The quality of current collection becomes a limiting factor when the aim is to increase the speed of the present railway systems. In this work an attempt is made to improve current collection quality optimising catenary geometry by means of a genetic algorithm (GA). As contact wire height and dropper spacing are thought to be highly influential parameters, they are chosen as the optimisation variables. The results obtained show that a GA can be used to optimise catenary geometry to improve current collection quality measured in terms of the standard deviation of the contact force. Furthermore, it is highlighted that apart from the usual pre-sag, other geometric parameters should also be taken into account when designing railway catenaries.

Catenaries are large cable structures which transmit electric current to trains through sliding contact with a pantograph. The finite element method is widely used to model this dynamic interaction problem and obtain the contact force between the pantograph and the catenary. As an alternative, analytical models can also be used to study catenary dynamics, although they require certain simplifications of the features considered in numerical models. In this paper, an analytical model composed of an infinite string and a visco-elastic support is introduced and enhanced by considering a Kelvin-Voigt damping model and the initial height of the contact wire. Considering the finite element (FE) model as a reference, the analytical model parameters are properly adjusted through static and wave propagation analyses to achieve similar behaviour in both the analytical and the FE models. To check the performance of the proposed model, the steady-state response of the pantograph–catenary coupled system is calculated and compared with the results of the FE model. Finally, the analytical model is used to analyse the interference phenomenon produced during two-pantograph operation.

The simulation of the pantograph–catenary dynamic interaction is at present mainly based on deterministic approaches. However, any errors made during the catenary stringing process are sources of variability that can affect the dynamic performance of the system. In this paper, we analyse the influence of dropper length, dropper spacing and support height errors on the current collection quality by applying a classic Monte Carlo method to obtain the probability density functions of several output quantities. The effects of installation errors are also studied for a range of train speeds. Finally, the pre-sag that, on average, produces the best behaviour of the system is identified, allowing for the uncertainty in the catenary installation. The results obtained show the convenience to consider variability in pantograph–catenary dynamic simulations.