A Data-driven Convex-optimization Method for Estimating Load Changes

This paper presents an optimization-based method to detect the occurrence, estimate the magnitude, and identify the location of load changes in the power system. The proposed method relies on measurements of only frequency at the output of synchronous generators along with a reduced-order power system dynamical model that captures locational effects of load disturbances on generator frequency dynamics. These locational aspects are retained in the estimation model by incorporating linearized power-flow balance into differential equations that describe synchronous-generator dynamics. The sparsity structure of load-change disturbances is leveraged so that only a limited number of measurements are needed to estimate load changes. Furthermore, a convex relaxation of the problem ensures that it can be solved online in a computationally efficient manner. Time-domain simulations involving the Western Electricity Coordinating Council 9-bus test system demonstrate the accuracy of the proposed method.