Dynamic Fault Analysis in Substations Based on Knowledge Graphs
This addresses the challenge of fault analysis in substations for power grid operators, but it appears incremental as it combines existing techniques like hidden Markov models and knowledge graphs for a specific domain application.
The paper tackles the problem of identifying hidden dangers in substations from unstructured text by proposing a dynamic analysis method that extracts information, uses a distributed search engine, trains data with a hidden Markov model, and visualizes results in a knowledge graph, with effectiveness demonstrated through a case analysis from a specific substation.
To address the challenge of identifying hidden danger in substations from unstructured text, a novel dynamic analysis method is proposed. We first extract relevant information from the unstructured text, and then leverages a flexible distributed search engine built on Elastic-Search to handle the data. Following this, the hidden Markov model is employed to train the data within the engine. The Viterbi algorithm is integrated to decipher the hidden state sequences, facilitating the segmentation and labeling of entities related to hidden dangers. The final step involves using the Neo4j graph database to dynamically create a knowledge graph that visualizes hidden dangers in the substation. The effectiveness of the proposed method is demonstrated through a case analysis from a specific substation with hidden dangers revealed in the text records.