摘要翻译：
我们提出了一个基于物理信息的高斯过程回归(GPR)模型来预测相角、角速度和风力机械功率。在传统的数据驱动探地雷达方法中，高斯过程协方差矩阵的形式是假定的，其参数是由测量数据确定的。在物理信息探地雷达中，我们将未知变量（包括风速和机械功率）视为一个随机过程，并根据得到的随机电网方程计算协方差矩阵。结果表明，基于物理信息的探地雷达方法对发电机角速度和相角的实时预测比基于数据驱动的方法准确得多。我们还表明，当仅从这些变量中的一个可获得测量值时，物理信息探地雷达提供了对未观测到的风机械功率、相位角或角速度的准确预测。对观测变量的即时预测和对未观测变量的预测可用于有效地管理电网（电力市场出清、管制行动）和早期发现异常行为和故障。基于物理的探地雷达预测时域取决于输入（风电、负荷等）相关时间和电网特征（松弛）时间的组合，并可扩展到短程和中程时间。
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英文标题：
《Physics-informed Machine Learning Method for Forecasting and Uncertainty
  Quantification of Partially Observed and Unobserved States in Power Grids》
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作者：
Ramakrishna Tipireddy and Alexandre Tartakovsky
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最新提交年份：
2018
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分类信息：

一级分类：Electrical Engineering and Systems Science        电气工程与系统科学
二级分类：Signal Processing        信号处理
分类描述：Theory, algorithms, performance analysis and applications of signal and data analysis, including physical modeling, processing, detection and parameter estimation, learning, mining, retrieval, and information extraction. The term "signal" includes speech, audio, sonar, radar, geophysical, physiological, (bio-) medical, image, video, and multimodal natural and man-made signals, including communication signals and data. Topics of interest include: statistical signal processing, spectral estimation and system identification; filter design, adaptive filtering / stochastic learning; (compressive) sampling, sensing, and transform-domain methods including fast algorithms; signal processing for machine learning and machine learning for signal processing applications; in-network and graph signal processing; convex and nonconvex optimization methods for signal processing applications; radar, sonar, and sensor array beamforming and direction finding; communications signal processing; low power, multi-core and system-on-chip signal processing; sensing, communication, analysis and optimization for cyber-physical systems such as power grids and the Internet of Things.
信号和数据分析的理论、算法、性能分析和应用，包括物理建模、处理、检测和参数估计、学习、挖掘、检索和信息提取。“信号”一词包括语音、音频、声纳、雷达、地球物理、生理、（生物）医学、图像、视频和多模态自然和人为信号，包括通信信号和数据。感兴趣的主题包括：统计信号处理、谱估计和系统辨识；滤波器设计；自适应滤波/随机学习；（压缩）采样、传感和变换域方法，包括快速算法；用于机器学习的信号处理和用于信号处理应用的机器学习；网络与图形信号处理；信号处理中的凸和非凸优化方法；雷达、声纳和传感器阵列波束形成和测向；通信信号处理；低功耗、多核、片上系统信号处理；信息物理系统的传感、通信、分析和优化，如电网和物联网。
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英文摘要：
  We present a physics-informed Gaussian Process Regression (GPR) model to predict the phase angle, angular speed, and wind mechanical power from a limited number of measurements. In the traditional data-driven GPR method, the form of the Gaussian Process covariance matrix is assumed and its parameters are found from measurements. In the physics-informed GPR, we treat unknown variables (including wind speed and mechanical power) as a random process and compute the covariance matrix from the resulting stochastic power grid equations. We demonstrate that the physics-informed GPR method is significantly more accurate than the standard data-driven one for immediate forecasting of generators' angular velocity and phase angle. We also show that the physics-informed GPR provides accurate predictions of the unobserved wind mechanical power, phase angle, or angular velocity when measurements from only one of these variables are available. The immediate forecast of observed variables and predictions of unobserved variables can be used for effectively managing power grids (electricity market clearing, regulation actions) and early detection of abnormal behavior and faults. The physics-based GPR forecast time horizon depends on the combination of input (wind power, load, etc.) correlation time and characteristic (relaxation) time of the power grid and can be extended to short and medium-range times.
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PDF链接：
https://arxiv.org/pdf/1806.1099