How to Construct a Power Knowledge Graph with Dispatching Data?

Abstract

Knowledge graph is a kind of semantic network for information retrieval. How to construct a knowledge graph that can serve the power system based on the behavior data of dispatchers is a hot research topic in the area of electric power artificial intelligence. In this paper, we propose a method to construct the dispatch knowledge graph for the power grid. By leveraging on dispatch data from the power domain, this method first extracts entities and then identifies dispatching behavior relationship patterns. More specifically, the method includes three steps. First, we construct a corpus of power dispatching behaviors by semi-automated labeling. And then, we propose a model, called the BiLSTM-CRF model, to extract entities and identify the dispatching behavior relationship patterns. Finally, we construct a knowledge graph of power dispatching data. The knowledge graph provides an underlying knowledge model for automated power dispatching and related services and helps dispatchers perform better power dispatch knowledge retrieval and other operations during the dispatch process.

Data switching method among heterogeneous power information system databases based on knowledge graph

“…These methods are more direct and convenient to solve the problem of data error correction in a broad sense, but the data exchange between heterogeneous power information system databases has its unique characteristics, On the one hand, most of the data exchange errors between heterogeneous power information system databases are due to the fact that one data is identified as another data in the process of data identification. There is similarity between the wrong data and the correct data, and the data complexity and data structure are similar; On the other hand, the wrong data after data exchange between heterogeneous power information system databases is only related to the data structure, and has nothing to do with the database where the correct data is located 12 . According to the characteristics of data exchange between heterogeneous power information system databases, this paper designs an error correction method for these two characteristics, and the error correction framework is shown in Figure 3.…”

“…between heterogeneous power information system databases is only related to the data structure, and has nothing to do with the database where the correct data is located. 12 According to the characteristics of data exchange between heterogeneous power information system databases, this paper designs an error correction method for these two characteristics, and the error correction framework is shown in Figure 3. Firstly, the collected data is segmented, and all kinds of data in the field of statistics are analyzed.…”

Domain-specific knowledge graphs: A survey

“…Electric power artificial intelligence systems have also contributed to the KGs construction and augmentation. For example, Fan et al [131] proposed an approach to construct the dispatch KG for the power grid to semantically describing behavior of dispatchers. They follow semi-automated labelling to construct a power corpus, then BiLSTM-CRF model was used to extract entities and indicate the dispatching behavior relationship patterns.…”

“…In fact, KG evaluation has been indicated as one of the most indicated weaknesses amongst the examined studies. For example, some studies carried out a superficial and subjective evaluation to the KG construction with no incorporation to concrete evaluation metrics [131,141,193]. Another thread of efforts attempted to involve theoretically proven evaluation metrics to systematically measure KG completion and KG correctness approaches.…”

Construction of the Evaluation Index System of Physical Education Teaching in Colleges and Universities Based on Scientific Knowledge Graph

“…Fan et al proposed a method to build a knowledge graph of electric grid scheduling. is method utilizes the dispatching data in the power field and then recognizes patterns of scheduling conduct in relation to construct a knowledge graph of electric power scheduling data and provide an embedded intellectual module for automated electric power scheduling and associated activities [3] [5]. Chernova uses the knowledge graph to devise a perceptual module of KM, which is used to form a complete education plan in the training system.…”