Cognitive knowledge graph generation for grid fault handling based on attention mechanism combined with multi-modal factor fusion

Abstract

With the rapid development of artificial intelligence, reinforcement learning frameworks combined with knowledge graph have been widely applied in power grid fault detection and handling. However, the existing algorithms lack the consideration of multi-modal factors, which makes it difficult to improve the accuracy of prediction. To solve the above problems, we propose the cognitive knowledge graph generation algorithm for grid fault handling based on the attention mechanism combined with multi-modal factors fusion. It uses the variational auto-encoder to initialize the embedding of power equipment nodes with different modal factor constraints and dynamically fuses the embeddings of nodes based on attention mechanism, then we train the representation of nodes and relations based on TransR and construct a cognitive knowledge graph of power grid fault handling. Our algorithm is validated on the power grid fault handling data set, and the accuracy is improved.