基于协同执行器的GCPS自适应调度模型

doi:10.16182/j.issn1004731x.joss.17-0404

系统仿真学报 ›› 2019, Vol. 31 ›› Issue (10): 2112-2121.doi: 10.16182/j.issn1004731x.joss.17-0404

基于协同执行器的GCPS自适应调度模型

张晶, 陈垚, 孙俊^*, 范洪博

昆明理工大学信息工程与自动化学院,云南昆明 650500

收稿日期:2017-08-17 修回日期:2017-11-19 出版日期:2019-10-10 发布日期:2019-12-12
作者简介:张晶(1974-),男,云南昆明,博士,教授,研究方向为实时嵌入式控制软件;陈垚(1993-),男,福建莆田,硕士生,研究方向为信息物理系统。
基金资助:
国家自然科学基金(61562051), 云南省应用基础研究计划重点项目(2014FA029)

GCPS Adaptive Scheduling Model Based on Cooperative Executor

Zhang Jing, Chen Yao, Sun Jun^*, Fan Hongbo

Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, China

Received:2017-08-17 Revised:2017-11-19 Online:2019-10-10 Published:2019-12-12

摘要/Abstract

摘要： 针对电网信息物理系统的不确定性易导致系统连锁故障的问题,建立基于协同执行器的GCPS自适应调度模型;分析了系统约束条件,提出了一种GCPS系统模型约束条件刻画电网系统的约束条件,并证明其度量与表示方法的一致性,通过PILOT求解逼近误差最优值,描述了模型框架,通过Ptolemey II平台对模型的性能指标、输出功率准确度及故障对系统的影响进行仿真分析,实验结果表明模型可较为准确地描述电源输出功率,且系统对故障有较好的适应能力。

关键词: 电网信息物理系统, 不确定性, 实时性, 自适应调度, 约束条件

Abstract: Aiming at the problem that the uncertainty of grid cyber physical systems leads to chain failure, an adaptive GCPS dispatching model based on co-actuator is established. First, the constraint conditions of the system are analyzed. A model constraints of GCPS system is presented to describe the constraint conditions of the power system, and it is proved that it meets the consistency of the measure and representing methods. Second, the optimal value of approximation error is solved by PILOT, and the framework of CA-SADM is described. Finally, the performance index, output power accuracy and the influence of fault on the system are simulated and analyzed by Ptolemy II platform. The experimental results show that the model can describe the output power accurately, and the system has good adaptability to the faults.

Key words: GCPS, Uncertainty, Real-time, Self-Adaptive Dispatching, Constraint conditions

中图分类号:

TP302.7

张晶, 陈垚, 孙俊, 范洪博. 基于协同执行器的GCPS自适应调度模型[J]. 系统仿真学报, 2019, 31(10): 2112-2121.

Zhang Jing, Chen Yao, Sun Jun, Fan Hongbo. GCPS Adaptive Scheduling Model Based on Cooperative Executor[J]. Journal of System Simulation, 2019, 31(10): 2112-2121.

参考文献

[1] 何积丰. Cyber-physical Systems[J]. 中国计算机学会通讯, 2010, 6(1): 25-29.
He Jifeng.Cyber-physical Systems[J]. Communications of CCF, 2010, 6(1): 25-29.
[2] Palensky P, Widl E, Elsheikh A.Simulating cyber-physical energy systems: challenges, tools and methods[J]. IEEE Transactions on Systems, Man and Cybernetcs: Systems (S2168-2216), 2014, 44(3): 318-326.
[3] Gao Y, Gao F, Zhai Q, et al.Self-Balancing Dynamic Scheduling of Electrical Energy for Energy-intensive Enterprises[J]. International Journal of Sytems Sciences (S0020-7721), 2013, 44(6): 1006-1025.
[4] 李仁发, 谢勇, 李蕊, 等. 信息-物理融合系统若干关键问题综述[J]. 计算机研究与发展, 2012, 49(6): 1149-1161.
Li Renfa, Xie Yong, Li Rui, et al.Survey of cyber-physical systems[J]. Journal of Computer Research Development, 2012, 49(6): 1149-1161.
[5] Susuki Y, Koo T J, Ebina H, et al.A Hybrid System Approach to the Analysis and Design of Power Grid Dynamic Performance[J]. Proceedings of the IEEE (S0018-9219), 2012, 100(1): 225-239.
[6] Ilic M D, Xie L, Khan U A, et al.Modeling of future cyber physical energy systems for distributed sensing and control[J]. IEEE Trans on Systems, Man and Cybernetics, Part A: Systems and Humans (S1083-4427), 2010, 40(7): 825-838.
[7] 郭庆来, 辛蜀骏, 孙宏斌, 等. 电力系统信息物理融合建模与综合安全评估: 驱动力与研究构想[J]. 中国电机工程学报, 2016, 36(6): 1481-1489.
Guo Qinglai, Xin Shujun, Sun Hongbin, et al.Power system cyber-physical modelling and security assessment: motivation and ideas[J]. Proceeding of the CSEE, 2016, 36(6): 1481-1489.
[8] 王冰玉, 孙秋野, 马大中, 等. 能源互联网多时间尺度的信息物理融合模型[J]. 电力系统自动化, 2016, 40(17): 13-21.
Wang Bingyu, Sun Qiuye, Ma Dazhong, et al.A cyber physical model of the energy internet based on multiple time scales[J]. Automation of Electric Power Systems, 2016, 40(17): 13-21.
[9] 韩宇奇, 郭创新, 朱炳铨, 等. 基于改进渗流理论的信息物理融合电力系统连锁故障模型[J]. 电力系统自动化, 2016, 40(17): 30-37.
Han Yuqi, Guo Chuangxin, Zhu Bingquan, et al.Model cascading failures in cyber physical power system based on improved percolation theory[J]. Automation of Electric Power Systems, 2016, 40(17): 30-37.
[10] Kundur D, Feng X, Mashayekh S, et al.Towards modelling the impact of cyber-attacks on a smart grid[J]. International Journal of Security & Networks, 2011, 6(1): 2-13.
[11] 赵俊华, 文福拴, 薛禹胜, 等. 电力信息物理融合系统的建模分析与控制研究框架[J]. 电力系统自动化, 2011, 35(16): 1-8.
Zhao Junhua, Wen Fusuan, Xue Yusheng, et al.Modeling analysis and control research framework of cyber physical power system[J]. Automation of Electric Power Systems, 2011, 35(16): 1-8.
[12] Alur R, Dill D L.A theory of timed automata[J]. Theoretical Computer Science, 1994, 126(2): 183-235.
[13] Tong B, Zhai Q, Guan X.An MILP Based Formulation for Short-Term Hydro Generation Scheduling With Analysis of the Linearization Effects on Solution Feasibility[J]. IEEE Transactions on Power Systems (S0885-8950), 2013, 28(4): 3588-3599.
[14] Sarjoughian H S, Sundaramoorthi S.Superdense time trajectories for DEVS simulation models[C]. Symposium on Theory of Modeling & Simulation: Devs Integrative M&S Symposium. Society for Computer Simulation International. Alexandria, VA, USA. 2015: 249-256.
[15] 张晶, 陈垚, 范洪博, 等. 信息物理融合系统任务调度权限控制策略[J]. 计算机工程, 2017, 43(4): 60-66.
Zhang Jing, Chen Yao, Fan Hongbo, et al.Control Strategy of Task Scheduling Permission in Cyber-physical System[J]. Computer Engineering, 2017, 43(4): 60-66.
[16] 张晶, 陈垚, 范洪博, 等. 基于信息物理融合系统执行器输出事件的价值评价调度策略[J]. 计算机应用, 2017, 37(6): 1663-1669.
Zhang Jing, Chen Yao, Fan Hongbo, et al.Scheduling strategy of value evaluation for output-event of actor based on cyber-physical system[J]. Journal of Computer Applications, 2017, 37(6): 1663-1669.
[17] Akkaya I, Liu Y, Lee E A.Modeling and Simulation of Network Aspects for Distributed Cyber-Physical Energy Systems[M]. Cyber Physical Systems Approach to Smart Electric Power Grid. Springer Berlin Heidelberg, 2015: 1-23.
[18] 徐洪智, 李仁发, 曾理宁. 基于Ptolemy的信息物理融合系统建模与仿真[J]. 系统仿真学报, 2014, 26(8): 1633-1638.
Xu Hongzhi, Li Renfa, Zeng Lining.Modeling and simulation of cyber-physical system based on Ptolemy[J]. Journal of System Simulation, 2014, 26(8): 1633-1638.
[19] 卢强,戚晓耀,何光宇. 智能电网与智能广域机器人[J]. 中国电机工程学报, 2011, 31(10): 1-5.
Lu Qiang, Qi Xiaoyao, He Guangyu.Smart Grid and Smart Wide Area Robot[J]. Proceedings of the CSEE, 2011, 31(10): 1-5.

基于协同执行器的GCPS自适应调度模型

GCPS Adaptive Scheduling Model Based on Cooperative Executor

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