Automatic traffic regulation plays an important role in high-frequency metro lines. This paper introduces a discrete event nonlinear traffic model for metro loop lines based on the deviations from nominal departure times. This model considers the transfer of knock-on delay from one train to another one and improves on previous models. The model also includes uncertainty to accommodate variation in passenger demand over the course of a work day. The nonlinear uncertain traffic model is approximated by the linear uncertain model which facilitates the design of a robust predictive controller. The objective of control is increasing the passenger satisfaction by minimizing the deviation from the nominal time schedule and keeping headways close to the nominal headway. The linear robust model predictive controller (RMPC) is employed to compensate the disturbances and to regulate traffic in the presence of operational constraints. YALMIP toolbox was used to design the RMPC. Model verification and simulations were carried out using real data from Tehran metro line 4. The simulation results are presented to show the trueness of the model and effectiveness of the proposed control methodology.

This paper presents a method for estimating the equivalent circuit parameters of three-phase induction motors by employing the extended Kalman filter. In this method, the stator currents and voltages, and rotor speed of the motor under an unspecified load and in unsaturated condition are considered. The rotor resistance, mutual inductance, and stator leakage inductance are estimated using the synchronous reference frame in the steady-state mode. Also, the rotor leakage inductance is estimated using the rotor reference frame in the transient mode. Experimental data are used to validate the analytical technique.

Any reduction in the cross-sectional area of wires (RCSAW) in the stator or rotor (field or damper) windings of synchronous generators (SGs) is considered as a fault in SGs. In this paper, a signal-model-based fault detection, isolation, and estimation approach based on the eigenvalues estimation of SGs is presented to diagnose RCSAW fault. The subspace identification method is employed to estimate the state matrix eigenvalues. A form of the characteristic equation in the faulty condition is introduced for fault diagnosis of SGs in the presence of linear and nonlinear loads. The stator and field currents and voltages, and the rotor speed are used for fault diagnosis. The effectiveness of the introduced fault diagnosis method is shown using simulation and experimental results on a salient-pole SG in Iran-Safir (ER24) diesel-electric locomotives.