Estimation of rotor angles of synchronous machines using artificial neural networks and local PMU-based quantities

This paper investigates a possibility for estimating rotor angles in the time frame of transient (angle) stability of electric power systems, for use in real-time. The proposed dynamic state estimation technique is based on the use of voltage and current phasors obtained from a phasor measurement unit supposed to be installed on the extra-high voltage side of the substation of a power plant, together with a multilayer perceptron trained off-line from simulations. We demonstrate that an intuitive approach to directly map phasor measurement inputs to the neural network to generator rotor angle does not offer satisfactory results. We found out that a good way to approach the angle estimation problem is to use two neural networks in order to estimate the sin(@d) and cos(@d) of the angle and recover the latter from these values by simple post-processing. Simulation results on a part of the Mexican interconnected system show that the approach could yield satisfactory accuracy for real-time monitoring and control of transient instability.