GCN-LSTM Based Transient Angle Stability Assessment Method for Future Power Systems Considering Spatial-temporal Disturbance Response Characteristics

Keyphrases

• AC-DC 25%
• Adjacency Matrix 50%
• Assessment Method 100%
• Cross-entropy Loss Function 25%
• Data-driven Methods 25%
• Disturbance Characteristics 50%
• Disturbance Response 100%
• Dynamic Change 25%
• Feature Network 25%
• Feature Quantity 50%
• Fully Connected Network 25%
• Future Power Systems 100%
• GCN-LSTM 100%
• Graph Convolutional Network 25%
• High Recall 25%
• High Renewable Penetration 25%
• Long Short-term Memory Network 25%
• LSTM Network 25%
• Network Topology 75%
• Normalized Features 25%
• Power Angle 25%
• Power Electronics 25%
• Power System 50%
• Recall Rate 25%
• Renewable Energy 25%
• Response Characteristics 100%
• Short Training 25%
• Softmax Activation 25%
• Solar Energy 25%
• Solar Power 25%
• Spatial Characteristics 25%
• Spatial Features 50%
• Spatial-temporal 100%
• Spatio-temporal Features 25%
• Spatiotemporal 50%
• Stability Algorithm 25%
• Stability Analysis 25%
• Stability Evaluation 100%
• State Quantity 25%
• Systems Network 25%
• Temporal Disturbance 100%
• Temporal Features 50%
• Testing Time 25%
• Training Time 25%
• Transient Angle Stability 100%
• Transient Power 25%
• Transient Power Angle Stability 100%
• Wind Energy 25%

Engineering

• Adjacency Matrix 66%
• Assessment Method 100%
• Disturbance Response 100%
• Future Power System 100%
• Input Feature 33%
• Loss Function 33%
• Lstm 100%
• Network Topology 100%
• Power Electronics 33%
• Power Engineering 66%
• Renewable Energy Source 33%
• Response Characteristic 100%
• Simulation Result 33%
• Solar Energy 66%
• Spatial Characteristic 33%
• Spatial Feature 66%
• Stability Analysis 33%
• Testing Time 33%