MATLAB Developer for PMU-Based Algorithm - 05/01/2026 22:51 EST
Budget: ₹1,500 – ₹12,500 INR
Project: MATLAB Implementation for PMU-Based Fault Detection
I am seeking an experienced MATLAB developer for end-to-end implementation of a PMU-based fault detection system using a provided CSV dataset.
Scope of Work:
Load and preprocess PMU dataset (Voltage, Current, Angles, Frequency)
Exclude non-physical identifiers (e.g., Bus ID)
Implement sliding-window statistical feature extraction
(mean, variance, skewness, kurtosis)
Train and evaluate ML models in MATLAB:
SVM (Linear), Random Forest, Decision Tree, k-NN, Logistic Regression
Compare models using accuracy, confusion matrix, ROC/AUC
Implement real-time fault detection simulation using streaming CSV data
Generate fault alerts and maintain prediction logs
Timeline:
Total duration: 15 days
Days 1–5: Data preprocessing & feature extraction
Days 6–10: Model training, evaluation & comparison
Days 11–15: Real-time simulation, testing & code cleanup
Requirements:
Strong MATLAB + Machine Learning experience
Clean, well-commented, reproducible code
Dataset will be provided.
I am seeking an experienced MATLAB developer for end-to-end implementation of a PMU-based fault detection system using a provided CSV dataset.
Scope of Work:
Load and preprocess PMU dataset (Voltage, Current, Angles, Frequency)
Exclude non-physical identifiers (e.g., Bus ID)
Implement sliding-window statistical feature extraction
(mean, variance, skewness, kurtosis)
Train and evaluate ML models in MATLAB:
SVM (Linear), Random Forest, Decision Tree, k-NN, Logistic Regression
Compare models using accuracy, confusion matrix, ROC/AUC
Implement real-time fault detection simulation using streaming CSV data
Generate fault alerts and maintain prediction logs
Timeline:
Total duration: 15 days
Days 1–5: Data preprocessing & feature extraction
Days 6–10: Model training, evaluation & comparison
Days 11–15: Real-time simulation, testing & code cleanup
Requirements:
Strong MATLAB + Machine Learning experience
Clean, well-commented, reproducible code
Dataset will be provided.