期刊
IEEE ACCESS
卷 8, 期 -, 页码 101068-101078出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2020.2980983
关键词
Small-signal stability; electromechanical oscillations; system identification; mode damping prediction; Lasso; sparse modeling machine learning; online learning
资金
- National Natural Science Foundation of China [51937005]
- Polish National Centre of Science [DEC-2018/31/N/ST7/03977]
This paper utilizes modern statistical and machine learning methodology to predict the oscillation mode of interest in complex power engineering systems. The damping ratio of the electromechanical oscillation mode is formulated as a function of the 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 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 and an online learning framework for the power system oscillation mode prediction is also provided.
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