基于LCC的外部电源薄弱地区同相贯通牵引供电方案优化
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摘要
基于同相贯通牵引供电系统原理,以系统的全寿命周期成本(Life Cycle Cost,LCC)最小为优化目标,分别以牵引变电所设置数目和位置为优化变量,将牵引供电方案主要设计原则设置为约束条件,建立基于LCC的同相贯通牵引供电系统规划模型;采用粒子群算法对模型进行求解时,为提高算法的全局收敛性,提出非线性进化策略用以改进学习因子并采用高斯函数递减策略动态调整惯性权重系数,采用种群适应度方差值体现种群中个体的汇聚程度。以青藏铁路格拉段为算例,采用自主开发的牵引供电负荷过程仿真平台和改进粒子群算法,对基于LCC的同相贯通牵引供电方案进行优化配置和经济性评估。结果表明:与既有AT牵引供电方案相比,基于LCC的同相贯通牵引供电方案在满足技术要求的同时体现了良好的经济性。
Based on the principle of cophase continuous traction power supply system, taking the minimum life cycle cost(LCC) of the system as the optimization goal, the number and position of traction substations as optimization variables respectively, and the main design principles of traction power supply system as constraints, the LCC-based planning model of cophase continuous traction power supply system was established. In order to improve the global convergence of the algorithm, a nonlinear evolutionary strategy was proposed to improve the learning factor and the Gaussian function decreasing strategy was used to adjust the inertial weight dynamically, and the population adaptability variance value was used to reflect the aggregation degree of the individuals in the population when the particle swarm algorithm was adopted to solve the model. Taking the Golmud-Lhasa section of Qinghai-Tibet Railway as an example, the self-developed traction power supply load process simulation platform and the improved particle swarm optimization algorithm were used to optimize the configuration and evaluate the economy of cophase continuous traction power supply scheme based on LCC. Results show that compared with the existing AT traction power supply scheme, the cophase continuous traction power supply scheme based on LCC not only satisfies the technical requirements, but also embodies the good economy.
Based on the principle of cophase continuous traction power supply system, taking the minimum life cycle cost(LCC) of the system as the optimization goal, the number and position of traction substations as optimization variables respectively, and the main design principles of traction power supply system as constraints, the LCC-based planning model of cophase continuous traction power supply system was established. In order to improve the global convergence of the algorithm, a nonlinear evolutionary strategy was proposed to improve the learning factor and the Gaussian function decreasing strategy was used to adjust the inertial weight dynamically, and the population adaptability variance value was used to reflect the aggregation degree of the individuals in the population when the particle swarm algorithm was adopted to solve the model. Taking the Golmud-Lhasa section of Qinghai-Tibet Railway as an example, the self-developed traction power supply load process simulation platform and the improved particle swarm optimization algorithm were used to optimize the configuration and evaluate the economy of cophase continuous traction power supply scheme based on LCC. Results show that compared with the existing AT traction power supply scheme, the cophase continuous traction power supply scheme based on LCC not only satisfies the technical requirements, but also embodies the good economy.
引文
[1] 李群湛,贺建闽.牵引供电系统分析[M].成都:西南交通大学出版社,2012.
[2] 赵元哲,李群湛,周福林,等.牵引网高次谐波对高低压三相系统的渗透特性[J].西南交通大学学报,2015,50(5):961-969.(ZHAO Yuanzhe,LI Qunzhan,ZHOU Fulin,et al.Permeability Characteristics of High-Order Harmonics of Traction Network for Three-Phase Power System[J].Journal of Southwest Jiaotong University,2015,50(5):961-969.in Chinese)
[3] 陈民武,宫衍圣,李群湛,等.电气化铁路电能质量评估及新型控制方案研究[J].电力系统保护与控制,2012,40(16):141-147.(CHEN Minwu,GONG Yansheng,LI Qunzhan,et al.Assessment of Power Quality of Electrified Railway and the Research on the New Control Scheme[J].Power System Protection and Control,2012,40(16):141-147.in Chinese)
[4] 杨东军,钱学明.采用分类学习的电气化机车受电弓拉弧检测方法[J].西安交通大学学报,2017,51(2):79-83.(YANG Dongjun,QIAN Xueming.An Arc Detection Approach for Pantograph of Electric Locomotive Using Classification Learning[J].Journal of Xi'an Jiaotong University,2017,51(2):79-83.in Chinese)
[5] 李群湛.论新一代牵引供电系统及其关键技术[J].西南交通大学学报,2014,49(4):559-568.(LI Qunzhan.On New Generation Traction Power Supply System and Its Key Technologies for Electrification Railway[J].Journal of Southwest Jiaotong University,2014,49(4):559-568.in Chinese)
[6] 陈民武,尚国旭,智慧,等.高速铁路牵引变压器容量与配置方案优化研究[J].中国铁道科学,2013,34(5):70-75.(CHEN Minwu,SHANG Guoxu,ZHI Hui,et al.Optimization on the Capacity and Configuration Scheme for Traction Transformer of High Speed Railway[J].China Railway Science,2013 ,34(5):70-75.in Chinese)
[7] 苏海锋,张建华,梁志瑞,等.基于LCC和改进粒子群算法的配电网多阶段网架规划优化[J].中国电机工程学报,2013,33(4):118-125.(SU Haifeng,ZHANG Jianhua,LIANG Zhirui,et al.Multi-Stage Planning Optimization for Power Distribution Network Based on LCC and Improved PSO[J].Proceedings of the CSEE,2013,33(4):118-125.in Chinese)
[8] HINOW M,MEVISSEN M.Substation Maintenance Strategy Adaptation for Life-Cycle Cost Reduction Using Genetic Algorithm[J].IEEE Transactions on Power Delivery,2011,26(1):197-204.
[9] 杨颢,陈民武,盛望群,等.新型贯通同相供电系统建模与运行特性分析[J].铁道科学与工程学报,2018,15(8):2131-2139.(YANG Hao,CHEN Minwu,SHENG Wangqun,et al.Modelling and Operation Characteristics Analysis of a New Continuous Co-Phase Traction Power Supply System[J].Journal of Railway Science and Engineering,2018,15(8):2131-2139.in Chinese)
[10] 杨颢.基于全寿命周期成本的贯通同相牵引供电系统优化设计[D].成都:西南交通大学,2018.(YANG Hao.Optimization Design of Cophase Continuous Traction Power Supply System Based on Life Cycle Cost[D].Chengdu:Southwest Jiaotong University,2018.in Chinese)
[11] 张迅,王平,邢建春,等.基于高斯函数递减惯性权重的粒子群优化算法[J].计算机应用研究,2012,29(10):3710-3712.(ZHANG Xun,WANG Ping,XING Jianchun,et al.Particle Swarm Optimization Algorithms with Decreasing Inertia Weight Based on Gaussian Function[J].Application Research of Computers,2012,29(10):3710-3712.in Chinese)
[12] 刘俊芳,高岳林.带自适应变异的量子粒子群优化算法[J].计算机工程与应用,2011,47(1):41-43.(LIU Junfang,GAO Yuelin.Quantum Particle Swarm Optimization Algorithm with Adaptive Mutation[J].Computer Engineering and Applications,2011,47(3):41-43.in Chinese)
[13] 中铁第一勘察设计院集团有限公司.改建铁路青藏线格尔木至拉萨段电气化工程预可行性研究[R].西安:中铁第一勘察设计院集团有限公司,2018.
[14] 国家铁路局.TB 10009—2016 铁路电力牵引供电设计规范[S].北京:中国铁道出版社,2016.
[15] 陈民武,许臣友,黄文勋,等.基于改进粒子群算法的牵引供电系统多目标优化设计[J].中国铁道科学,2016,37(1):85-92.(CHEN Minwu,XU Chenyou,HUANG Wenxun,et al.Multi-Objective Optimization Design of Traction Power Supply System Based on Improved Particle Swarm Algorithm[J].China Railway Science,2016,37(1):85-92.in Chinese)
[2] 赵元哲,李群湛,周福林,等.牵引网高次谐波对高低压三相系统的渗透特性[J].西南交通大学学报,2015,50(5):961-969.(ZHAO Yuanzhe,LI Qunzhan,ZHOU Fulin,et al.Permeability Characteristics of High-Order Harmonics of Traction Network for Three-Phase Power System[J].Journal of Southwest Jiaotong University,2015,50(5):961-969.in Chinese)
[3] 陈民武,宫衍圣,李群湛,等.电气化铁路电能质量评估及新型控制方案研究[J].电力系统保护与控制,2012,40(16):141-147.(CHEN Minwu,GONG Yansheng,LI Qunzhan,et al.Assessment of Power Quality of Electrified Railway and the Research on the New Control Scheme[J].Power System Protection and Control,2012,40(16):141-147.in Chinese)
[4] 杨东军,钱学明.采用分类学习的电气化机车受电弓拉弧检测方法[J].西安交通大学学报,2017,51(2):79-83.(YANG Dongjun,QIAN Xueming.An Arc Detection Approach for Pantograph of Electric Locomotive Using Classification Learning[J].Journal of Xi'an Jiaotong University,2017,51(2):79-83.in Chinese)
[5] 李群湛.论新一代牵引供电系统及其关键技术[J].西南交通大学学报,2014,49(4):559-568.(LI Qunzhan.On New Generation Traction Power Supply System and Its Key Technologies for Electrification Railway[J].Journal of Southwest Jiaotong University,2014,49(4):559-568.in Chinese)
[6] 陈民武,尚国旭,智慧,等.高速铁路牵引变压器容量与配置方案优化研究[J].中国铁道科学,2013,34(5):70-75.(CHEN Minwu,SHANG Guoxu,ZHI Hui,et al.Optimization on the Capacity and Configuration Scheme for Traction Transformer of High Speed Railway[J].China Railway Science,2013 ,34(5):70-75.in Chinese)
[7] 苏海锋,张建华,梁志瑞,等.基于LCC和改进粒子群算法的配电网多阶段网架规划优化[J].中国电机工程学报,2013,33(4):118-125.(SU Haifeng,ZHANG Jianhua,LIANG Zhirui,et al.Multi-Stage Planning Optimization for Power Distribution Network Based on LCC and Improved PSO[J].Proceedings of the CSEE,2013,33(4):118-125.in Chinese)
[8] HINOW M,MEVISSEN M.Substation Maintenance Strategy Adaptation for Life-Cycle Cost Reduction Using Genetic Algorithm[J].IEEE Transactions on Power Delivery,2011,26(1):197-204.
[9] 杨颢,陈民武,盛望群,等.新型贯通同相供电系统建模与运行特性分析[J].铁道科学与工程学报,2018,15(8):2131-2139.(YANG Hao,CHEN Minwu,SHENG Wangqun,et al.Modelling and Operation Characteristics Analysis of a New Continuous Co-Phase Traction Power Supply System[J].Journal of Railway Science and Engineering,2018,15(8):2131-2139.in Chinese)
[10] 杨颢.基于全寿命周期成本的贯通同相牵引供电系统优化设计[D].成都:西南交通大学,2018.(YANG Hao.Optimization Design of Cophase Continuous Traction Power Supply System Based on Life Cycle Cost[D].Chengdu:Southwest Jiaotong University,2018.in Chinese)
[11] 张迅,王平,邢建春,等.基于高斯函数递减惯性权重的粒子群优化算法[J].计算机应用研究,2012,29(10):3710-3712.(ZHANG Xun,WANG Ping,XING Jianchun,et al.Particle Swarm Optimization Algorithms with Decreasing Inertia Weight Based on Gaussian Function[J].Application Research of Computers,2012,29(10):3710-3712.in Chinese)
[12] 刘俊芳,高岳林.带自适应变异的量子粒子群优化算法[J].计算机工程与应用,2011,47(1):41-43.(LIU Junfang,GAO Yuelin.Quantum Particle Swarm Optimization Algorithm with Adaptive Mutation[J].Computer Engineering and Applications,2011,47(3):41-43.in Chinese)
[13] 中铁第一勘察设计院集团有限公司.改建铁路青藏线格尔木至拉萨段电气化工程预可行性研究[R].西安:中铁第一勘察设计院集团有限公司,2018.
[14] 国家铁路局.TB 10009—2016 铁路电力牵引供电设计规范[S].北京:中国铁道出版社,2016.
[15] 陈民武,许臣友,黄文勋,等.基于改进粒子群算法的牵引供电系统多目标优化设计[J].中国铁道科学,2016,37(1):85-92.(CHEN Minwu,XU Chenyou,HUANG Wenxun,et al.Multi-Objective Optimization Design of Traction Power Supply System Based on Improved Particle Swarm Algorithm[J].China Railway Science,2016,37(1):85-92.in Chinese)