论文标题
基于PMU的机器学习应用程序，用于快速检测风电场的强制振荡
A PMU-Based Machine Learning Application for Fast Detection of Forced Oscillations from Wind Farms
论文作者
论文摘要
当今的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，这些电源需要范式转移实用程序操作。例如，由于风电场控制器与电网的相互作用，公用事业已经报道了频率约13-15 Hz的频率的次同步振荡。大多数SCADA工具由于采样频率低和缺乏同步而无法观察到这种频率的动力学。相量测量单元（PMU）数据的实时或离线频域分析已成为确定这种现象的宝贵方法，以牺牲昂贵的电力系统数据和通信基础架构为代价。本文提出了一种基于替代机器学习（ML）的应用程序，用于在风电场应用中进行次同步振荡检测。该应用程序的目标是在边缘实时实现，从而在数据和通信需求方面节省了大量资金。使用北美风电场运营商的数据进行验证。
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.
