Abstract
A fast multi-step prediction and rolling optimization excitation control method for multi-machine power system
Highlights
The synchronous generator excitation control system can maintain the voltage of generator terminals and pivot points at the reasonable range
The simulated results show that MPCGE based on reduced model (MPCGERM) is feasible, and the Gramian balanced reduction technique can be applied to a large-scale power system
The Gramian balanced reduction technique can decrease the memory storage occupied by MPCGE based on full-order model (MPCGEFM)
Summary
The synchronous generator excitation control system can maintain the voltage of generator terminals and pivot points at the reasonable range It is one of the most efficient methods to improve power system stability. For MPC of generator excitation (MPCGE) in power system, most researchers directly derive the analytical and/or single-step prediction optimization control law based on power system model [13,14,15]. These methods need not solve dynamic optimization problems. In this method multi-step prediction and rolling optimization are realized. A fast algorithm, i.e. low-rank Cholesky factor-alternation direction implicit (LRCF-ADI), is used to solve the large-scale Lyapunov equations in the Gramian balanced reduction
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