Help with Master's Thesis – AI Predictive Maintenance for Distribution Transformers (MATLAB / Power Systems / ML)
Budget: $250 – $750 AUD
I am looking for an experienced freelancer with knowledge in:
Power systems
Transformer fault analysis
Machine learning / AI
MATLAB or PowerWorld simulation
The goal is academic guidance and technical support for my research work.
Current Stage of Research
I have already prepared:
Thesis proposal
Initial literature review
Research scope and methodology
For the next supervisor meeting, I need help refining the following research components.
Tasks Required
1. Updated Literature Review
Assist in expanding the literature review using recent research papers (last 3–5 years) related to:
Transformer fault detection
AI-based predictive maintenance
Machine learning for power system asset management
Sources may include:
IEEE
Elsevier
Google Scholar
Springer
2. Dataset Collection
Help identify suitable datasets used in previous research for transformer fault prediction.
Possible sources include:
Open-access datasets
IEEE Dataport
Public research datasets
Datasets used in previous academic papers
If necessary, synthetic data generation approaches used in research papers can also be considered.
3. Transformer Fault Simulation
Assist in identifying electrical faults in distribution transformers, such as:
winding faults
insulation failure
overheating
short circuit faults
partial discharge
Then help simulate these faults to generate data for predictive maintenance research.
Possible tools:
MATLAB
MATLAB Simulink
PowerWorld
other power system simulation tools
4. AI Model Preparation
Assist with setting up a baseline predictive maintenance model using:
Machine Learning techniques (SVM, Random Forest, etc.)
Evaluation metrics such as accuracy, precision, recall
This will be used as the baseline model for the research.
5. Research Novelty
The freelancer should also help identify potential novelty or improvement opportunities, such as:
combining synthetic and real datasets
improving model accuracy
integrating predictive maintenance with maintenance scheduling
comparing classical ML and deep learning approaches
Deliverables
For the next meeting with my supervisor, I need:
Updated literature review suggestions
Identification of datasets to use
Plan for transformer fault simulation
Outline of AI modelling approach
Power systems
Transformer fault analysis
Machine learning / AI
MATLAB or PowerWorld simulation
The goal is academic guidance and technical support for my research work.
Current Stage of Research
I have already prepared:
Thesis proposal
Initial literature review
Research scope and methodology
For the next supervisor meeting, I need help refining the following research components.
Tasks Required
1. Updated Literature Review
Assist in expanding the literature review using recent research papers (last 3–5 years) related to:
Transformer fault detection
AI-based predictive maintenance
Machine learning for power system asset management
Sources may include:
IEEE
Elsevier
Google Scholar
Springer
2. Dataset Collection
Help identify suitable datasets used in previous research for transformer fault prediction.
Possible sources include:
Open-access datasets
IEEE Dataport
Public research datasets
Datasets used in previous academic papers
If necessary, synthetic data generation approaches used in research papers can also be considered.
3. Transformer Fault Simulation
Assist in identifying electrical faults in distribution transformers, such as:
winding faults
insulation failure
overheating
short circuit faults
partial discharge
Then help simulate these faults to generate data for predictive maintenance research.
Possible tools:
MATLAB
MATLAB Simulink
PowerWorld
other power system simulation tools
4. AI Model Preparation
Assist with setting up a baseline predictive maintenance model using:
Machine Learning techniques (SVM, Random Forest, etc.)
Evaluation metrics such as accuracy, precision, recall
This will be used as the baseline model for the research.
5. Research Novelty
The freelancer should also help identify potential novelty or improvement opportunities, such as:
combining synthetic and real datasets
improving model accuracy
integrating predictive maintenance with maintenance scheduling
comparing classical ML and deep learning approaches
Deliverables
For the next meeting with my supervisor, I need:
Updated literature review suggestions
Identification of datasets to use
Plan for transformer fault simulation
Outline of AI modelling approach