上海交通大学学报

上海交通大学学报 >

2021 , Vol. 55 >Issue S2: 37 - 41

DOI: https://doi.org/10.16183/j.cnki.jsjtu.2021.S2.006

基于数字孪生和深度学习的新一代调控系统预调度方法

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  • 1.国家电网有限公司华东分部,上海 200120
    2.国家电力调度控制中心,北京 100031
    3.南瑞集团有限公司(国网电力科学研究院有限公司),南京 211106
王兴志(1979-),男,江苏省连云港市人,高级工程师,从事电力系统自动化等研究.电话(Tel.):13671878688;E-mail: wang_xz@ec.sgcc.com.cn.

收稿日期: 2021-10-26

  网络出版日期: 2022-01-24

基金资助

国家重点研发计划资助项目(2017YFB0902600)

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Pre-Dispatching Method of New Generation Dispatching and Control System Based on Digital Twin and Deep Learning

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  • 1. East Branch of State Grid Corporation of China, Shanghai 200120, China
    2. National Power Dispatching Control Center, Beijing 100031, China
    3. Nari Group Corporation (State Grid Electric Power Research Institute), Nanjing 211106, China

Received date: 2021-10-26

  Online published: 2022-01-24

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摘要

针对新型电力系统下传统调度自动化系统可扩展性和决策前瞻性不足等问题,提出新一代调控系统预调度方法.在描述子系统层建立能够反映电网一次设备、二次设备和环境等状态的电网数字孪生体;在预测子系统层,电网数字孪生体基于电网运行数据进行深度学习,并预测电网运行的未来态势和事故风险;以华东电网新一代调控系统的预调度试点应用为例,验证所提方法的可行性.应用结果表明:该预调度方法提高了系统处理新型电力系统运行控制问题的效率,可以为新一代调控系统的全面建设和推广应用提供有益参考.

关键词： 数字孪生; 深度学习; 预调度; 新一代调控系统

本文引用格式

王兴志, 翟海保, 严亚勤, 吴庆曦 . 基于数字孪生和深度学习的新一代调控系统预调度方法[J]. 上海交通大学学报, 2021 , 55(S2) : 37 -41 . DOI: 10.16183/j.cnki.jsjtu.2021.S2.006

Abstract

To meet the demand of scalability and decision-making foresight of the traditional dispatching automation system in the new power system, a novel pre-dispatching method of new generation dispatching and control was proposed. First, the power grid digital twin was established in the description subsystem layer, which can reflect the state of power grid primary equipment, secondary equipment, and environment. Then, in the prediction subsystem level, the deep learning models were used to learn and predict future situation or accident risk of power grid operation in power grid digital twin. Finally, the feasibility of the proposed method was verified by the implementation example of East China Grid. The application results show that the pre-dispatching method improves the efficiency of system in dealing with the operation control problems of the new power system, which also provides a useful reference for comprehensive construction, popularization, and application of new generation power systems.

Key words： digital twin; deep learning; pre-dispatching; new generation dispatching and control system

参考文献

[1] 辛耀中, 郭建成, 杨胜春, 等. 智能电网调度控制系统: 总体架构[M]. 北京: 中国电力出版社, 2016.
[1] XIN Yaozhong, GUO Jiancheng, YANG Shengchun, et al. Smart grid dispatching control system: Overall architecture[M]. Beijing: China Electric Power Press, 2016.
[2] 许洪强, 姚建国, 於益军, 等. 支撑一体化大电网的调度控制系统架构及关键技术[J]. 电力系统自动化, 2018, 42(6): 1-8.
[2] XU Hongqiang, YAO Jianguo, YU Yijun, et al. Architecture and key technologies of dispatch and control system supporting integrated bulk power grids[J]. Automation of Electric Power Systems, 2018, 42(6): 1-8.
[3] 潘毅, 于尔铿. 能量管理系统(EMS) 第12讲调度员培训模拟器(DTS)[J]. 电力系统自动化, 1997, 21(12): 76-78.
[3] PAN Yi, YU Erkeng. Energy management system (EMS) Part twelve Dispatcher training simulator[J]. Automation of Electric Power Systems, 1997, 21(12): 76-78.
[4] 张慎明, 姚建国. 调度员培训仿真系统(DTS)的现状和发展趋势[J]. 电网技术, 2002, 26(7): 60-66.
[4] ZHANG Shenming, YAO Jianguo. Current situation and development trend of dispatcher training simulator[J]. Power System Technology, 2002, 26(7): 60-66.
[5] 李峰, 庄卫金, 王勇, 等. 基于PSS/E的可用于调度主站验证的仿真系统设计[J]. 中国电力, 2014, 47(1): 66-70.
[5] LI Feng, ZHUANG Weijin, WANG Yong, et al. PSS/E-based simulation system design for functional verification on the power dispatching automation system[J]. Electric Power, 2014, 47(1): 66-70.
[6] 刘俊, 王勇, 杨胜春, 等. 新一代调控系统预调度架构及关键技术[J]. 电力系统自动化, 2019, 43(22): 201-208.
[6] LIU Jun, WANG Yong, YANG Shengchun, et al. Pre-dispatching architecture and key technologies of new generation dispatching and control system[J]. Automation of Electric Power Systems, 2019, 43(22): 201-208.
[7] 闪鑫, 陆晓, 翟明玉, 等. 人工智能应用于电网调控的关键技术分析[J]. 电力系统自动化, 2019, 43(1): 49-57.
[7] SHAN Xin, LU Xiao, ZHAI Mingyu, et al. Analysis of key technologies for artificial intelligence applied to power grid dispatch and control[J]. Automation of Electric Power Systems, 2019, 43(1): 49-57.
[8] 姜静, 何玉鹏, 张子鹏, 等. 基于在线仿真技术的核电厂事故评价与预测方案[J]. 上海交通大学学报, 2019, 53(Sup.1): 123-126.
[8] JIANG Jing, HE Yupeng, ZHANG Zipeng, et al. Nuclear power plant accident evaluation and prediction method based on online simulation technology[J]. Journal of Shanghai Jiao Tong University, 2019, 53(Sup.1): 123-126.
[9] NEGRI E, FUMAGALLI L, MACCHI M. A review of the roles of digital twin in CPS-based production systems[J]. Procedia Manufacturing, 2017, 11:939-948.
[10] 吴倩红, 韩蓓, 冯琳, 等. “人工智能+”时代下的智能电网预测分析[J]. 上海交通大学学报, 2018, 52(10): 1206-1219.
[10] WU Qianhong, HAN Bei, FENG Lin, et al. “AI+” based smart grid prediction analysis[J]. Journal of Shanghai Jiao Tong University, 2018, 52(10): 1206-1219.
[11] 沈沉, 贾孟硕, 陈颖, 等. 能源互联网数字孪生及其应用[J]. 全球能源互联网, 2020, 3(1): 1-13.
[11] SHEN Chen, JIA Mengshuo, CHEN Ying, et al. Digital twin of the energy Internet and its application[J]. Journal of Global Energy Interconnection, 2020, 3(1): 1-13.
[12] UTKIN L V, RYABININ M A. A Siamese deep forest[J]. Knowledge-Based Systems, 2018, 139:13-22.
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