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13 January 2023 Method of power grid operation rules information recognition based on deep learning
Di Cheng, Yang Wang, Yue Zhang, Tao Jiang, Mengya Zhang, Kefan Zhu, Lianfei Shan, Yu Wang
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Proceedings Volume 12510, International Conference on Statistics, Data Science, and Computational Intelligence (CSDSCI 2022); 125101P (2023) https://doi.org/10.1117/12.2656834
Event: International Conference on Statistics, Data Science, and Computational Intelligence (CSDSCI 2022), 2022, Qingdao, China
Abstract
In order to improve the online application ability and auxiliary decision-making ability of power grid dispatching operation rules, a method for identifying key information of dispatching operation rules based on Bidirectional Long Short-Term Memory network-Conditional Random Field (BiLSTM-CRF) is proposed. A method for feature extraction and feature labeling of dispatching operation rule text is proposed, and BiLSTM is used to learn nonlinear characteristics and implicit timing information between regular text features, and CRF is used to optimize bidirectional network encoding labels globally. The recognized rule text information is combined according to the definition level to form a knowledge graph of dispatching operation rules. Through the verification of the power grid operation rules of a control center, compared with other methods, the proposed model recognizes the average precision rate, recall rate and F1 value of 98.10%, 98.19% and 98.10%, respectively, which can more accurately support the refined retrieval and positioning of the text of the dispatching operation rules.
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Di Cheng, Yang Wang, Yue Zhang, Tao Jiang, Mengya Zhang, Kefan Zhu, Lianfei Shan, and Yu Wang "Method of power grid operation rules information recognition based on deep learning", Proc. SPIE 12510, International Conference on Statistics, Data Science, and Computational Intelligence (CSDSCI 2022), 125101P (13 January 2023); https://doi.org/10.1117/12.2656834
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KEYWORDS
Data modeling
Information operations
Feature extraction
Statistical modeling
Control systems
Performance modeling
Process modeling
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