A Frequency Domain Approach to Predict Power System Transients
A Frequency Domain Approach to Predict Power System Transients
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DOI：
10.1109/tpwrs.2023.3259960
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发表时间：
2021-11
影响因子：
6.6
通讯作者：
Wenqi Cui;Weiwei Yang;Baosen Zhang
中科院分区：
文献类型：
--
作者：
Wenqi Cui;Weiwei Yang;Baosen Zhang
电网的动力学是由大量的非线性微分方程和代数方程（DAEs）控制的。为了安全运行系统，操作人员需要在各种潜在故障发生后检查这些DAEs描述的状态是否保持在规定的范围内。然而，当前DAEs的数值解算器对于实时系统操作来说通常太慢。此外，详细的系统参数往往不确切地知道。已经提出了机器学习方法来减少计算工作量，但现有方法通常存在过拟合和预测不稳定行为失败的问题。本文提出了一种新的基于频域学习的电力系统暂态预测框架。直觉是，虽然系统行为在时域是复杂的，但在频域有相对较少的主导模式。因此，我们通过构建具有傅里叶变换和滤波层的神经网络来学习预测。系统拓扑和故障信息通过进行多维傅里叶变换进行编码，使我们能够利用轨迹在时间和空间频率上都是稀疏的这一事实。结果表明，该方法不需要详细的系统参数，大大加快了预测计算速度，对不同类型的故障具有较高的预测精度。
The dynamics of power grids are governed by a large number of nonlinear differential and algebraic equations (DAEs). To safely operate the system, operators need to check that the states described by these DAEs stay within prescribed limits after various potential faults. However, current numerical solvers of DAEs are often too slow for real-time system operations. In addition, detailed system parameters are often not exactly known. Machine learning approaches have been proposed to reduce the computational efforts, but existing methods generally suffer from overfitting and failures to predict unstable behaviors. This paper proposes a novel framework to predict power system transients by learning in the frequency domain. The intuition is that although the system behavior is complex in the time domain, there are relatively few dominant modes in the frequency domain. Therefore, we learn to predict by constructing neural networks with Fourier transform and filtering layers. System topology and fault information are encoded by taking a multi-dimensional Fourier transform, allowing us to leverage the fact that the trajectories are sparse both in time and spatial frequencies. We show that the proposed approach does not need detailed system parameters, greatly speeds up prediction computations and is highly accurate for different fault types.