Distributed Power Grid Fault Detection and Classification Strategy Based on Data Mining
The increasing scale of distributed energy network has had a significant impact on the fault detection and classification of traditional overcurrent relays.A spatiotemporal recursive graph neural network model that can effectively detect faults in distributed power grids was constructed through emerging graph learning techniques.The neural network structure can extract spatiotemporal features by detecting bus voltage unit data,and perform fault event detection,fault type classification,fault phase recognition,and fault localization based on the spatiotemporal features of the data.The simulation was performed on an IEEE 123 node system.The results indicate that the proposed fault diagnosis strategy based on voltage measurement has higher accuracy compared to existing traditional schemes.The proposed strategy only needs to extract voltage signals rather than current signals,and is not limited by the number of relays installed,therefore,the proposed strategy is more practical and versatile.
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