Transformer Defect Recognition and Maintenance Decision-making Method Based on CNN-BiGRU-ATTENTION and Knowledge Graph
Traditional transformer operation and maintenance(O&M)requires manual judgment of the severity of equipment defects from complex operation defect records and then makes maintenance decisions,leading to low defect handling efficiency.To address this issue,this paper proposes a CNN-BiGRU-ATTENTION algorithm composed convolutional neural network(CNN),bidirectional gated recurrent unit(BiGRU),and attention mechanism to realize transformer defect classification recognition;then,based on transformer standards,test procedures,and operation experience,a transformer maintenance decision-making knowledge graph is constructed;finally,according to the classification recognition results of defect records and the relationship between defect maintenance measures in the maintenance decision-making knowledge graph,the maintenance decision-making knowledge graph pushes the defect maintenance measures,realizing the intelligent decision-making process from defect records to O&M maintenance.Taking the 35 kV and above transformer defect records of a city-level power grid as training samples,the experimental results that the accuracy of defect record classification of the model is over 88%,and based on the defect record classification results,the intelligent push of maintenance decisions from maintenance decision-making knowledge graph effectively improves the O&M efficiency,which has certain practical value.
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