Application of natural language understanding in Chinese power dispatching centre

It is difficult for computer to understand the texts in unstructured Chinese language, which becomes an obstacle for further application of artificial intelligence in the power dispatch center. Understanding of the orders from human dispatchers is the premise for the collaboration of machine and human being in power system operation. Towards understanding of dispatching texts, this paper proposes a textual semantic analysis framework with active learning of the semantic structure knowledge. Firstly, the words are vectorized by the Skip-gram models. And the hierarchical clustering algorithm is designed to detect the sentence patterns. Then the knowledge base is set up by converting the sentence structure to their regular expressions. In application, define a proprietary semantic framework to extract important device information and to parse the semantic slot using dependency syntax. Application shows that the Chinese texts describing the operation mode switching process can be understood accurately by the computer program.