scholarly journals Optimization of High-Speed Train Control Strategy for Traction Energy Saving Using an Improved Genetic Algorithm

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Ruidan Su ◽  
Qianrong Gu ◽  
Tao Wen
Keyword(s):  
Genetic Algorithm ◽  
Energy Consumption ◽  
Control Strategy ◽  
High Speed ◽  
Energy Savings ◽  
Fitness Function ◽  
High Speed Train ◽  
Improved Genetic Algorithm ◽  
Running Time ◽  
Train Control

A parallel multipopulation genetic algorithm (PMPGA) is proposed to optimize the train control strategy, which reduces the energy consumption at a specified running time. The paper considered not only energy consumption, but also running time, security, and riding comfort. Also an actual railway line (Beijing-Shanghai High-Speed Railway) parameter including the slop, tunnel, and curve was applied for simulation. Train traction property and braking property was explored detailed to ensure the accuracy of running. The PMPGA was also compared with the standard genetic algorithm (SGA); the influence of the fitness function representation on the search results was also explored. By running a series of simulations, energy savings were found, both qualitatively and quantitatively, which were affected by applying cursing and coasting running status. The paper compared the PMPGA with the multiobjective fuzzy optimization algorithm and differential evolution based algorithm and showed that PMPGA has achieved better result. The method can be widely applied to related high-speed train.

An Improved Genetic Algorithm Based Annulus-sector Clustering Routing Protocol for Wireless Sensor Networks

2021 ◽  
Author(s):  
Wang Chu-hang ◽  
Liu Xiao-li ◽  
Youjia Han ◽  
Hu Huang-shui ◽  
Wu Sha-sha
Keyword(s):  
Genetic Algorithm ◽  
Wireless Sensor Networks ◽  
Energy Consumption ◽  
Sensor Networks ◽  
Routing Protocol ◽  
Fitness Function ◽  
Minimum Energy ◽  
Wireless Sensor ◽  
Optimal Routing ◽  
Improved Genetic Algorithm

Abstract In wireless sensor networks, uniform cluster formation and optimal routing paths finding are always the two most important factors for clustering routing protocols to minimize the network energy consumption and balance the network load. In this paper, an improved genetic algorithm based annulus-sector clustering routing protocol called GACRP is proposed. In GACRP, the circular network is divided into sectors with the same size for each annulus, whose number is determined by calculating the minimum energy consumption of each annulus. Each annulus-sector forms a cluster and the best node in this annulus-sector is selected as cluster head. Moreover, an improved genetic algorithm with a novel fitness function considering energy and load balance is presented to find the optimal routing path for each CH, and an adaptive round time is calculated for maintaining the clusters. Simulation results show that GACRP can significantly improve the network energy efficiency and prolong the network lifetime as well as mitigate the hot spot problem.

Operation-oriented reliability and availability evaluation for onboard high-speed train control system with dynamic Bayesian network

2018 ◽  
Vol 233 (3) ◽  
pp. 455-469 ◽  
Author(s):  
Lei Jiang ◽  
Yiliu Liu ◽  
Xiaomin Wang ◽  
Mary Ann Lundteigen
Keyword(s):  
Control System ◽  
Bayesian Network ◽  
High Speed ◽  
Dynamic Bayesian Network ◽  
Dynamic Bayesian Networks ◽  
High Speed Train ◽  
Train Control ◽  
Train Control System ◽  
Onboard System ◽  
Reliability And Availability

The reliability and availability of the onboard high-speed train control system are important to guarantee operational efficiency and railway safety. Failures occurring in the onboard system may result in serious accidents. In the analysis of the effects of failure, it is significant to consider the operation of an onboard system. This article presents a systemic approach to evaluate the reliability and availability for the onboard system based on dynamic Bayesian network, with taking into account dynamic failure behaviors, imperfect coverage factors, and temporal effects in the operational phase. The case studies are presented and compared for onboard systems with different redundant strategies, that is, the triple modular redundancy, hot spare double dual, and cold spare double dual. Dynamic fault trees of the three kinds of onboard system are constructed and mapped into dynamic Bayesian networks. The forward and backward inferences are conducted not only to evaluate the reliability and availability but also to recognize the vulnerabilities of the onboard systems. A sensitivity analysis is carried out for evaluating the effects of failure rates subject to uncertainties. To improve the reliability and availability, the recovery mechanism should be paid more attention. Finally, the proposed approach is validated with the field data from one railway bureau in China and some industrial impacts are provided.

scholarly journals Characteristics of airflow in the platform with high-speed train passing through the underground railway station

2020 ◽  
Vol 165 ◽  
pp. 04075
Author(s):  
Qizhang Li ◽  
Yongliang Xie
Keyword(s):  
Energy Consumption ◽  
High Speed ◽  
High Speed Train ◽  
Air Velocity ◽  
Railway Station ◽  
High Speed Railway ◽  
Air Conditioning System ◽  
Environment Control ◽  
Underground Railway ◽  
Waiting Line

Underground high-speed railway station is becoming more and more popular in recent years, due to its advantage in relieving the tense situation of urban construction land. HVAC (Heating, Ventilation and Air Conditioning) system of underground railway station consumes large energy, therefore it is necessary to find a way to decrease the energy consumption in stations. Reasonable ventilation and air organization are the basis of energy-saving design of environment control system in stations. The energy consumption could be reduced greatly by utilizing the piston wind properly. In the present work, airflow characteristics in the station are investigated when high-speed train is passing through the underground railway station with CCM+ software. Results show that piston wind has different effects on airflow in the platform when the high-speed train is running. However, the air velocity in the platform is always lower than 5 m/s. In order to analyse the effect of piston wind on the airflow in the platform in more detail, the velocities and temperatures at waiting line are extracted. The air velocity near two ends of platform is larger and the similar results could also be observed for temperatures.

scholarly journals Multiobjective Synthesis of Linear Arrays by Using an Improved Genetic Algorithm

2019 ◽  
Vol 2019 ◽  
pp. 1-13 ◽  
Author(s):  
Bo Yang
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Fitness Function ◽  
Particle Swarm ◽  
Weight Vector ◽  
Side Lobe Level ◽  
Improved Genetic Algorithm ◽  
Pattern Synthesis ◽  
Swarm Optimization ◽  
Dynamic Weight

In this paper, an improved genetic algorithm with dynamic weight vector (IGA-DWV) is proposed for the pattern synthesis of a linear array. To maintain the diversity of the selected solution in each generation, the objective function space is divided by the dynamic weight vector, which is uniformly distributed on the Pareto front (PF). The individuals closer to the dynamic weight vector can be chosen to the new population. Binary- and real-coded genetic algorithms (GAs) with a mapping method are implemented for different optimization problems. To reduce the computation complexity, the repeat calculation of the fitness function in each generation is replaced by a precomputed discrete cosine transform matrix. By transforming the array pattern synthesis into a multiobjective optimization problem, the conflict among the side lobe level (SLL), directivity, and nulls can be efficiently addressed. The proposed method is compared with real number particle swarm optimization (RNPSO) and quantized particle swarm optimization (QPSO) as applied in the pattern synthesis of a linear thinned array and a digital phased array. The numerical examples show that IGA-DWV can achieve a high performance with a lower SLL and more accurate nulls.

Influence of Volumetric Capacity on Energy Consumption in Oil-Lubricated Compressed Air Systems

2016 ◽  
Vol 8 (4) ◽  
Author(s):  
Pawel Olszewski ◽  
Claus Borgnakke
Keyword(s):  
Mathematical Model ◽  
Energy Efficiency ◽  
Energy Consumption ◽  
Potential Energy ◽  
Uncertainty Analysis ◽  
Control Strategy ◽  
Energy Savings ◽  
Compressed Air ◽  
Numerical Code ◽  
Volumetric Capacity

The aim of this research is to estimate the influence of compressed air volumetric capacity on the energy consumption in systems equipped with oil-lubricated screw compressors. A mathematical model of oil-lubricated screw compressors has been proposed. The model is verified by comparing with real measurements, and overall uncertainty analysis is estimated. An in-house developed numerical code (c++) is used to calculate the energy consumption in 252,000 combinations. The final result can be used to estimate the energy efficiency of existing air systems and to assess potential energy savings due to changes in the operation of the system and its control strategy.

scholarly journals Optimal High-Speed Railway Timetable by Stop Schedule Adjustment for Energy-Saving

2019 ◽  
Vol 2019 ◽  
pp. 1-9
Author(s):  
Dingjun Chen ◽  
Sihan Li ◽  
Junjie Li ◽  
Shaoquan Ni ◽  
Xiaolong Liu
Keyword(s):  
Energy Consumption ◽  
Energy Saving ◽  
Travel Time ◽  
High Speed ◽  
Optimization Techniques ◽  
High Speed Rail ◽  
High Speed Railway ◽  
Running Time ◽  
Total Travel Time ◽  
Timetable Optimization

Timetable optimization techniques offer opportunity for saving energy and hence reducing operational costs for high-speed rail services. The existing energy-saving timetable optimization is mainly concentrated on the train running state adjustment and the running time redistribution between two stations. Not only the adjustment space of timetables is limited, but also it is hard for the train to reach the optimized running state in reality, and it is difficult to get feasible timetable with running time redistribution between two stations for energy-saving. This paper presents a high-speed railway energy-saving timetable based on stop schedule optimization. Under the constraints of safety interval and stop rate, with the objective of minimizing the increasing energy consumption of train stops and the shortest travel time of trains, the high-speed railway energy-saving timetable optimization model is established. The fuzzy mathematics programming method is used to design an efficient algorithm. The proposed model and algorithm are demonstrated in the actual operation data of Beijing-Shanghai high-speed railway. The results show that the total operating energy consumption of the train is reduced by 3.7%, and the total travel time of the train is reduced by 11 minutes.

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