An Operation State Analysis Method for Integrated Energy System Based on Correlation Information Adversarial Learning
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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.Abstract: The operation state analysis of integrated energy system (IES) is based on data provided through information technology application. However, data missing caused by ubiquitous device failure, network interruption and so on, has direct influence on quality of data. Considering data missing, this paper proposes an operation state analysis method for integrated energy system based on correlation information adversarial learning. Firstly, deep generative adversarial network (GAN) is presented to complete the reliable data imputation process. In the designed generator structure, an adjacent matrix is presented to optimize the input data sorting, and then the multi-attribute fusion loss function in training process is utilized to drive generator for obtaining high precision completed data. Based on different time completed energy data features extracted by discriminator, difference values of shallow and deep features are considered as the judging index to realize operation state analysis. Finally, the feasibility and effectiveness of the proposed method are verified through the different data imputation situation and the changes of different types of nodes.
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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 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 
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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
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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
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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
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