Research On Optimization Of Energy Saving Based On Particle Swarm Algorithm

The simulation of train performance and power system of the Urban Rail Transit have important significance for its design and operation. The energy consumption is great, and it’s urgent to research the methods of energy-saving. The main researches in this paper are as following:1.In dependence of Nanjing Metro Line One and data of the locomotive, multi-particle simulation model was built and the results were compared to the measured data to adjust the parameters, improving the model closed to reality.2.Train traction control strategy depicted by the working sequence table and condition transformation point was optimized through the intelligent particle swarm algorithm coded in real number, the energy saving effect was remarkable.3. The whole model of DC traction power system was built in this article, and the working condition of the vehicle braking resistor was also taken into account. The node admittance matrix of the time-varying network was stored by orthogonal linked list, and the nonlinear equations of the system were solved in quasi-Newton method.4. The integer-coded particle swarm algorithm was asserted to optimize the train schedule to attain the goal of energy saving, the optimized consequence of the posed proposal was verified by two simple models.5.The asynchronous and parallel mode of PSO was analyzed, multiple computers on the Internet could be used. The algorithm of parallel processing was complied in the client-server mode, the speed of the optimization was exponentially accelerated. The optimization of parallel execution model was applied in Nanjing Metro Line One and South Extension Line, and the net energy saving rate was greater than3%.Finally, the frame of the simulation and optimization software was designed, the main process, the main implementation methods and results output display were analyzed, and the "URT System Simulation and Energy-Saving Optimization Software" was perfectly realized.