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                    基于關聯信息對抗學習的綜合能源系統運行狀態分析方法

                    胡旭光 馬大中 鄭君 張化光 王睿

                    胡旭光, 馬大中, 鄭君, 張化光, 王睿. 基于關聯信息對抗學習的綜合能源系統運行狀態分析方法. 自動化學報, 2020, 46(9): 1783?1797 doi: 10.16383/j.aas.c200171
                    引用本文: 胡旭光, 馬大中, 鄭君, 張化光, 王睿. 基于關聯信息對抗學習的綜合能源系統運行狀態分析方法. 自動化學報, 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

                    基于關聯信息對抗學習的綜合能源系統運行狀態分析方法

                    doi: 10.16383/j.aas.c200171
                    基金項目: 國家重點研發計劃(2018YFA0702200), 國家自然科學基金(61773109, 61627809, 61621004), 遼寧省“興遼英才計劃”項目(XLYC1801005, XLYC1807009)資助
                    詳細信息
                      作者簡介:

                      胡旭光:東北大學信息科學與工程學院博士研究生. 主要研究方向為基于數據驅動的故障診斷, 信息物理系統的建模及優化控制.E-mail: 1710252@stu.neu.edu.cn

                      馬大中:東北大學信息科學與工程學院副教授. 主要研究方向為故障診斷, 容錯控制, 能源管理系統以及分布式發電系統、微網和能源互聯網的優化與控制. 本文通信作者.E-mail: madazhong@ise.neu.edu.cn

                      鄭君:東北大學信息科學與工程學院碩士研究生. 主要研究方向為基于機器學習的綜合能源系統故障檢測與診斷.E-mail: zj623928036@163.com

                      張化光:東北大學信息科學與工程學院教授. 主要研究方向為自適應動態規劃, 模糊控制, 網絡控制, 混沌控制. E-mail: hgzhang@ieee.org

                      王睿:東北大學信息科學與工程學院博士研究生. 2016年于東北大學獲得電氣工程及其自動化專業學士學位. 主要研究方向為能源互聯網中分布式電源的協同優化及其電磁時間尺度穩定性分析.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: 綜合能源系統(Integrated energy system, IES)運行狀態分析常以廣泛化信息技術應用提供的數據為支撐, 然而傳感器故障、網絡通信中斷等信息異常導致的數據缺失會直接影響數據質量. 在考慮數據缺失的情況下, 本文提出了一種基于關聯信息對抗學習的綜合能源系統運行狀態分析方法. 首先構建深度生成對抗網絡(Generative adversarial network, GAN)對數據缺失部分進行可靠性補償. 在設計生成器結構過程中, 通過引入系統拓撲鄰接矩陣對生成器輸入數據進行優化排序, 進而在訓練過程中采用設計的多屬性融合生成器損失函數, 促使生成器進一步得到高精度補償數據. 接著將判別器提取的不同時刻完整能源數據的特征作為基礎, 采用淺層特征分布及深層特征信息差異值融合判斷, 從而實現系統運行狀態分析. 最后對不同數據缺失補償及不同類型節點改變情況進行仿真, 驗證了本文所提方法的可行性與有效性.
                    • 圖  1  GAN結構示意圖

                      Fig.  1  Diagram of GAN structure

                      圖  2  系統狀態判斷方法

                      Fig.  2  Operation state judgement method

                      圖  3  狀態分析方法流程圖

                      Fig.  3  Flowchart of operation state analysis method

                      圖  4  綜合能源系統結構圖

                      Fig.  4  Integrated energy system structure

                      圖  5  電節點15數據缺失補償曲線

                      Fig.  5  Data imputation curves of electricity node 15

                      圖  6  多節點數據缺失補償曲線

                      Fig.  6  Multi-node data imputation curves

                      圖  7  電節點21變化前后系統數據及特征

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

                      圖  8  電節點21變化前后系統淺層特征分布曲線

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

                      圖  9  熱節點18變化前后系統數據及特征

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

                      圖  10  熱節點18變化前后系統淺層特征分布曲線

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

                      表  1  多節點數據缺失補償結果 (×10?5)

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

                      補償節點 MAE MSE MPE
                      電節點9 1.4852 1.4948 1.4136
                      電節點12 1.5093 1.5188 1.4418
                      氣節點7 1.5250 1.5362 2.1993
                      下載: 導出CSV

                      表  2  不同數量的缺失數據補償結果 (×10?5)

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

                      缺失數量 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
                      下載: 導出CSV

                      表  3  不同方法補償結果對比 (×10?5)

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

                      補償方法 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
                      下載: 導出CSV

                      表  4  不同節點的狀態判斷結果(%)

                      Table  4  State judgment results of different nodes (%)

                      變化節點 (0, 1%) (1%, 2%) (2%, 3%) (3%, 4%) (4%, 5%)
                      電節點17 0 60 100 100 100
                      電節點21 0 70 100 100 100
                      熱節點18 0 70 100 100 100
                      熱節點30 0 70 100 100 100
                      下載: 導出CSV
                      360彩票
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                    • 收稿日期:  2020-03-31
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