摘要
航空交流系统工作环境复杂、故障电弧检测可靠性要求较高,而单一特征的检测方法适应能力相对较差。开展了航空交流电源条件下串联型故障电弧模拟试验,分别对电源频率为360 Hz、400 Hz、450 Hz时的线性负载线路电流进行数据采集。根据电弧电流的特点,提出了一种融合波形畸变特征、间谐波特征和能量分布不确定性特征的多维特征量检测方法。引入支持向量机和粒子群优化算法进行参数寻优,用训练得到的分类模型对测试集进行分类预测。结果表明,该串联故障电弧分类模型最高分类准确率可达到98.83%。
In view of the problem that arc fault detection of aviation AC system requires high reliability but generalization ability of the single feature for the arc fault detection method was poor with complex working environment. Arc fault simulation experiments in aviation AC system were carried out. Collecting current data from linear load line when the power frequency was 360 Hz,400 Hz and 450 Hz. According to the arc characteristics of aviation,a multi-dimension feature detection method of arc fault in aviation AC system was proposed which is fused with waveform distortion,inter harmonic and uncertainty of energy distribution. The support vector machine( SVM)optimized by particle swarm optimization( PSO). Testing set was classified forecast by using classification model.The results showed that,classification accuracy of the designed model was 98. 83%.
出处
《科学技术与工程》
北大核心
2017年第13期38-43,共6页
Science Technology and Engineering
基金
河北省自然科学基金青年基金(E2015202143)
河北省教育厅青年基金(QN2014148)资助
关键词
航空故障电弧
多维特征量
支持向量机
粒子群优化
aviation arc fault
multi-dimensional features
support vector machine(SVM)
particle swarm optimal(PSO)
