The data-driven TSCP model, while effective in predicting the required transient stability correction factor (TSCF) under varying loading conditions, may have limitations in handling extreme or unforeseen scenarios. To improve its robustness, several measures can be implemented:
Enhanced Training Data: Increasing the diversity and volume of training data by incorporating simulated extreme events and rare contingencies can help the model learn to generalize better.
Ensemble Learning: Implementing ensemble learning techniques by combining predictions from multiple models can improve accuracy and reduce prediction errors.
Continuous Learning: Implementing a continuous learning framework that updates the model with real-time data and feedback from system operations can enhance its adaptability to changing conditions.
Uncertainty Quantification: Incorporating uncertainty quantification methods to assess the confidence levels of TSCF predictions can provide insights into the reliability of the model's outputs.
Model Interpretability: Ensuring the model's interpretability by explaining the rationale behind its predictions can help operators trust and validate its outputs in critical decision-making processes.