Dynamic Fault Analysis in Substations Based on Knowledge Graphs

TL;DR

This paper presents a novel method combining natural language processing, hidden Markov models, and knowledge graphs to dynamically analyze and visualize hidden dangers in electrical substations from unstructured text data.

Contribution

It introduces an integrated approach that extracts, models, and visualizes hidden dangers in substations using knowledge graphs and advanced data analysis techniques.

Findings

Effective identification of hidden dangers from unstructured text

Successful visualization of dangers using knowledge graphs

Demonstrated approach improves safety analysis in substations

Abstract

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.