Multi-Task Logistic Low-Ranked Dirty Model for Fault Detection in Power Distribution System
Multi-Task Logistic Low-Ranked Dirty Model for Fault Detection in Power Distribution System
复制标题
DOI：
10.1109/tsg.2019.2938989
复制
发表时间：
2020-01
影响因子：
9.6
通讯作者：
Mostafa Gilanifar;Jose Cordova;Haibo Wang;M. Stifter;E. Ozguven;T. Strasser;R. Arghandeh
中科院分区：
文献类型：
--
作者：
Mostafa Gilanifar;Jose Cordova;Haibo Wang;M. Stifter;E. Ozguven;T. Strasser;R. Arghandeh
提出了一种基于配电相量测量单元（PMU）数据的配电网故障检测多任务逻辑斯蒂低秩脏模型（MT-LLRDM）。MT-LLRDM通过利用配电网络中多个位置之间的故障数据流中的相似性来提高故障检测精度。所捕获的相似性将信息补充到感兴趣位置处的故障检测任务，创建多任务学习框架，从而提高学习准确性。该算法是验证与实时PMU流从硬件在环试验台，仿真真实的现场通信和监测条件的配电网。结果表明，使用从电力硬件在环测试台获得的实际同步相量数据，MT-LLRDM的性能优于其他最先进的分类方法。
This paper proposes a Multi-task Logistic Low-Ranked Dirty Model (MT-LLRDM) for fault detection in power distribution networks by using the distribution Phasor Measurement Unit (PMU) data. The MT-LLRDM improves the fault detection accuracy by utilizing the similarities in the fault data streams among multiple locations across a power distribution network. The captured similarities supplement the information to the task of fault detection at a location of interest, creating a multi-task learning framework and thereby improving the learning accuracy. The algorithm is validated with real-time PMU streams from a hardware-in-the-loop testbed that emulates real field communication and monitoring conditions in distribution networks. The results showed that the MT-LLRDM outperforms other state-of-the-art classification methods using actual synchrophasor data achieved from a power hardware-in-the-loop testbed.