/Spatial-temporal-Adaptive-Transient-Stability-Assessment-for-Power-System-under-Missing-Data

Codes for our paper "Spatial-temporal Adaptive Transient Stability Assessment for Power System under Missing Data"

Primary LanguageMATLAB

Copyright notice: These codes can be used only for reproducing our unpublished paper "Spatial-temporal Adaptive Transient Stability Assessment for Power System under Missing Data" submitted to International Journal of Electrical Power & Energy Systems, and other uses are not allowed!

Spatial-temporal-Adaptive-Transient-Stability-Assessment-for-Power-System-under-Missing-Data

These codes are for our paper "Spatial-temporal Adaptive Transient Stability Assessment for Power System under Missing Data", and the following introduction provides usages for them. As the capacity of Github is limited, we apologize that the datasets cannot be uploaded, but readers can generate them according to data generation method in our paper. Enjoy it!

Environment Requirement

  • Python 3.x
  • Matlab

1.Relief_FT

  • main.m: The main program of feature importance calculation. Its input is the training data, and the output is the importance of each temporal feature.
  • Relief_FT: Program of feature importance calculation, called by main.m.
  • weights.mat: The results of main.m

2.Optimal PMU Clusters Searching Model

  • optimal_featureset.m: The main program of the optimal PMU clusters searching model. Its input is the temporal feature importance, and the output is the optimal PMU clusters.
  • fitness.m: The objective function of the optimal PMU clusters searching model.
  • circlecon.m: The constraints of the optimal PMU clusters searching model.
  • PMU_Place.m: The power system observability description algorithm.
  • 17.csv: The results of the optimal PMU clusters.

3. Spatial-temporal Adaptive TSA

  • Spatial_temporal_adaptive_TSA.py: The main program of spatial-temporal adaptive TSA. Its input is testing data, and the output is the average response time and the average accuracy of TSA under missing data.
  • Ensemble_LSTM.py: This is ensemble LSTM model. Its input is the validation data, and the output is the weight for each LSTM. Then ensemble LSTM model is created by integrating each LSTM with corresponding the weights.
  • DynamicLSTM.py: The training program for single LSTM.