A hybrid analytical-simulation approach for maintenance optimization of deteriorating equipment: Case study of wind turbines

• The optimum equipment maintenance strategy is studied using a hybrid analytical/simulation approach.
• We develop a Monte Carlo simulation version of a Markov decision processes model.
• The impact of longer maintenance and repair on cost and availability of wind turbines are obtained.
• It will be optimum to hire one repair technician for every 12–16 turbines in a wind farm.
• Hiring a technician at the cost-effective point increases the availability of wind turbine by 1.5%.

As the power system moves toward more efficient operation, one of the main challenges for asset managers is to determine the optimum maintenance strategy for deteriorating equipment such as wind turbines. This problem can be addressed using a variety of optimization methods including analytical approaches which globally find the best strategy. However, since there are numerous factors (e.g., resource availability, weather conditions, etc.) affecting the operation and maintenance of equipment, there is not a unique solution for all of the possible situations. While it will be complicated to include these scenarios in an analytical model, a simulation model is more flexible and can easily handle these conditions. In order to benefit from the advantages of both analytical and simulation models, we propose a hybrid analytical-simulation approach toward solving a maintenance optimization problem with actual system limitations. In the first step of this approach, the optimum maintenance policy for a wind turbine is obtained using semi-Markov decision processes. Then, this model is built and solved with a Monte Carlo simulation, and the results are compared for justification of the simulation model. In the second step, the effects of maintenance and repair constraints on system availability and costs are studied using the simulation model developed. The model developed can assist the asset managers in including their own restrictions through sensitivity analysis and performing a cost/benefit analysis to determine, for example, how many technicians are required for a fleet of equipment, such as a wind farm. The effectiveness of this approach is demonstrated by the results from the case study of wind turbines where a number of maintenance and repair restrictions are considered.