Power System Oscillation Mode Prediction Data

This paper utilizes modern statistical and machine learning methodology to track oscillation modes in complex power engineering systems. The damping ratio of the electromechanical oscillation mode is formulated as a function of power of the generators and loads as well as bus voltage magnitudes in the entire power system. The celebrated Lasso algorithm is implemented to solve this high-dimension modeling problem. By the nature of the $L_1$ design, the Lasso algorithm can automatically render a sparse solution, and by eliminating redundant features, it provides desirable prediction power. The resultant model processes a simple structure, and it is easily interpretable. The precision of our sparse modeling framework is demonstrated in the context of an IEEE 50-Generator 145-Bus power network.本文采用现代统计学与机器学习方法，对复杂电力工程系统中的振荡模式进行追踪分析。本文将机电振荡模式（electromechanical oscillation mode）的阻尼比建模为全电力系统中发电机出力、负荷功率与母线电压幅值的函数。本文采用经典的套索算法（Lasso）求解该高维建模问题：基于L1正则化的设计特性，套索算法可自动生成稀疏解；通过剔除冗余特征，该算法能够获得优异的预测性能。所得模型结构简洁，且具备良好的可解释性。本文在IEEE 50机145母线电力系统场景下，验证了所提出的稀疏建模框架的预测精度。