To develop a sophisticated approach integrating both Multilayer Feedforward Neural Networks (MLFN) and Radial Basis Function Neural Networks (RBFN) for Dynamic Stability Assessment (DSA), incorporating feature reduction techniques like Fisher Discrimination and Divergence, here's a comprehensive framework that can be implemented in MATLAB.
Approach Overview:
Dataset Preparation:
Collect time-domain simulation data for the GSO 37-bus system under different fault conditions.
Label the dataset with stability conditions (stable or unstable).
Feature Reduction:
Apply Fisher Discrimination and Divergence to identify the most important features from your dataset.
Neural Networks Integration:
Design both MLFN and RBFN to predict the stability of the power system based on the reduced set of features.
Compare their performance based on accuracy, speed, and generalization ability.
Model Evaluation:
Use performance metrics like confusion matrices, accuracy, precision, recall, and F1-scores to compare MLFN and RBFN.
Tune the networks based on results.
Detailed Steps:
Step 1: Simulate the GSO 37-Bus Power System in MATLAB
You need to perform time-domain simulations for the GSO 37-bus system under different fault conditions:
For this step, you can use MATLAB's Simulink and SimPowerSystems to simulate the power system and export the simulation results as a dataset.
Step 2: Feature Reduction Using Fisher Discrimination and Divergence
After generating the simulation data, apply Fisher Discrimination and Divergence to reduce the feature set.
Fisher Discrimination: Separates classes (stable vs unstable) by maximizing the between-class variance and minimizing the within-class variance.
Divergence: Measures the separation between probability distributions for different classes.
Step 3: (MLFN)
Step 4: (RBFN)
Step 5: Comparing MLFN and RBFN
Performance Metrics: Compare the two models based on:
Accuracy: Percentage of correct predictions.
Precision and Recall: Evaluates model's performance in predicting stable and unstable conditions.
F1-Score: A balance of precision and recall.
Confusion Matrix: Helps visualize false positives and false negatives.
Step 6: Fine-Tuning the Networks
MLFN Fine-Tuning:
Adjust the number of hidden layers and neurons.
Experiment with different training algorithms (trainlm, trainbr).
Apply regularization techniques like dropout to avoid overfitting.
RBFN Fine-Tuning:
Adjust the spread parameter to control the width of the Gaussian functions.
Experiment with the error goal to balance speed and accuracy.
Step 7: Evaluation of Results
Training Time: Evaluate the time taken to train each network.
Generalization Ability: Check how well the models perform on unseen data (test dataset).
Model Complexity: Compare the complexity of the two models and their computational requirements.
Accuracy vs. Speed Tradeoff: RBFN may be faster to train, but MLFN could achieve higher accuracy, especially for more complex, non-linear relationships in the data.