Bounding Regression Errors in Data-Driven Power Grid Steady-State Models

• Published in: IEEE transactions on power systems Vol. 36; no. 2; pp. 1023 - 1033
• Main Authors: Liu, Yuxiao, Xu, Bolun, Botterud, Audun, Zhang, Ning, Kang, Chongqing
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.03.2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Analytical models; Complexity theory; Data models; Electric power grids; Evaluation; Learning theory; linear regression; Picture archiving and communication systems; power flow; Power grids; Rademacher complexity; Regression analysis; Steady state models; support vector machine; Training; Training data

Data-driven models analyze power grids under incomplete physical information, and their accuracy has been mostly validated empirically using certain training and testing datasets. This paper explores error bounds for data-driven models under all possible training and testing scenarios drawn from an...