Identification Technology of Grid Monitoring Alarm Event Based on Natural Language Processing and Deep Learning in China
Ziyu Bai,
Guoqiang Sun,
Haixiang Zang,
Ming Zhang,
Peifeng Shen,
Yi Liu and
Zhinong Wei
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Ziyu Bai: College of Energy and Electrical Engineering, Hohai University, Nanjing 210098, China
Guoqiang Sun: College of Energy and Electrical Engineering, Hohai University, Nanjing 210098, China
Haixiang Zang: College of Energy and Electrical Engineering, Hohai University, Nanjing 210098, China
Ming Zhang: Nanjing Power Supply Company of State Grid Jiangsu Electric Power Co., Ltd., Nanjing 210019, China
Peifeng Shen: Nanjing Power Supply Company of State Grid Jiangsu Electric Power Co., Ltd., Nanjing 210019, China
Yi Liu: State Grid Jiangsu Electric Power Co., Ltd., Nanjing 210024, China
Zhinong Wei: College of Energy and Electrical Engineering, Hohai University, Nanjing 210098, China
Energies, 2019, vol. 12, issue 17, 1-19
Abstract:
Power dispatching systems currently receive massive, complicated, and irregular monitoring alarms during their operation, which prevents the controllers from making accurate judgments on the alarm events that occur within a short period of time. In view of the current situation with the low efficiency of monitoring alarm information, this paper proposes a method based on natural language processing (NLP) and a hybrid model that combines long short-term memory (LSTM) and convolutional neural network (CNN) for the identification of grid monitoring alarm events. Firstly, the characteristics of the alarm information text were analyzed and induced and then preprocessed. Then, the monitoring alarm information was vectorized based on the Word2vec model. Finally, a monitoring alarm event identification model based on a combination of LSTM and CNN was established for the characteristics of the alarm information. The feasibility and effectiveness of the method in this paper were verified by comparison with multiple identification models.
Keywords: power grid monitoring; alarm information mining; Word2vec; long short-term memory network; convolutional neural network (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2019
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Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:12:y:2019:i:17:p:3258-:d:260523
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