电工技术学报  2024, Vol. 39 Issue (14): 4557-4566    DOI: 10.19595/j.cnki.1000-6753.tces.231668
高速列车电气化控制 |
高速列车大功率操纵过程分数阶建模及速度时滞预测
张坤鹏1,2,3, 严斐1,2,3, 杨辉1,2,3, 刘鸿恩4, 安春兰1,2,3
1.华东交通大学电气与自动化工程学院 南昌 330013;
2.江西省先进控制与优化重点实验室 南昌 330013;
3.轨道交通基础设施性能监测与保障国家重点实验室 南昌 330013;
4.江西理工大学永磁磁浮技术与轨道交通研究院 赣州 341000
Fractional Order Modeling and Speed Delay Prediction of High-Speed Train High Power Control Process
Zhang Kunpeng1,2,3, Yan Fei1,2,3, Yang Hui1,2,3, Liu Hongen4, An Chunlan1,2,3
1. School of Electrical and Automation Engineering East China Jiaotong University Nanchang 330013 China;
2. Jiangxi Provincial Key Laboratory of Advanced Control and Optimization Nanchang 330013 China;
3. State Key Laboratory of Performance Monitoring and Protecting of Rail Transit Infrastructure Nanchang 330013 China;
4. Institute of Permanent Magnetic Levitation Technology and Rail Transportation Jiangxi University of Science and Technology Ganzhou 341000 China
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摘要 针对现有司机操纵策略难以完整描述列车大功率操纵过程的微观变化,设计机理特性和数据驱动模型联合驱动的分数阶建模策略,来满足高速列车无级调速下安全平稳运行的要求。针对高速列车大功率操纵模式下的大惯性特性和司机反应时间,该文首先对大功率牵引系统和制动系统的时滞特性进行分析,进而对列车操纵策略设计精简冗余工序编码策略,从闭环控制角度实现速度时滞特性精准预测;其次提出了一种新的分数阶建模方法,并设计分数阶最小二乘算法对模型中的时变参数进行精细化辨识;最后基于现场实验数据验证了所提方法的有效性。
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张坤鹏
严斐
杨辉
刘鸿恩
安春兰
关键词 ： 高速列车,  大功率操纵过程,  数据驱动,  时滞,  分数阶最小二乘模型    
Abstract：The high-power control process of high-speed trains is a complex dynamic system. As the speed of high-speed trains increases, the interactive dynamics among the vehicle, wheel, rail, and grid become frequent. Traditional modeling methods ignore these strong nonlinear differences and only give a single-speed forecasting result at a large spatial scale. It is insufficient to fully describe the micro-changes in the train high-power control process. Recently, some methods have been presented to forecast the time-delay characteristics of high-speed trains. However, most need to consider the common structural characteristics shared by the mechanism and the data-driven models. This paper proposes a fractional modeling strategy to meet the requirements of safe and stable operation of high-speed trains under step-less speed regulation. By learning the optimal control strategy from historical speed and power data, the local train speed for multiple operation scenarios can be forecasted accurately.
Firstly, the time-delay characteristics of high-power traction and braking systems are analyzed based on the features of large inertia and driver reaction time. Secondly, a simplified and redundant process coding strategy for train control strategy is constructed to accurately predict speed time-delay characteristics from the perspective of closed-loop control. Then, a new fractional-order modeling method is proposed, and the fractional-order least squares algorithm is designed to identify the time-varying parameters in the model.
Simulation results on the actual high-speed train operation data show that the V-S (velocity-distance) curve based on the manual control strategy has unstable fluctuations. The control accuracy is 13.1%, which is difficult to meet the multi-objective control range of 8% of the high-speed train automatic driving control strategy, increasing energy consumption and reducing ride comfort. On the other hand, the proposed fractional-order control strategy can effectively reduce the wide range of speed fluctuations. Compared with the benchmark index of high-speed train continuous operation with high power and constant speed, the proposed precision prediction method under the closed-loop structure can meet the safety margin within 3.5% and the control range within 8%. According to the ten-octave equivalent compression property of logarithmic frequency characteristics in automatic control theory, the integer order description and fractional order have equivalent properties for the same high-speed train control system. When the fractional order number is greater than 0.35, the high-speed train speed prediction cannot reach the integer order, and the overfitting effect occurs. Theoretically, realizing the micro speed prediction with a small order is difficult under the current computation power. As a result, the order of fractional least squares is generally set to 0.2.
The following conclusions can be drawn from the simulation analysis. (1) The time-delay characteristics of the high-power control process of high-speed trains are given, and the fine modeling of the operating parameters of the high-speed train control process is realized. (2) The simulation results based on real data show that the proposed method can meet the operational requirements of high-power control of high-speed trains.
Key words： High-speed train    high power control process    data-driven    time delay    fractional-order least squares algorithm   
收稿日期: 2023-10-10     
PACS: TM922  
基金资助:国家自然科学基金项目（U2034211, 62063007）、江西省自然科学基金项目（20224BAB212021, 20232BAB202029）、江西省教育厅项目（GJJ200610, GJJ210647）和流程工业综合自动化国家重点实验室联合开放基金项目（2022-KF-21-03）资助
通讯作者: 张坤鹏, 男,1986年生,博士,硕士生导师,研究方向为电气化轨道交通控制。E-mail: ecjtu.zhangkunpeng@163.com   
作者简介: 严斐, 男,1998年生,硕士研究生,研究方向为高速列车运行过程控制。E-mail: yf2930135179@163.com
引用本文:   
张坤鹏, 严斐, 杨辉, 刘鸿恩, 安春兰. 高速列车大功率操纵过程分数阶建模及速度时滞预测[J]. 电工技术学报, 2024, 39(14): 4557-4566. Zhang Kunpeng, Yan Fei, Yang Hui, Liu Hongen, An Chunlan. Fractional Order Modeling and Speed Delay Prediction of High-Speed Train High Power Control Process. Transactions of China Electrotechnical Society, 2024, 39(14): 4557-4566.
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