The naming conventions of dual sets of fault recorders configured by various manufacturers in different construction periods of a smart substation are different. This leads to inconsistent names of different devices in the same channel of fault recorder files. Manual amendment at a later stage involves a heavy workload, takes a long time and cannot ensure the correctness of the results. Thus a method is proposed for identifying the names of recorder file channels based on incremental learning optimization. First, the channel names are extracted from fault recorder configuration files and text preprocessing is implemented. Second, the Word2vec model based on incremental learning optimization is employed to achieve the generation and online learning of Chinese word vectors for the channel names. Third, a text-similarity matching algorithm that combines cosine similarity and inverse document frequency (IDF) is adopted to realize channel name identification. Finally, experiments are carried out with empirical data obtained from the channel names extracted from recorded files. The results show that the proposed method has effectively improved the adaptivity and accuracy of name identification of recorder file channels.

This work is supported by the National Natural Science Foundation of China (No. 51877084).