用于未来态预测的电网运行断面时空相似性挖掘

doi:10.19595/j.cnki.1000-6753.tces.211486

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Abstract Effective prediction of operation state can provide a data basis for power grid risk prediction and optimal regulation, which is of great significance for ensuring the system safe operation. This paper proposes a method for mining the temporal and spatial similarity of power grid running section for future state prediction. Firstly, the graph representation learning algorithm was used to deeply unsupervised learn the power grid topology and its attribute information, and the attribute vector representing the spatial features of running section was extracted. Further, the spatial feature vectors corresponding to the historical running sections was divided into multiple windows according to different time periods through the sliding time window algorithm. Finally, the similarity of corresponding samples between different windows was calculated from the microscopic and macroscopic perspectives to obtain a group of continuous sections that were most similar to the sections in the current period. The subsequent section of the most similar historical section was used as a reference for the future state of the current power grid. The results validated by IEEE 39-bus system and practical power grid simulation examples show that the proposed method can effectively match the most similar historical sections, then realize the auxiliary prediction of the future operation state.

Key words： Operation state prediction graph representation learning algorithm running section spatial feature period division similarity matching

Received: 23 September 2021

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TM732

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	Articles by authors
	Gu Xueping
	Liu Tong
	Li Shaoyan
	Wang Tieqiang
	Yang Xiaodong

Cite this article:

Gu Xueping,Liu Tong,Li Shaoyan等. Temporal and Spatial Similarity Mining of Power Grid Running Section for Future State Prediction[J]. Transactions of China Electrotechnical Society, 2022, 37(23): 6145-6156.

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https://dgjsxb.ces-transaction.com/EN/10.19595/j.cnki.1000-6753.tces.211486 OR https://dgjsxb.ces-transaction.com/EN/Y2022/V37/I23/6145

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