Research On ATO Energy Saving Operation Optimization Of Urban Rail Train

In recent years,the rapid development of China’s urban rail transit has greatly improved the environmental pollution and traffic jam caused by the acceleration of urbanization.At the same time,with the continuous expansion of the network dimension of urban rail transport,the problem of dramatical rise in energy consumption has become more and more prominent.How to reduce the energy consumption of the operation system has become one of the core issues to maintain the green and sustainable development of urban rail transit.In the energy consumption form of urban rail transit operation system,the main energy consumption form is the energy consumed by the traction force of train operation.Therefore,it is of great practical significance to study the reduction of the total energy consumption and operation cost of the system.In urban rail transit,the speed of train is adjusted by Automatic Train Operation(ATO)system to complete the automatic operation between stations.The ATO system takes the target speed curve generated by the optimization layer as the reference curve,and through the command output of the traction and braking units,the train can achieve the close tracking of the target curve,which determines the specific energy consumption of the actual operation of the train.However,the traditional energy saving research of trains adopts different algorithms to optimize the train target speed curve under various constraints.The tracking control adjustment module in ATO actual operation is not considered comprehensively,which makes the actual output of train speed controller deviate from the target speed curve,which has a certain impact on the actual operation energy consumption of the train.Based on this,the simulated annealing probabilistic jump improved particle swarm optimization algorithm and seeker optimization PID control method in the thesis are proposed to optimize the train target speed curve and ATO tracking control strategy,so as to realize energy-saving operation between urban rail stations.The specific research contents are as follows:(1)Firstly,the working principle,function and double-layer control structure of train ATO system are analyzed in depth,and the close relationship between core ATO subsystem and other subsystems in train operation control system is clarified.Through the analysis of the train traction characteristics,braking characteristics,resistance characteristics and working conditions conversion principles,the dynamic model of urban rail trains is established.On this basis,the composition of the energy consumption and the main influencing factors o f the train operation are analyzed.Several calculation methods of energy consumption for the urban rail train operation are proposed,which provide the necessary theoretical basis for the optimization of the energy-saving operation of the urban rail train.(2)Secondly,based on the analysis of train operation process and operation mode,the energy-saving optimization control strategy of train based on operation mode is proposed.The energy-saving optimization problem is transformed into the problem of solving the speed sequence of mode conversion in the process of train operation,and the optimization model of train energy-saving operation control is established by considering multiple constraint indexes.(3)Thirdly,the standard Particle Swarm Optimization(PSO)is improved,and a Particle Swarm Optimization with Constrict Factor of Simulate Anneal(PSOCFSA)is proposed to solve the train energy-saving control model,optimize the target speed curve of train energy-saving operation,and obtain the minimum target operation energy consumption of ATO optimization layer.Compared with the standard PSO algorithm,the improved algorithm introduces compression factor to adjust the control parameters,combined with simulated annealing mechanism,which effectively improves the global optimization performance and convergence performance of the algorithm,and verifies the effectiveness of the algorithm.(4)Finally,aiming at the problem that the traditional PID control is used in the automatic operation of urban rail transit train,and the train speed control is inflexible due to the cumbersome parameter adjustment,the Seeker Optimization Algorithm(SOA)is proposed to use the three PID parameters as the combined optimization quantity to carry out PID parameter self-tuning.And the improved SOA-PID controller is applied to the ATO tracking control layer to make the train accurately track the energy-saving target speed curve and complete the smooth operation between stations.It is verified that the improved controller can ensure good tracking control effect,reduce the difference between the actual operation energy consumption and the target curve energy consumption,and effectively reduce the train operation energy consumption.