… timal power flow (OPF) based on the stacked extreme learning machine (SELM) framework.
SELM has a fast training speed and does not require the time-consuming parameter tuning …

… machine learning techniques. Although machine learning techniques are studied for many
power systems problems, the literature on machine learning for OPF applications is limited. …

… machine learning methods for AC Optimal Powerflow (ACOPF) - the task of optimizing
power … We present two formulations of ACOPF as a machine learning problem: 1) an end-to-end …

… As should be clear, this tutorial focuses on optimal power flow applications. For a general
presentation of machine learning in power systems, readers can consult the comprehensive …

… Accordingly, we introduce a concise framework for generalizing methods for machine
learning … For several synthetic power grids with interconnected utilities, we show that locality …

… power flow (OPF) methods based on machine learning. By exploiting the information in
local exogenous disturbance variables, a machine learning … In concert, all machine learning …

… This paper developed a machine learning-aided method for solving security … power flow,
which integrates both power network equations and a multi-input multi-output machine learning …

… power flow equations with a linearized version. After we obtain the OPF setpoints, we run an
exact power flow … voltages from the exact BFS power flow. These inner iterations are carried …

… Abstract—The AC Optimal Power Flow (AC-OPF) is a key building block in many power
system applications. It determines generator setpoints at minimal cost that meet the power de…

… This paper has demonstrated that machine learning can be used to create effective network
state-based algorithm selectors for power flow management. A method has been presented …