Disruptive event classification using PMU data in distribution networks
Disruptive event classification using PMU data in distribution networks
复制标题
使用配电网络中的 PMU 数据进行破坏性事件分类
DOI：
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发表时间：
2017
期刊：
影响因子：
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通讯作者：
H. Livani
中科院分区：
文献类型：
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作者：
I. Niazazari;H. Livani
配电网中具有高采样率的先进计量装置如微相量测量单元(μPMU)的普及，为广域监测和诊断应用提供了前所未有的潜力，例如，对配电资产的态势感知、健康监测。随着时间的推移，中断配电网中资产正常运行的意外中断事件可能最终导致永久性故障，并带来昂贵的重置成本。因此，故障事件分类为配电网资产的预防性维护提供了有用的信息。预防性维护在时间方面提供了广泛的好处，避免了意外停机、维护人员利用率和设备更换成本。提出了一种基于PMU数据驱动的配电网中断事件分类框架。将故障电容器组投切和故障调整器有载分接开关(OLTC)投切这两种故障事件与配电网正常的负荷突变区分开来。通过对IEEE13节点配电网事件的仿真，验证了该框架的有效性。事件分类使用两种不同的算法：i)主成分分析(PCA)与多类支持向量机(SVM)相结合；ii)自动编码器与Softmax分类器相结合。实验结果证明了该算法的有效性和令人满意的分类精度。
Proliferation of advanced metering devices with high sampling rates in distribution grids, e.g., micro-phasor measurement units (μPMU), provides unprecedented potentials for wide-area monitoring and diagnostic applications, e.g., situational awareness, health monitoring of distribution assets. Unexpected disruptive events interrupting the normal operation of assets in distribution grids can eventually lead to permanent failure with expensive replacement cost over time. Therefore, disruptive event classification provides useful information for preventive maintenance of the assets in distribution networks. Preventive maintenance provides wide range of benefits in terms of time, avoiding unexpected outages, maintenance crew utilization, and equipment replacement cost. In this paper, a PMU-data-driven framework is proposed for classification of disruptive events in distribution networks. The two disruptive events, i.e., malfunctioned capacitor bank switching and malfunctioned regulator on-load tap changer (OLTC) switching are considered and distinguished from the normal abrupt load change in distribution grids. The performance of the proposed framework is verified using the simulation of the events in the IEEE 13-bus distribution network. The event classification is formulated using two different algorithms as; i) principle component analysis (PCA) together with multi-class support vector machine (SVM), and ii) autoencoder along with softmax classifier. The results demonstrate the effectiveness of the proposed algorithms and satisfactory classification accuracies.