Predictive maintenance strategy of running fault based on ELM algorithm for power transformer

Wu, Qian; Yang, Xiu; Deng, Renming

doi:10.1504/ijims.2018.10013208

Cited by 1 publication

(1 citation statement)

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“…Based on this, this paper proposes a method for constructing a knowledge graph for power transformer maintenance based on ALBERT. First, using transformer maintenance guidelines [16] as a guide and combining expert opinions, the entities and relationships related to power transformer maintenance are classified to construct the ontology layer of the knowledge graph. Next, publicly available power transformer maintenance literature is collected using the Selenium framework.…”

Section: Introductionmentioning

confidence: 99%

Improvement of operation and maintenance efficiency of power transformers based on knowledge graphs

Yang,

Meng,

Zhang

2024

IET Electric Power Appl

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Currently, the digital transformation of the power grid is underway, and the intelligent health management technology for power transformers is rapidly advancing. However, there are issues in the operation and maintenance process, such as weak information correlation and low decision‐making efficiency. Knowledge graphs have been applied in other industrial fields, such as spacecraft maintenance, to significantly improve knowledge query efficiency. However, there is a lack of literature on knowledge graph construction in the field of power transformer operation and maintenance. Additionally, there is limited publicly available data and difficulties in effectively mining operation and maintenance knowledge in this field. A method for constructing a knowledge graph for power transformer operation and maintenance based on ALBERT is proposed. Firstly, publicly available literature in the field of power transformers is collected, and a sample enhancement method using regular matching is used to enrich the semi‐structured corpora, such as power system accident investigation reports, to construct a training dataset for power transformer operation and maintenance. Then, the ALBERT‐BiLSTM‐CRF deep learning algorithm is applied to extract power transformer operation and maintenance entities from the relevant literature and accident investigation reports, and this method is compared with traditional deep learning algorithms to demonstrate its superiority. Subsequently, the ALBERT‐BiLSTM‐Attention deep learning algorithm, which incorporates ALBERT and attention mechanism, is utilised to extract relationships between power transformer operation and maintenance entities. Compared to other deep learning algorithms, this algorithm demonstrates better performance in the domain‐specific texts of power transformer operation and maintenance. Finally, the Neo4j graph database is used to visualise and present the knowledge graph, enabling decision support based on the power transformer operation and maintenance knowledge graph.

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Section: Introductionmentioning

confidence: 99%