  In a railroad feeding system, detecting a location of pole to earth faults is
important for safe operation of the system. The goal of this paper is to use a
combination of the evolutionary algorithm and neural networks to increase the
accuracy of single pole-to-earth fault detection and location on Tehran
railroad power supply system. Accordingly, Imperialist Competitive Algorithm
(ICA) and Particle Swarm Optimization (PSO) are used to train the neural
network for enhancing learning process accuracy and the convergence. Owing to
the nonlinearity of system, the fault detection is an ideal application for the
proposed method where 600 Hz harmonic ripple method is used in this paper for
fault detection. The substations were simulated by considering various
situations in feeding the circuit, the transformer and the silicon rectifier
has been developed by typical Tehran metro parameters. Required data for the
network learning the process have been gathered from simulation results. 600Hz
components value will change with the change of the location of single pole to
earth fault. Therefore, 600Hz components are used as inputs of the neural
network when fault location is the output of the network system. The simulation
results show that the fault location can be accurately predicted in proposed
methods.
