Sparse Wide-Area Control of Power Systems using Data-driven Reinforcement Learning

In this paper we present an online wide-area oscillation damping control (WAC) design for uncertain models of power systems using ideas from reinforcement learning. We assume that the exact small-signal model of the power system at the onset of a contingency is not known to the operator and use the nominal model and online measurements of the generator states and control inputs to rapidly converge to a state-feedback controller that minimizes a given quadratic energy cost. However, unlike conventional linear quadratic regulators (LQR), we intend our controller to be sparse, so its implementation reduces the communication costs. We, therefore, employ the gradient support pursuit (GraSP) optimization algorithm to impose sparsity constraints on the control gain matrix during learning. The sparse controller is thereafter implemented using distributed communication. Using the IEEE 39-bus power system model with 1149 unknown parameters, it is demonstrated that the proposed learning method provides reliable LQR performance while the controller matched to the nominal model becomes unstable for severely uncertain systems.Comment: Submitted to IEEE ACC 2019. 8 pages, 4 figure

Long Range Prediction of Fading Signals: Enabling Adaptive Transmission for Mobile Radio Channels

Recently it was proposed to adapt several transmission methods, including modulation, power control, channel coding and antenna diversity to rapidly time variant fading channel conditions. Prediction of the channel coefficients several tens-to-hundreds of symbols ahead is essential to realize these methods in practice. We describe a novel adaptive long range fading channel prediction algorithm (LRP) and its utilization with adaptive transmission methods. This channel prediction algorithm computes the linear Minimum Mean Squared Error (MMSE) estimates of future fading coefficients based on past observations. This algorithm can forecast fading signals far into the future due to its significant memory span, achieved by using a sufficiently low sampling rate for a given fixed filter size. The LRP is validated for standard stationary fading models, and tested with measured data and with data produced by our novel realistic physical channel model. This model accounts for the variation of the amplitude, frequency and phase of each reflected component of the fading signal. Both numerical and simulation results show that long range prediction makes adaptive transmission techniques feasible for mobile radio channels