Energy-Saving Optimization And Control For High-Speed Trains Tracking Operation

Recently,China is energetically advocating the construction of a conservation oriented society,and the key of realization is to reduce or minimize the waste of resources.With the development of high-speed railways in China,high-speed train is becoming an important and indispensable part of travel tools.But,with the increase of running mileage and the larger overall traffic volume,the tremendous energy consumption of trains cannot be ignored.The problem of reducing the energy consumption in train operation has become a very hot research topic,which is concerned by many scholars at home and abroad.Therefore,it is of great significance to study the problem of train operation optimization.The study of this paper can extends from the optimization of the speed curve of the single train to the tracking train at multi-train tracking operation.Based on the target velocity curve,a speed controller of the ATO(Automatic Train Operation)system with self-tuning parameters is designed to ensure the train to follow the target velocity.The main work of this paper is as follows:(1)The traction characteristic of the train is analyzed,and the single point model of the train is established.On this basis,the principle of operation condition of the train is analyzed in depth.At last,the calculation model of train running between stations is established.(2)The optimization of single-train energy-saving operation speed by the real number crossbreeding multi-objective particle swarm algorithm.Firstly,energy optimization strategy of single train is given.Secondly,the multi-objective optimization model of energy consumption and running time is established for different operation conditions,and constraints are dealt with.Thirdly,considering the problem of multi-objective particle swarm optimization falling into the local optimum,the multi-objective particle swarm optimization algorithm based on real number crossbreeding is proposed based on the concept of crossbreeding in genetic algorithm.The energy-saving speed curve of the train leaving the station is solved by off-line optimization.The improved algorithm can ensure the diversity of population and improve the ability of global convergence of the algorithm.At last,the feasibility and validity of the algorithm are verified by using actual line data.(3)The optimization of the tracking train of the multi-train tracking operation by the multi-objective particle swarm optimization with dynamic inertia weight and dynamic sensitive factor.Firstly,characteristics of trains tracking operation is analyzed,and the optimization strategy of multi-train tracking is given.Secondly,the calculation model of trains tracking headway distance is established.Thirdly,the optimization problem of trains tracking is considered as a dynamic optimization problem and a multi-objective model of energy consumption and running time is established.Then,considering limitations of the non-dynamic algorithm solving the dynamic optimization problem,a multi-objective particle swarm optimization algorithm with dynamic inertia weight and dynamic sensitive factor is proposed,which can optimize the speed timely when the tracking train is affected.Finally,the feasibility and validity of the algorithm are verified by using actual data.(4)The design of speed controller based on particle swarm algorithm.Firstly,the single point train nonlinear model is linearized.Secondly,on the basis of linear model,a PID controller design scheme based on particle swarm optimization algorithm is proposed,which avoids the problem of parameter tuning in PID controller issues.The design process of controller is considered as an optimization problem with constraints,and the controller,which can tune parameters automatically,is applied to the ATO system,so as to the train can follow the optimized target speed of intelligent algorithm.Finally,the performance of the controller is verified by the optimized target velocity curve.