明,该方法能较好地识别出电弧故障,检测准确率达到99.5%,相比于粒子群算法或遗传算法优化的支持向量机对电弧故障的检测准确率分别高出2.5%和2%。

    Abstract:

    A sparrow search algorithm optimized support vector machine based aviation arc fault detection method is proposed to address the issues of high concealment and difficulty in detecting arc fault in aviation line systems.Firstly,wavelet decomposition is used to decompose the arc fault current data,which can effectively overcome the problem of modal aliasing during empirical mode decomposition.From the perspective of signal disorder,energy entropy,fuzzy entropy, and approximate entropy are extracted from the current component, and feature vectors are constructed.Then, the sparrow search algorithm is used to optimize the weights of the support vector machine to obtain the optimal weights.Finally, the trained support vector machine is used to classify the test samples.In order to verify the effectiveness of the proposed method,an arc experimental platform was established to simulate the generation of arc faults in aviation line systems.AC series normal and arc fault current data were collected,and the SSA-SVM algorithm proposed in this paper is applied for arc fault detection.The results show that the method can effectively identify arc fault,with a detection accuracy of 99.5%,Compared to particle swarm optimization or genetic algorithm optimized support vector machines,the detection accuracy of arc fault is 2.5% and 2% higher, respectively.

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戴洪德,张志亮,崔伟成,等. 基于SSA-SVM的航空电弧故障检测[J]. 科学技术与工程, 2024, 24(13): 5626-5633.
Dai Hongde, Zhang Zhiliang, Cui Weicheng, et al. Aviation Arc Fault Detection Based on SSA-SVM[J]. Science Technology and Engineering,2024,24(13):5626-5633.

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  • 收稿日期:2023-08-22
  • 最后修改日期:2024-03-05
  • 录用日期:2023-10-10
  • 在线发布日期: 2024-05-17
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