Data-driven Electricity-gas Integrated Energy System Situation Awareness Considering Time Series Correlation

被引：0

作者：

Lin Z. ^{[1
]}

Jiang F. ^{[1
]}

Tu C. ^{[2
]}

He G. ^{[3
]}

Zhang X. ^{[3
]}

Liu K. ^{[3
]}

机构：

[1] School of Electrical and Information Engineering, Changsha University of Science and Technology, Hunan Province, Changsha

[2] National Electric Power Conversion and Control Engineering Technology Research Center (Hunan University), Hunan Province, Changsha

[3] China Electric Power Research Institute, Haidian District, Beijing

来源：

Dianwang Jishu/Power System Technology | 2022年 / 46卷 / 09期

基金：

湖南省自然科学基金;

关键词：

convolutional neural network; data-driven technology; integrated energy system; situation awareness;

D O I：

10.13335/j.1000-3673.pst.2022.0213

中图分类号：

学科分类号：

摘要：

Efficient and accurate situation awareness technology is the key to early warning of operational risks of the electricity-gas integrated energy system (EGIES). However, the situation awareness technology of the traditional power system cannot fully adapt to the strong nonlinearity and the differential energy coupling characteristic of the EGIES. The rapid development of data-driven technology provides new ideas for the situation awareness of the EGIES. A data-driven electric-gas integrated energy system situation awareness considering the time series correlation is proposed. In the situation perception stage, the state estimation is used to filter the measurement noise, and then solve the state variables of the EGIES and perceive the system deviation. In the situation comprehension stage, based on the Gramian angular difference field theory the time series correlation of the historical deviation changes are comprehended, and the operating trend of future deviation changes are qualitatively understood at the same time. In the situation projection stage, a convolutional neural network model is established, and the historical deviation is used to project the future operation trend of the system. A case study of the EGIES coupling a 14-node power system with a 7-node gas system shows: compared with the long/short-term memory neural network and the support vector machine, the projection accuracy of the proposed method is increased by 2.87% and 4.95%, respectively. It has a higher projection accuracy when the proportion of the training set changes. © 2022 Power System Technology Press. All rights reserved.

引用

页码：3385 / 3393

页数：8

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