STFT Cluster Analysis for DC Pulsed Load Monitoring and Fault Detection on Naval Shipboard Power Systems
STFT Cluster Analysis for DC Pulsed Load Monitoring and Fault Detection on Naval Shipboard Power Systems
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DOI：
10.1109/tte.2020.2981880
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发表时间：
2020-06-01
影响因子：
7
通讯作者：
Ma, Yue
中科院分区：
文献类型：
--
作者：
Maqsood, Atif;Oslebo, Damian;Ma, Yue
当前海军舰船电力系统架构的发展趋势表明，未来舰船的电气化是不可避免的，它将配备从直流微电网中汲取周期性脉冲电流或在切换状态时具有大瞬变的负载。为了监测这些负载的操作，仅仅基于时间的特征是不够的，因为它们不能提供足够独特的信息来区分负载分布的各种瞬态阶段。最近的研究重点是提取时频特征。然而，不存在可以适用于任何一般负载配置文件的综合解决方案。本文提出的负载监测和故障检测方法概述了一种基于数据聚类的方法，从任何脉冲负载的短时傅里叶变换分析中提取独特的特征向量。然后，这些特征可用于识别负载瞬态中的各种事件以及分流故障和串联电弧故障。包括几个负载配置文件和故障情况下的计划的实施和性能。
Current trends in Naval shipboard power system architecture indicate that the electrification of future warships is inevitable, and it will be equipped with loads that draw periodic pulsed currents from the dc microgrid or have large transients while switching state. In order to monitor the operation of those loads, solely time-based features are not enough as they do not provide sufficiently unique information to differentiate various transient stages of the load profile. The focus of more recent research has been on extracting time-frequency features. However, no comprehensive solution exists that could work for any general load profile. The proposed load monitoring and fault detection method presented in this article outlines a data clustering-based approach to extract unique feature vectors from short-time Fourier transform analysis for any pulsed load. These features can then be used to identify various events in the load transient as well as shunt faults and series arcing faults. Implementation and performance of the scheme for several load profiles and fault scenarios are included.