基于改进粒子滤波的综合能源系统预测辅助状态估计
作者:
中图分类号:

TM732

基金项目:

国家自然科学基金资助项目(51977212)


Forecasting-aided state estimation of integrated energy systems based on improved particle filter
Author:
  • 摘要
  • | |
  • 访问统计
  • |
  • 参考文献 [25]
  • |
  • 相似文献 [20]
  • | | |
  • 文章评论
    摘要:

    高效准确的状态估计是综合能源系统安全稳定的基础。粒子滤波具有精度高、对非线性系统适应性强的优点,已应用于电力系统的状态估计中。为提高综合能源系统的状态估计精度,文中提出一种基于改进粒子滤波的综合能源系统预测辅助状态估计方法。首先,构建包含电-热-气网络的区域综合能源系统模型;然后,将粒子滤波算法拓展到电-热-气网络,在粒子滤波相关理论的基础上,针对传统粒子滤波算法存在的跟踪误差问题对粒子滤波的预测步进行改进;最后,利用经典的综合能源系统算例对文中提出的改进粒子滤波算法进行验证。结果证明该方法能够有效解决传统粒子滤波算法的跟踪误差问题,提高系统的估计精度。

    Abstract:

    Efficient and accurate state estimation is the basis for the safety and stability of the integrated energy system (IES). Particle filter has high precision and strong adaptability to nonlinear systems,and it has been applied to state estimation of power systems. To improve the precision of state estimation in IES,a forecasting-aided state estimation method based on improved particle filter is proposed. Firstly,a regional IES model including an electricity-heat-gas network is constructed. Secondly,the particle filter algorithm is applied to the electricity-heat-gas network. The prediction step of the particle filter is improved because of the tracking error problem of traditional particle filtering algorithm,which is based on particle filter theory. Finally,the improved particle filter algorithm is verified by using the classical IES example. The results show that this method can effectively solve the tracking error problem of the traditional particle filter algorithm,which can improve the precision of state estimation in IES.

    参考文献
    [1] 胡枭,尚策,程浩忠,等. 综合能源系统能流计算方法述评与展望[J]. 电力系统自动化,2020,44(18):179-191. HU Xiao,SHANG Ce,CHENG Haozhong,et al. Review and prospect of calculation method for energy flow in integrated energy system[J]. Automation of Electric Power Systems,2020,44(18):179-191.
    [2] 崔明勇,宣名阳,卢志刚,等. 基于合作博弈的多综合能源服务商运行优化策略[J]. 中国电机工程学报,2022,42(10):3548-3564. CUI Mingyong,XUAN Mingyang,LU Zhigang,et al. Operation optimization strategy of multi integrated energy service companies based on cooperativegame theory[J]. Proceedings of the CSEE,2022,42(10):3548-3564.
    [3] 赵永凯,王靖韬,赵维,等. 计及多目标的园区级综合能源系统协同优化模型研究[J].电力信息与通信技术,2021,19(11):123-130. ZHAO Yongkai,WANG Jingtao,ZHAO Wei,et al. Multi-objective optimization of park-level integrated energy system:model and analysis[J]. Electric Power Information and Communication Technology,2021,19(11):123-130.
    [4] 郁丹,郭雨涵,吴君,等. 考虑不确定性的区域综合能源系统灵活性提升规划及评估[J]. 供用电,2022,39(4):84-92. YU Dan,GUO Yuhan,WU Jun,et al. Flexibility improvement planning and evaluation of regional integrated energy system considering uncertainty[J]. Distribution & Utilization,2022,39(4):84-92.
    [5] ZHAO J B,GÓMEZ-EXPÓSITO A,NETTO M,et al. Power system dynamic state estimation:motivations,definitions,methodologies,and future work[J]. IEEE Transactions on Power Systems,2019,34(4):3188-3198.
    [6] MELIOPOULOS A P S,COKKINIDES G J,MYRDA P,et al. Dynamic state estimation-based protection:status and promise[J]. IEEE Transactions on Power Delivery,2017,32(1):320-330.
    [7] 郑文迪,聂建雄,邵振国,等. 智能配电网状态估计研究现状和展望[J]. 电力系统及其自动化学报,2021,33(4):8-16. ZHENG Wendi,NIE Jianxiong,SHAO Zhenguo,et al. Status quo and prospect of researches on state estimation for smart distribution network[J]. Proceedings of the CSU-EPSA,2021,33(4):8-16.
    [8] 陈艳波,马进. 一种双线性抗差状态估计方法[J]. 电力系统自动化,2015,39(6):41-47. CHEN Yanbo,MA Jin. A bilinear robust state estimation method for power systems[J]. Automation of Electric Power Systems,2015,39(6):41-47.
    [9] 郑顺林,刘进,陈艳波,等. 基于加权最小绝对值的电-气综合能源系统双线性抗差状态估计[J]. 电网技术,2019,43(10):3733-3744. ZHENG Shunlin,LIU Jin,CHEN Yanbo,et al. Bilinear robust state estimation based on weighted least absolute value for integrated electricity-gas system[J]. Power System Technology,2019,43(10):3733-3744.
    [10] CHEN Q Y,YANG D C,WANG Y N,et al. Robust state estimation of electricity-gas-heat integrated energy system based on the bilinear transformations[J]. IET Generation,Transmission & Distribution,2021,15(1):149-163.
    [11] MA W T,QIU J Z,LIU X H,et al. Unscented Kalman filter with generalized correntropy loss for robust power system forecasting-aided state estimation[J]. IEEE Transactions on Industrial Informatics,2019,15(11):6091-6100.
    [12] DO COUTTO FILHO M B,STACCHINI DE SOUZA J C,FREUND R S. Forecasting-aided state estimation-part Ⅱ:implementation[J]. IEEE Transactions on Power Systems,2009,24(4):1678-1685.
    [13] 李延真,郭英雷,彭博,等. 基于多时间尺度状态估计的配电网实时态势预测[J]. 电力工程技术,2020,39(2):127-134. LI Yanzhen,GUO Yinglei,PENG Bo,et al. Real-time situation prediction of distribution network based on multi-time scale state estimation[J]. Electric Power Engineering Technology,2020,39(2):127-134.
    [14] 郝永晶,樊晓军,李军,等. 改进RAUKF算法的配电网动态状态估计研究分析[J]. 电子元器件与信息技术,2020,4(4):170-171. HAO Yongjing,FAN Xiaojun,LI Jun,et al. Research and analysis of dynamic state estimation of distribution network based on improved RAUKF algorithm[J]. Electronic Component and Information Technology,2020,4(4):170-171.
    [15] 刘鑫蕊,李垚,孙秋野,等. 基于多时间尺度的电-气-热耦合网络动态状态估计[J]. 电网技术,2021,45(2):479-490. LIU Xinrui,LI Yao,SUN Qiuye,et al. Interaction and joint state estimation of electric-gas-thermal coupling network[J]. Power System Technology,2021,45(2):479-490.
    [16] 谢美美. 面向目标跟踪的非线性滤波算法性能分析研究[D]. 西安:长安大学,2019. XIE Meimei. Study on performance analysis of nonlinear filtering algorithms for target tracking[D]. Xi'an:Chang'an University,2019.
    [17] 孟庆旭. 粒子滤波算法研究及其在非线性估计中的应用[D]. 武汉:华中科技大学,2019. MENG Qingxu. Research on particle filtering algorithm and its application in nonlinear estimation[D]. Wuhan:Huazhong University of Science and Technology,2019.
    [18] 石倩,刘敏. 基于容积粒子滤波的配电网动态状态估计. 电测与仪表:1-6. https://kns.cnki.net/kcms/detail/23.1202.TH.20210113.1907.006.html. SHI Qian,LIU Min. Dynamicstate estimation of distribution network based on CPF. Electrical Measurement & Instrumentation:1-6. https://kns.cnki.net/kcms/detail/23.1202.TH.20210113.1907.006.html.
    [19] 罗永平. 基于无迹粒子滤波的配电网状态估计研究[D]. 贵阳:贵州大学,2020. LUO Yongping. Research on distribution network state estimation based on unscented particle filter[D]. Guiyang:Guizhou University,2020.
    [20] 慈文斌,顾海飞,朱劲松. 多时间尺度电热综合能源系统状态估计研究[J]. 热力发电,2021,50(9):94-100. CI Wenbin,GU Haifei,ZHU Jinsong. Multi-timescale state estimation for integrated electricity and heat system[J]. Thermal Power Generation,2021,50(9):94-100.
    [21] 钟俊杰,李勇,曾子龙,等. 综合能源系统多能流准稳态分析与计算[J]. 电力自动化设备,2019,39(8):22-30. ZHONG Junjie,LI Yong,ZENG Zilong,et al. Quasi-steady-state analysis and calculation of multi-energy flow for integrated energy system[J]. Electric Power Automation Equipment,2019,39(8):22-30.
    [22] 王英瑞,曾博,郭经,等. 电-热-气综合能源系统多能流计算方法[J]. 电网技术,2016,40(10):2942-2951. WANG Yingrui,ZENG Bo,GUO Jing,et al. Multi-energy flow calculation method for integrated energy system containing electricity,heat and gas[J]. Power System Technology,2016,40(10):2942-2951.
    [23] 赵洪山,田甜. 基于自适应无迹卡尔曼滤波的电力系统动态状态估计[J]. 电网技术,2014,38(1):188-192. ZHAO Hongshan,TIAN Tian. Dynamic state estimation for power system based on an adaptive unscented Kalman filter[J]. Power System Technology,2014,38(1):188-192.
    [24] 王少帅. 时间序列数据采集及其应用[D]. 太原:中北大学,2018. WANG Shaoshuai. Time series data acquisition and application[D]. Taiyuan:North University of China,2018.
    [25] 张忠平. 指数平滑法[M]. 北京:中国统计出版社,1996. ZHANG Zhongping. Exponential smoothing[M]. Beijing:China Statistics Press,1996.
    引证文献
    网友评论
    网友评论
    分享到微博
    发 布
引用本文

杨德昌,王雅宁,李朝霞,龚雪娇,余建树,李玲.基于改进粒子滤波的综合能源系统预测辅助状态估计[J].电力工程技术,2022,41(6):172-181

复制
分享
文章指标
  • 点击次数:839
  • 下载次数: 1043
  • HTML阅读次数: 1900
  • 引用次数: 0
历史
  • 收稿日期:2022-06-19
  • 最后修改日期:2022-09-26
  • 录用日期:2022-02-08
  • 在线发布日期: 2022-11-24
  • 出版日期: 2022-11-28
文章二维码