AI 中文总结
针对电力系统故障电流相量估计精度不足的问题，提出SymbolicPhasor动态深度符号回归框架，通过滑动窗口学习解析表达式并结合谐波标记，在多条件下实现基波分量高精准估计，可用于继电保护等场景。
AI 中文摘要
电力系统故障期间的相量估计精度面临挑战，因为故障电流包含衰减直流偏移、谐波、噪声以及可能的频率偏差，这些失真会严重降低传统基于离散傅里叶变换的估计器的性能，尤其是在故障发生后的第一个周期内。本文提出了SymbolicPhasor，一种用于估计失真故障电流信号基波分量的动态深度符号回归框架。该方法通过重叠滑动窗口处理信号，学习每个窗口内完整波形的可解释解析表达式，随后将重构信号投影到标称正弦和余弦基上以恢复瞬时基波幅值和相位。通过嵌入对应标称、三次和五次谐波频率的符号标记，所提方法在保持数据驱动灵活性的同时，被引导向符合物理意义的表达式。该方法在单一衰减直流、多重衰减直流和非标称频率条件下进行评估，结果显示重构精度始终较高，决定系数达到0.985，表明所提框架可在一个周期内恢复基波分量，适用于实际的继电保护和测量应用。
英文摘要
Accurate phasor estimation during power system faults is challenging because fault currents contain decaying DC offsets, harmonics, noise, and possible frequency deviations. These distortions can significantly degrade conventional discrete Fourier transform-based estimators, especially during the first cycle after fault inception. This paper presents SymbolicPhasor, a dynamic deep symbolic regression framework for estimating the fundamental component of distorted fault current signals. The method processes the signal through overlapping moving windows, learns interpretable analytical expressions for the full waveform within each window, and then projects the reconstructed signal onto nominal sine and cosine bases to recover the instantaneous fundamental magnitude and phase. By embedding symbolic tokens corresponding to the nominal, third-, and fifth-order harmonic frequencies, the proposed approach is guided toward physically meaningful expressions while preserving data-driven flexibility. The method is evaluated under single decaying-DC, multiple decaying-DC, and off-nominal frequency conditions. Results show consistently high reconstruction accuracy, with coefficient of determination values reaching 0.985, demonstrating that the proposed framework can recover the fundamental component within one cycle for practical protective relaying and measurement applications.
Comments6 pages, 4 figures, Accepted, 58th North American Power Symposium (NAPS)
