A hybrid machine learning framework for enhancing PMU-based event identification with limited labels

Mathematics

• Hybrid Learning 100%
• Machine Learning 52%
• Supervised Learning 41%
• Semi-supervised Learning 30%
• Active Learning 30%
• Framework 27%
• Interpretability 25%
• Situational Awareness 19%
• Learning 17%
• Event Detection 17%
• Learning Process 13%
• Feature Selection 12%
• Power System 12%
• Simulator 12%
• Simulation 12%
• High Resolution 11%
• Artificial Intelligence 11%
• System Dynamics 11%
• Preparation 10%
• Industry 9%
• Community 9%
• Computational Results 9%
• Signature 9%
• Biased 9%
• Grid 7%
• Generator 7%
• Energy 6%
• Model 6%

Engineering & Materials Science

• Supervised learning 51%
• Labels 41%
• Semi-supervised learning 39%
• Problem-Based Learning 37%
• Machine learning 35%
• Labeled data 18%
• Data Analytics 17%
• Big data 14%
• Artificial intelligence 14%
• Dynamical systems 13%
• Feature extraction 12%
• Simulators 11%
• Industry 7%

Physics & Astronomy

• machine learning 61%
• learning 52%
• situational awareness 8%
• artificial intelligence 7%
• simulators 4%
• installing 4%
• industries 4%
• generators 4%
• grids 3%
• signatures 3%
• simulation 3%
• preparation 3%
• high resolution 2%
• energy 1%