基于实测数据和行车运行图的高铁牵引变电站负荷预测方法

doi:10.19595/j.cnki.1000-6753.tces.181892

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Abstract In order to precisely forecast the dynamic load of the high-speed railway substation, this paper analyzed the typical operation process and the load characteristics of EMU in the power supply interval with measured data. Moreover, it studied the detection and recognition method for the typical operation process using the sliding windows. Consequently, it extracted the dynamic load of the typical operation processes from the measured data and created the load library. To deal with the dynamic characteristics of the traction load, it established the dynamic load models of typical operation processes by using the regression model and probability and statistics method. Furthermore, it proposed a load forecasting method for high-speed railway traction substation with the information of the train timetable, including train trips and operating time. Case studies were carried out to verify the validation of the proposed method. The prediction method can accurately predict the energy consumption of the high-speed railway substation under different scheduled train timetables and can provide an electrical reference for the adjustment of the train timetable.

Key words： High-speed railway traction loads measured data scheduled train time-table load estimation regression model

Received: 12 December 2018 Published: 17 January 2020

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TM922.3

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	Articles by authors
	Wei Bo
	Hu Haitao
	Wang Ke
	Fu Qi
	He Zhengyou

Cite this article:

Wei Bo,Hu Haitao,Wang Ke等. Research on Traction Load Forecasting Method for High-Speed Railway Traction Substation Based on Measured Data and Train Timetable[J]. Transactions of China Electrotechnical Society, 2020, 35(1): 179-188.

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https://dgjsxb.ces-transaction.com/EN/10.19595/j.cnki.1000-6753.tces.181892 OR https://dgjsxb.ces-transaction.com/EN/Y2020/V35/I1/179

[1] 罗培, 陈跃辉, 罗隆福, 等. 铁路电能质量控制系统容量优化设计[J]. 电工技术学报, 2016, 31(8): 181-188.
Luo Pei, Chen Yuehui, Luo Longfu, et al.Capacity optimization design of railway power quality control system[J]. Transactions of China Electrotechnical Society, 2016, 31(8): 181-188.
[2] 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.
[3] Wei Wenjing, Hu Haitao, Wang Ke, et al.The energy distribution analysis of traction power supply system based on matlab/simulink[C]//Proceedings of the First International Conference on Rail Transportation, Chengdu, 2017: 642-653.
[4] 张民, 何正友, 高仕斌. 不同负荷模型下高速铁路牵引供电系统谐波谐振敏感度分析[J]. 电力系统保护与控制, 2015, 43(20): 21-28.
Zhang Ming, He Zhengyou, Gao Shibin.Harmonic resonance modal sensitivity analysis for high-speed railway traction supply system considering different harmonic load models[J]. Power System Protection and Control, 2015, 43(20): 21-28.
[5] Hu Haitao, He Zhengyou, Li Xin, et al.Power quality impact assessment for high-speed railway associated with high-speed trains using train timetable—part I: methodology and modeling[J]. IEEE Transactions on Power Delivery, 2016, 31(2): 693-703.
[6] Wang Ke, Hu Haitao, He Zhengyou, et al.Study on power factor behavior in high-speed railways considering train timetable[J]. IEEE Transactions on Transportation Electrification, 2017, 4(1): 220-231.
[7] 王科, 陈丽华, 胡海涛, 等. 一种考虑行车运行图的高速铁路牵引供电系统谐波评估方法[J]. 铁道学报, 2017, 39(4): 32-41.
Wang Ke, Chen Lihua, Hu Haitao, et al.A novel method to assess harmonic of high-speed railway traction power supply system considering train timetable[J]. Journal of the China Railway Society, 2017, 39(4): 32-41.
[8] 王科, 胡海涛, 魏文婧, 等. 基于列车运行图的高速铁路动态牵引负荷建模方法[J]. 中国铁道科学, 2017, 38(1): 102-110.
Wang Ke, Hu Haitao, Wei Wenjing, et al.Modelling method for dynamic traction load of high speed railway based on train working diagram[J]. China Railway Science, 2017, 38(1): 102-110.
[9] 张俊骐, 吴命利. 基于多负荷端口戴维南等效的电气化铁路牵引网潮流算法[J]. 电工技术学报, 2018, 33(11): 2479-2485.
Zhang Junqi, Wu Mingli.Power flow algorithm for electric railway traction network based on multiple load port thévenin equivalence[J]. Transactions of China Electrotechnical Society, 2018, 33(11): 2479-2485.
[10] Xie Shaofeng.Probability model of traction load harmonics based on least square approximation[C]// IEEE International Electrical Machine and Drives Conference, Antalya, Turkey, 2007: 503-507.
[11] 杨少兵, 吴命利. 电气化铁道牵引变电所负荷概率模型[J]. 电力系统自动化, 2010, 34(24): 40-45.
Yang Shaobing, Wu Mingli.A load probability model for electrified railway traction substations[J]. Automation of Electric Power Systems, 2010, 34(24): 40-45.
[12] Xu W, Dong Ming.Tracking energy consumptions of home appliances using electrical signature data[C]//Power and Energy Society General Meeting, San Diego, 2012: 1-5.
[13] Dong Ming, Meira P C M, Xu W, et al. Non-intrusive signature extraction for major residential loads[J]. IEEE Transactions on Smart Grid, 2013, 4(3): 1421-1430.
[14] Dong Ming, Meira P C M, Xu W, et al. An event window based load monitoring technique for smart meters[J]. IEEE Transactions on Smart Grid, 2012, 3(2): 787-796.
[15] 徐青山, 娄藕蝶, 郑爱霞, 等. 基于近邻传播聚类和遗传优化的非侵入式负荷分解方法[J]. 电工技术学报, 2018, 33(16): 3868-3878.
Xu Qingshan, Lou Oudie, Zheng Aixia, et al.Non-intrusive load decomposition method based on neighborhood propagation clustering and genetic optimization[J]. Transactions of China Electrotechnical Society, 2018, 33(16): 3868-3878.
[16] 周明, 宋旭帆, 涂京, 等. 基于非侵入式负荷监测的居民用电行为分析[J]. 电网技术, 2018, 42(10): 3268-3274.
Zhou Ming, Song Xufan, Tu Jing, et al.Residential electricity consumption behavior snalysis based on non-intrusive load monitoring[J]. Power System Technology, 2018, 42(10): 3268-3274.
[17] 王宁, 谢敏, 邓佳梁, 等. 基于支持向量机回归组合模型的中长期降温负荷预测[J]. 电力系统保护与控制, 2016, 44(3): 92-97.
Wang Ning, Xie Min, Deng Jialiang, et al.Mid-long term temperature-lowering load forecasting based on combination of support vector machine and multiple regression[J]. Power System Protection and Control, 2016, 44(3): 92-97.
[18] 翟鹤峰, 杨明, 王栋, 等. 计及注入转移分布因子估计误差的鲁棒实时调度[J]. 电工技术学报, 2017,32(14): 217-228.
Zhai Hefeng, Yang Ming, Wang Dong, et al.Robust real-time dispatch considering the injection shift factor estimation errors[J]. Transactions of China Electrotechnical Society, 2017, 32(14): 217-228.
[19] 唐祥玲, 王平, 李思岑, 等. 基于方差-协方差组合预测的中长期电力负荷预测研究[J]. 电气技术, 2015, 16(1): 15-18.
Tang Xiangling, Wang Ping, Li Sicen, et al.Research on medium and long-term electric load forecasting based on variance-covariance combined model[J]. Electrical Engineering, 2015, 16(1): 15-18.
[20] 孙玲玲, 赵美超, 王宁, 等. 基于电压偏差机会约束的分布式光伏发电准入容量研究[J]. 电工技术学报, 2018, 33(7): 1560-1569.
Sun Lingling, Zhao Meichao, Wang Ning, et al.Research of permitted capacity of distributed photovoltaic generation based on voltage deviation chance constrained[J]. Transactions of China Electrotechnical Society, 2018, 33(7): 1560-1569.
[21] 麻秀范, 李颖, 王皓, 等. 基于电动汽车出行随机模拟的充电桩需求研究[J]. 电工技术学报, 2017, 32(增刊2): 190-202.
Ma Xiufan, Li ying, Wang Hao, et al. Research on demand of charging piles based on stochastic simulation of EV trip chain[J]. Transactions of China Electrotechnical Society, 2017, 32(S2): 190-202.
[22] 姜涛, 贾宏杰, 姜懿郎, 等. 跨区互联电网热稳定安全域边界近似方法[J]. 电工技术学报, 2016, 31(8): 134-146.
Jiang Tao, Jia Hongjie, Jiang Yilang, et al.Approximating method of wide area thermal security region boundary in bulk power system[J]. Transactions of China Electrotechnical Society, 2016, 31(8): 134-146.
[23] 徐文远, 雍静. 电力扰动数据分析学——电能质量监测数据的新应用[J]. 中国电机工程学报, 2013, 33(19): 93-101.
Xu Wenyuan, Yong Jing.Power disturbance data analytics-new application of power quality monitoring data[J]. Proceedings of the CSEE, 2013, 33(19): 93-101.