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基于关联信息对抗学习的综合能源系统运行状态分析方法

胡旭光 马大中 郑君 张化光 王睿

胡旭光, 马大中, 郑君, 张化光, 王睿. 基于关联信息对抗学习的综合能源系统运行状态分析方法. 自动化学报, 2020, 46(9): 1783−1797 doi: 10.16383/j.aas.c200171
引用本文: 胡旭光, 马大中, 郑君, 张化光, 王睿. 基于关联信息对抗学习的综合能源系统运行状态分析方法. 自动化学报, 2020, 46(9): 1783−1797 doi: 10.16383/j.aas.c200171
Hu Xu-Guang, Ma Da-Zhong, Zheng Jun, Zhang Hua-Guang, Wang Rui. An operation state analysis method for integrated energy system based on correlation information adversarial learning. Acta Automatica Sinica, 2020, 46(9): 1783−1797 doi: 10.16383/j.aas.c200171
Citation: Hu Xu-Guang, Ma Da-Zhong, Zheng Jun, Zhang Hua-Guang, Wang Rui. An operation state analysis method for integrated energy system based on correlation information adversarial learning. Acta Automatica Sinica, 2020, 46(9): 1783−1797 doi: 10.16383/j.aas.c200171

基于关联信息对抗学习的综合能源系统运行状态分析方法

doi: 10.16383/j.aas.c200171
基金项目: 国家重点研发计划(2018YFA0702200), 国家自然科学基金(61773109, 61627809, 61621004), 辽宁省“兴辽英才计划”项目(XLYC1801005, XLYC1807009)资助
详细信息
    作者简介:

    胡旭光:东北大学信息科学与工程学院博士研究生. 主要研究方向为基于数据驱动的故障诊断, 信息物理系统的建模及优化控制.E-mail: 1710252@stu.neu.edu.cn

    马大中:东北大学信息科学与工程学院副教授. 主要研究方向为故障诊断, 容错控制, 能源管理系统以及分布式发电系统、微网和能源互联网的优化与控制. 本文通信作者.E-mail: madazhong@ise.neu.edu.cn

    郑君:东北大学信息科学与工程学院硕士研究生. 主要研究方向为基于机器学习的综合能源系统故障检测与诊断.E-mail: zj623928036@163.com

    张化光:东北大学信息科学与工程学院教授. 主要研究方向为自适应动态规划, 模糊控制, 网络控制, 混沌控制. E-mail: hgzhang@ieee.org

    王睿:东北大学信息科学与工程学院博士研究生. 2016年于东北大学获得电气工程及其自动化专业学士学位. 主要研究方向为能源互联网中分布式电源的协同优化及其电磁时间尺度稳定性分析.E-mail: 1610232@stu.neu.edu.cn

An Operation State Analysis Method for Integrated Energy System Based on Correlation Information Adversarial Learning

Funds: Supported by National Key Research and Development Program of China (2018YFA0702200), National Natural Science Foundation of China (61773109, 61627809, 61621004), and Liaoning Revitalization Talents Program (XLYC1801005, XLYC1807009)
  • 摘要: 综合能源系统(Integrated energy system, IES)运行状态分析常以广泛化信息技术应用提供的数据为支撑, 然而传感器故障、网络通信中断等信息异常导致的数据缺失会直接影响数据质量. 在考虑数据缺失的情况下, 本文提出了一种基于关联信息对抗学习的综合能源系统运行状态分析方法. 首先构建深度生成对抗网络(Generative adversarial network, GAN)对数据缺失部分进行可靠性补偿. 在设计生成器结构过程中, 通过引入系统拓扑邻接矩阵对生成器输入数据进行优化排序, 进而在训练过程中采用设计的多属性融合生成器损失函数, 促使生成器进一步得到高精度补偿数据. 接着将判别器提取的不同时刻完整能源数据的特征作为基础, 采用浅层特征分布及深层特征信息差异值融合判断, 从而实现系统运行状态分析. 最后对不同数据缺失补偿及不同类型节点改变情况进行仿真, 验证了本文所提方法的可行性与有效性.
  • 图  1  GAN结构示意图

    Fig.  1  Diagram of GAN structure

    图  2  系统状态判断方法

    Fig.  2  Operation state judgement method

    图  3  状态分析方法流程图

    Fig.  3  Flowchart of operation state analysis method

    图  4  综合能源系统结构图

    Fig.  4  Integrated energy system structure

    图  5  电节点15数据缺失补偿曲线

    Fig.  5  Data imputation curves of electricity node 15

    图  6  多节点数据缺失补偿曲线

    Fig.  6  Multi-node data imputation curves

    图  7  电节点21变化前后系统数据及特征

    Fig.  7  IES data and features change of electricity node 21

    图  8  电节点21变化前后系统浅层特征分布曲线

    Fig.  8  Data distribution change of shallow features for electricity node 21

    图  9  热节点18变化前后系统数据及特征

    Fig.  9  IES data and features change of heat node 18

    图  10  热节点18变化前后系统浅层特征分布曲线

    Fig.  10  Data distribution change of shallow features for heat node 18

    表  1  多节点数据缺失补偿结果 (×10−5)

    Table  1  Multi-node data imputation results (×10−5)

    补偿节点 MAE MSE MPE
    电节点9 1.4852 1.4948 1.4136
    电节点12 1.5093 1.5188 1.4418
    气节点7 1.5250 1.5362 2.1993
    下载: 导出CSV

    表  2  不同数量的缺失数据补偿结果 (×10−5)

    Table  2  Imputation results of different numbers of missing data (×10−5)

    缺失数量 MAE MSE MPE
    1 1.5022 1.4926 1.5008
    3 1.5190 1.5205 1.5063
    5 1.5183 1.5194 1.5253
    7 1.5852 1.5933 1.5757
    9 1.6232 1.6201 1.6193
    下载: 导出CSV

    表  3  不同方法补偿结果对比 (×10−5)

    Table  3  Comparison of different data imputation methods (×10−5)

    补偿方法 MAE MSE MPE
    CNN 2.3248 2.3003 2.2741
    DAE 2.2428 2.1892 2.1505
    DCGAN 1.9255 1.8864 1.8469
    DCGAN-L1 1.8421 1.7605 1.7844
    Pix2Pix 1.7274 1.6148 1.6303
    本文方法 1.5934 1.4835 1.4492
    下载: 导出CSV

    表  4  不同节点的状态判断结果(%)

    Table  4  State judgment results of different nodes (%)

    变化节点 (0, 1%) (1%, 2%) (2%, 3%) (3%, 4%) (4%, 5%)
    电节点17 0 60 100 100 100
    电节点21 0 70 100 100 100
    热节点18 0 70 100 100 100
    热节点30 0 70 100 100 100
    下载: 导出CSV
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  • 收稿日期:  2020-03-31
  • 录用日期:  2020-06-28
  • 网络出版日期:  2020-09-28
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