Inferring Power System Frequency Oscillations using Gaussian Processes
Inferring Power System Frequency Oscillations using Gaussian Processes
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DOI：
10.1109/cdc45484.2021.9683760
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发表时间：
2021-12
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影响因子：
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通讯作者：
M. Jalali;V. Kekatos;Siddharth Bhela;Hao Zhu
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文献类型：
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作者：
M. Jalali;V. Kekatos;Siddharth Bhela;Hao Zhu
同步数据为推断电力系统总线上的电压频率和频率变化率（ROCOF）提供了前所未有的机会。为了实现这一目标，本文提出了一个新的框架，通过利用高斯过程（GP）的工具来学习小信号干扰后的动态。我们扩展的结果推断的输入和输出的线性时不变系统，利用全球定位系统的多输入多输出设置利用电力系统摆动动态。这种物理感知学习技术在连续时间内捕获时间导数，适应可能以不同速率采样的数据流，并可以科普丢失的数据和异构的准确性水平。虽然基于卡尔曼滤波器的方法需要知道所有系统输入，但所提出的框架处理总线的任意子集上的系统输入、输出、它们的导数及其组合的读数。依靠最少的系统信息，它进一步提供了不确定性量化，除了点估计动态网格信号。所需的时空协方差通过探索由环境扰动驱动的近似摆动动力学的统计特性。数值试验验证，该技术可以推断频率和ROCOF在非计量总线下（非）环境干扰的IEEE 300总线基准的线性化动态模型。
Synchronized data provide unprecedented opportunities for inferring voltage frequencies and rates of change of frequencies (ROCOFs) across the buses of a power system. Aligned to this goal, this work puts forth a novel framework for learning dynamics after small-signal disturbances by leveraging the tool of Gaussian processes (GPs). We extend results on inferring the input and output of a linear time-invariant system using GPs to the multi-input multi-output setup by exploiting power system swing dynamics. This physics-aware learning technique captures time derivatives in continuous time, accommodates data streams sampled potentially at different rates, and can cope with missing data and heterogeneous levels of accuracy. While Kalman filter-based approaches require knowing all system inputs, the proposed framework handles readings of system inputs, outputs, their derivatives, and combinations thereof on an arbitrary subset of buses. Relying on minimal system information, it further provides uncertainty quantification in addition to point estimates for dynamic grid signals. The required spatiotemporal covariances are obtained by exploring the statistical properties of approximate swing dynamics driven by ambient disturbances. Numerical tests verify that this technique can infer frequencies and ROCOFs at non-metered buses under (non)-ambient disturbances for a linearized dynamic model of the IEEE 300-bus benchmark.