基于PMU的机器学习在风电场强迫振荡快速检测中的应用
A PMU-Based Machine Learning Application for Fast Detection of Forced Oscillations from Wind Farms
摘要：
当今不断发展的电力系统包含越来越多的电力电子接口能源和负载，这需要公用事业运营的范式转变。例如，由于风电场控制器与电网的相互作用，公用事业公司已经报告了频率约为13-15 Hz的次同步振荡。由于低采样频率和缺乏同步，大多数SCADA工具无法观察到这种频率下的动态。相量测量单元（PMU）数据的实时或离线频域分析已成为识别此类现象的有价值的方法，但代价是昂贵的电力系统数据和通信基础设施。本文提出了一种基于机器学习（ML）的风电场次同步振荡检测应用。该应用程序的目标是在边缘实时实施，从而在数据和通信需求方面节省大量资金。验证使用来自北美风电场运营商的数据进行。
英文摘要：
Today's evolving power system contains an increasing amount of power electronic interfaced energy sources and loads that require a paradigm shift in utility operations. Sub-synchronous oscillations at frequencies around 13-15 Hz, for instance, have been reported by utilities due to wind farm controller interactions with the grid. Dynamics at such frequencies are unobservable by most SCADA tools due to low sampling frequencies and lack of synchronization. Real-time or off-line frequency domain analysis of phasor measurement unit (PMU) data has become a valuable method to identify such phenomena, at the expense of costly power system data and communication infrastructure. This article proposes an alternative machine learning (ML) based application for sub-synchronous oscillation detection in wind farm applications. The application is targeted for real-time implementation at the edge, resulting in significant savings in terms of data and communication requirements. Validation is performed using data from a North American wind farm operator.
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