Recurrent neural networks for phasor detection and adaptive identification in power system control and protection

A multi-input multi-output (MIMO) recurrent neural network (RNN) is used as a versatile tool for the high-speed phasor detection and the adaptive identification of control and protection signals in power systems. For the application as a phasor detector, a fast pseudo-gradient training is performed off-line to estimate the time-invariant weights of the RNN. This network is then operated in real-time, in recall mode only, to behave as a nonlinear fixed-coefficient filter. For the application as an adaptive identifier of nonlinear components, training is performed off-line for initializing the connection weights, but subsequently, they are continuously updated in real time. This results in an adaptive identifier suitable for detecting abrupt changes in complex nonlinear systems. Following an initial evaluation on synthetic signals, these two proposed RNNs are then validated using realistic waveforms generated from a series-compensated power system model