Online Learning Based Reduced-Order Broadcast Control for Damping Power System Oscillations

We present an online system-identification based control design for damping inter-area oscillations in power systems. The identification is based on a partly reduced-order model and a partly full-order model of the grid. Assuming the grid to be divided into coherent areas, the coherent states of the generators are averaged while the non-coherent states, such as the internal states of each power system stabilizer, are not. A hybrid state vector consisting of a mixture of these averaged and non-averaged states is computed, and used for identifying a hybrid state-space model. A linear quadratic regulator (LQR) based on this hybrid model is, thereafter, designed. The reducedorder part of the model is found to save significant amount of online time for both learning and control, while retaining the full-order part is found to enhance closed-loop stability. N4SID with randomized singular value decomposition (rSVD) is used for making the identification loop fast. Results are validated using the IEEE 68-bus power system model.