An Integrated Transfer Learning Method for Power System Dynamic Security Assessment of Unlearned Faults With Missing Data
Chao Ren, Yan Xu, Bijian Dai, Rui Zhang
Abstract
This letter proposes an integrated transfer learning (TL) method for pre-fault dynamic security assessment (DSA) of power systems, which aims to simultaneously achieve fault transfer and address missing data issue for unlearned faults. Moreover, this letter provides the tight mathematic proof for the guaranteed DSA performance of the proposed integrated TL method. Comprehensive simulation results on the benchmark testing system have shown that the proposed integrated TL method can achieve a high DSA accuracy for unknown faults with complete data and can also maintain a satisfactory DSA accuracy for unknown faults even with incomplete data inputs.
Topics & Concepts
Benchmark (surveying)Computer scienceFault (geology)Electric power systemTransfer (computing)Missing dataTransfer of learningPower (physics)Maximum power transfer theoremReliability engineeringData miningArtificial intelligenceEngineeringMachine learningSeismologyQuantum mechanicsGeodesyGeologyPhysicsParallel computingGeographyMachine Learning and ELMPower System Reliability and MaintenanceDomain Adaptation and Few-Shot Learning