2021 IEEE-NASPI振荡源定位竞赛:啄木鸟队

Guoyan Zheng, Honggang Wang, Shaopeng Liu, M. Farrokhifard
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引用次数: 4

摘要

由IEEE和北美同步相量倡议(NASPI)组织的2021年振荡源定位(OSL)竞赛旨在评估OSL方法的效率及其在实际实施中的适用性。为参与者提供了一个基本模型和13个模拟区域间机电振荡和/或强迫振荡(FOs)的现实挑战的测试数据集。测试场景包括fo与自然模式共振、故障诱发振荡、各种源位置、资产类型和控制器类型。本文对竞赛设计进行了评论,并介绍了啄木鸟团队的获奖方法,该方法突出了(i)在探索候选源时的物理指导模式匹配,以及(ii)基于模型的分析来验证源。特别是,啄木鸟展示了基于机器学习模式识别(ML-PR)的OSL方法对耗散能量流(DEF)方法的补充。这种方法可以根据可用的pmu识别振荡源位置,即使源没有被监控。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
2021 IEEE-NASPI Oscillation Source Location Contest: Team Woodpecker
The 2021 Oscillation Source Location (OSL) Contest organized by IEEE and North American SynchroPhasor Initiative (NASPI) aimed at evaluating the efficiency of OSL methods and their applicability for practical implementation. The participants were provided with a base model and thirteen test data sets mimicking the real-world challenges of inter-area electromechanical oscillations and/or forced oscillations (FOs). Testing scenarios include FOs resonating with natural modes, faults induced oscillations, various source locations, asset types and controller types. This paper comments on the contest design and presents the top awarded method by team Woodpecker, which highlights (i) physics-guided pattern matching in exploring the sources candidates, and (ii) model-based analytics to verify the source. In particular, Woodpecker demonstrated the usefulness of machine learning pattern recognition (ML-PR) based OSL method for complementing the dissipating energy flow (DEF) method. This approach can identify the oscillation source location based on available PMUs even when the source is not monitored.
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