Windfarm Maintenance Optimization
Research Assistant · Ohio State · Research
- Python
- Markov chains
- MCMC
- Cost modeling
20%
modeled maintenance cost reduction
MCMC
maintenance strategy simulation
Time-based
failure-rate modeling
Maintenance as a stochastic system
This project modeled wind turbine maintenance as a Markov process. The goal was to compare maintenance strategies and understand when more advanced strategies could justify their higher investment costs.
What I built
- Built a Markov chain Monte Carlo simulation for turbine maintenance states.
- Added time-dependent failure rates to make the model behave closer to the real aging pattern of equipment.
- Compared traditional maintenance with two more advanced maintenance strategies.
- Ran cost-benefit analysis across the strategies.
Result
The time-dependent maintenance strategy showed a 20% reduction in modeled maintenance cost. The useful part was not only the number, but the way the model made strategy tradeoffs visible.