MingQQQ / Identification-of-Key-Factors-on-Stability-of-Multi-outfeed-HVDC

Resources and extra documentation for the manuscript "A Global Sensitivity-based Identification of Key Factors on Stability of Power Grid with Multi-outfeed HVDC" published in IEEE Latin America Transactions.

The code for the integration of stability indexes and global sensitivity analysis of fusion index in Multi-outfeed HVDC

Resources and extra documentation for the manuscript "A Global Sensitivity-based Identification of Key Factors on Stability of Power Grid with Multi-outfeed HVDC" published in IEEE Latin America Transactions. The project hierarchy and folders description is as follows

1. MATLAB_EWF_AHP: The effectiveness verification and fusion of stability indexes.
   1. make_plots.m. The actual power grid simulation data in the engineering software PSD-BPA is used to verify the effectiveness of various stability indexes.
   2. EWF_AHP.m. The code for the entropy weight-fuzzy analytic hierarchy process method, after calculating the comprehensive weights, combines multiple indexes in each sample into a single comprehensive index.
2. PYTHON_GSA: Global sensitivity analysis of key operational variables in the system that affect the comprehensive index.
   1. gaussian_process_emulator.py. Methods of training and making predictions using a Gaussian Process emulator.
   2. example_gaussian_process_emulator.py. Example of how to train and test a Gaussian process emulator. Gaussian process is used to capture the correlation between parameterized operational variables and the comprehensive indicator.
   3. sensitivity_analysis_sobol.py. Implementation of Sobol sensitivity analysis using a Gaussian process emulator.
   4. example_sobol.py. Example of how to extract Sobol sensitivities.Sobol method is used to analyze the key variables that influence the comprehensive index.
   5. data.csv. An example data file containing the inputs and ouputs of an agent based model.
3. GraphicalAbstract.png: The figure for the README.md file.