On the Impact of Measurement Errors on Power System Automatic Generation Control

In this paper, we propose a framework to evaluate the impact on power system dynamic performance of different types of errors in the measurements utilized by the automatic generation control (AGC) system. To address the random nature of these errors, stochastic system analysis methods are utilized to evaluate the statistics of system state variables. By examining the convergence properties of these statistics, errors that cause instability are identified. A reduced-order model, obtained by using singular perturbation arguments, is also formulated that enables us to provide analytical expressions capturing the impact of the errors. The proposed method is illustrated and verified through several case studies with different types of errors on a simplified New England/New York system model.