多振动信息下的高压断路器机械故障随机森林融合诊断方法  

Mechanical Fault Fusion Diagnosis of High Voltage Circuit Breaker Using Multi-Vibration Information Based on Random Forest

作　　者：马速良[1] 武建文[1] 袁洋 贾博文[1] 罗晓武 李维新 Ma Suliang;Wu Jianwen;Yuan Yang;Jia Bowen;Luo Xiaowu;Li Weixin(School of Automation Science and Electrical Engineering Beihang University,Beijing 100191 China)

机构地区：[1]北京航空航天大学自动化科学与电气工程学院,北京100191

出　　处：《电工技术学报》2020年第S02期421-431,共11页Transactions of China Electrotechnical Society

基　　金：国家自然科学基金(51677002,51977002);北京航空航天大学博士研究生卓越学术基金资助项目。

摘　　要：高压断路器的健康状况对电力系统有着重要影响。随着人工智能的发展,众多先进方法被应用于高压断路器的故障类型识别。目前,相关研究大多致力于改进基于单个传感器的特征提取过程或分类方法,以获得更高精度。然而,改进后的方法只能接近于数据信息所决定的上限,忽视了单一信息对故障辨识能力的有限性。因此,该文提出一种基于随机森林的多传感器联合决策方法。首先,分析不同位置振动信息在典型故障下的特征差异;然后,从随机森林出发,设计多传感器融合诊断过程;最后,基于高压断路器实验平台,对比六种典型分类器和不同传感器组合下随机森林融合方法的诊断结果,验证了所提方法可以显著提高故障诊断性能,为推动高压断路器故障定位应用提供了新思路。Healthy condition of high voltage circuit breaker(HVCB)has an important impact on the power system.With the development of artificial intelligence,many advanced methods have been applied to fault type identification of HVCBS.At present,most related researches are devoted to improving the feature extraction process or the classification method based on a single sensor to obtain a higher accuracy.However,the improved method can only approach the upper limit determined by data information,ignoring the limited ability of a single information to identify faults.Therefore,this study has proposed a multi-sensor joint decision approach based on random forest.Firstly,under the typical faults condition,the differences of vibration characteristic at the different locations are analyzed.Then,based on a random forest algorithm,a multi-sensor fusion diagnosis process is designed.Finally,based on the HVCB experimental platform,the results of six typical classifiers and random forest fusion method under different sensor combinations are compared to verify that the proposed method can significantly improve fault diagnosis performance and provide new ideas for promoting the application of HVCB fault location.

关 键 词：高压断路器 故障诊断 振动信号 随机森林 多传感器信息融合 

分 类 号：TM561[电气工程—电器]