Application of differential evolution in harmonic worst-case identification of mass rapid transit power supply system

Mass rapid transit operation results in voltage and current harmonic distortions in a.c. suppy systems. Power-supply standards limit the acceptable harmonic-distortion levels. In order to observe these limits, it is necessary to identify the worst-case harmonic distortions in MRT system. Train operating modes and system configurations affect the level of harmonic distortions. Harmonic worst-case identification is treated as an optimization problem in terms of train separations and traffic conditions. The approach uses approximate train movement and consumption models, and a.c./d.c. harmonic loadflow for evaluating the harmonic distortions. This paper uses a new method called Differential Evolution (DE) for solving the problem. Parallel studies using genetic algorithim are carried out. Comparative results demonstrate the favourable features of DE for large-scale optimization with real variables, as the method is efficient and rapidly converging.