Worst-case identification of touch voltage and stray current of DC railway system using genetic algorithm

The problem of reducing the touch voltage and stray current in DC railways is multiobjective and conflicting. It is affected by many factors such as the earthing and bonding design, as well as the normal and failure operating conditions. An approach of genetic algorithm based multiobjective optimisation is proposed to identify the worst-case touch voltage and stray current in MRT systems. A two-step design scheme is formulated to represent both the normal and the failure conditions. The method of Pareto-optimal sets is developed to best improve the touch voltages and stray current integral for the normal condition. The decision-maker is given a powerful tool for picking the most appropriate earthing and bonding design from the set, and for identifying the worst-case performance from the list of credible failure conditions. Simulation results are presented which demonstrate the effectiveness of the proposed approach in fulfilling the design objectives