Yes, you can use MATLAB to integrate “MATPOWER” with machine learning techniques. Here's a high-level approach to combine “MATPOWER” with machine learning:
- Data loading
“MATPOWER” provides data structures for power system models, such as case9, case30, etc. You can load these case files into MATLAB and extract the data you need. Here’s a pseudo code depicting how can you use a data structure for your reference
% Load MATPOWER case
mpc = loadcase('case9');
% Extract relevant data (e.g., bus data, generator data)
bus_data = mpc.bus;
gen_data = mpc.gen;
2. Feature Extraction
For machine learning, you'll typically need to extract features from the MATPOWER data and prepare it for training.
This process usually involves feature engineering and normalization as explained below:
- Feature Engineering: Creating features that represent different aspects of the power system.
- Normalization/Scaling: Normalize or scale features to make them suitable for machine learning models.
% Pseudo code for feature extraction (customize as needed)
features = [bus_data(:, 2), gen_data(:, 2)]; % Example feature extraction
labels = ...; % Define your target labels based on the problem
3. Select and train a ML model
You can choose a ML model based on your specific problem (e.g., regression, classification). MATLAB provides support for various models like SVM, decision trees, neural networks, etc.
% Example using a decision tree
model = fitctree(features, labels);
For more information on “fitchtree” function, you can refer here: https://www.mathworks.com/help/stats/fitctree.html
4. Model evaluation
After training the model, you must evaluate its performance and use it for predictions or analysis.
% Make predictions
predictions = predict(model, features);
% Evaluate model performance
accuracy = sum(predictions == labels) / length(labels);
disp(['Model Accuracy: ', num2str(accuracy)]);
5. Integration with MATPOWER
You can also use the trained model to make decisions or predictions within MATPOWER's simulation environment. For example, you might predict future power demand or system states and use this information to adjust your power system analysis.
You can refer to following file exchange for more information on using “MATPOWER”,
Hope this helps!
