Detecting power grid frequency events from µPMU voltage phasor data using machine learning
Detecting power grid frequency events from µPMU voltage phasor data using machine learning
复制标题
使用机器学习从 µPMU 电压相量数据检测电网频率事件
DOI：
10.1049/icp.2022.1686
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发表时间：
2022
期刊：
影响因子：
--
通讯作者：
Dey M
中科院分区：
文献类型：
--
作者：
Dey M
配电网上可再生能源发电量的增加正在导致系统运营商以前从未经历过的新的和前所未有的挑战。异步发电的增加和电网上旋转电力负载的减少导致系统范围内惯性的相应减少。惯性下降导致频率和频率变化率（RoCoF）偏移的数量增加，如果不成功管理，则可能导致不必要的负载损失、发电机跳闸和对电网上的资产的损坏。因此，本文研究了使用来自微同步相量测量单元（μ PMU）的高精度电压相量数据来使用无监督机器学习技术检测电力系统频率异常事件。一个组合的特征窗口选择，聚类大型应用程序（CLUSTING LARGE APPLICATION）和自适应阈值方法已被用来检测频率事件。本文采用了真实的数据从两个并网太阳能发电场在英国诺福克，以评估检测异常频率事件的有效性，这也与传统的电能质量记录进行了比较，以验证和评估本文概述的方法。
The increase in renewable power generation on the electrical distribution grid is leading to new and unprecedented challenges that system operators have not previously experienced. The increase in asynchronous generation and decrease in rotating electrical loads on the grid are causing a commensurate reduction in system wide inertia. Declining inertia leads to an increased number of frequency and Rate of Change of Frequency (RoCoF) excursions, which if not successfully managed, may lead to unwanted loss of load, generator trips, and damage to assets on the grid. This paper, therefore, investigates the use of high-accuracy voltage phasor data from micro-synchrophasor measurement unit (μPMU) to detect anomalous power system frequency events using unsupervised machine learning techniques. A combination of a feature window selection, Clustering LARge Applications (CLARA) and an adaptive thresholding method has been employed to detect frequency events. This paper has employed real data from two grid-connected solar farms in Norfolk, England, to assess effectiveness in detecting anomalous frequency events, which have also been compared with conventional power quality recordings to validate and assess the method outlined in this paper.