Train energy-saving operation method based on multi-target particle swarm algorithm

Abstract

The invention discloses a train energy-saving operation method based on a multi-target particle swarm algorithm. The method comprises the following steps: setting train operation environment parameters including line data, train data and operation data; establishing a train kinematics model and a traction energy consumption calculation method; establishing a train energy consumption multi-objective optimization model by taking traction energy consumption and operation time as optimization objectives; solving the optimization model by adopting a multi-objective particle swarm algorithm to obtain multiple groups of non-inferior solutions of traction energy consumption and running time of the train in a single interval; calculating the traction energy consumption and running time non-inferiorsolution of the train in each section of the whole line, and selecting the optimal solution of each section by adopting a dynamic programming method, so as to enable the energy consumption of the whole line of the train to be minimum under the condition of meeting the requirement of specified running time. According to the method, the searching efficiency of the train energy-saving optimization problem is improved, the train energy consumption is effectively reduced, and the train operation punctuality is improved.

Description

technical field [0001] The invention belongs to the technical field of train operation control, in particular to a train energy-saving operation method based on a multi-objective particle swarm algorithm. Background technique [0002] With the acceleration of urbanization in my country, the scale of cities is gradually expanding, and the urban population is increasing rapidly. Rail transit has developed into an important part of the public transportation system in first- and second-tier cities due to its advantages of large transportation volume and fast speed. With the rapid development of urban rail transit, the construction cost and operating cost of urban rail transit are also increasing year by year. According to the statistical results of electricity consumption and energy consumption of urban rail transit, the energy consumption of urban rail transit system mainly includes: train traction power supply, air conditioning system, lighting, escalator and other systems, a...

AI Technical Summary

Problems solved by technology

[0003] Train energy-saving operation control is a kind of multi-objective optimization problem, it is difficult to establish an accurate mathematical model to describe the train operation process

Traditional mathematical methods, such as numerical analysis methods, are difficult to obtain the exact solution of the model. Even if the iterative method is used, only the approximate solution of the model can be obtained, and it is easy to fall into a local optimum

The current method to deal with multi-objective and multi-constrained optimization problems of train energy saving is mainly to convert multi-objectives into single-objective optimization by weighting multiple optimization objectives. Local optimization, only one set of solutions can be obtained

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