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.
Highlights
With the rapid construction of power informatization, there has been explosive growth in power-grid data
For the sake alarm of further studyingmining, the application of deep learning andmonitoring its combination grid monitoring information this paper proposes a grid alarm model event in grid monitoring alarm information mining, this paper proposes a grid monitoring alarm event identification method based on natural language processing (NLP) and a long short-term memory (LSTM)-convolutional neural network (CNN) combination identification method based on NLP
For the sake of studying the application effect of the monitoring alarm event identification model constructed in this paper, a total of more than 14 million historical monitoring alarm information of a city grid company in 2016 and 2017 was used as a corpus, and nine types of alarm event samples were extracted for training and testing
Summary
With the rapid construction of power informatization, there has been explosive growth in power-grid data. It is easy for the regulator to miss important information regarding an alarm and they cannot accurately identify it in a short period of time. The text mining of historical alarm information and the establishment of a fast and accurate identification method have become important issues in the field of power dispatching. Rough set (RS) [1,2], Petri net [3,4,5], Bayesian network [6,7,8], and fuzzy set (FS) [9,10,11] have been successfully applied in the intelligent identification and alarm of power systems. A rule base is generated by using expert experience knowledge and fuzzy inference matching rules are applied to the alarm information to Energies 2019, 12, 3258; doi:10.3390/en12173258 www.mdpi.com/journal/energies
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