Maintenance optimization for coal power plants: a GSPN-based approach

In this paper, the benefits of modeling the maintenance procedures in the Plomin 2 coal-fired power plant have been analyzed. Renewable electricity power sources have completely changed the paradigm of electricity power markets, where many small power sources, highly dependent on weather conditions, have been connected to the power grid. Under these new circumstances, baseload power plants have been pushed to the edge of profitability. To reduce production costs, the availability and reliability of the plant should be increased. Therefore, to introduce a novel maintenance strategy, an adequate modeling and analysis approach is required. By identifying the key components of the power plant, maintenance actions have been optimized using Generalized Stochastic Petri Nets (GSPN). The model provides a comprehensive representation of the critical subsystem of the power plant and allows a more accurate prediction of future failures, reduced maintenance costs, and unexpected breakdowns. The results obtained in the conducted study indicate that mill availability ranges are between 0.983 and 1, with an average of 17.6 failures per mill, while the mean time between failures (MTBF) is equal to 2752.6 hours. The results have shown that to enhance system reliability, preventive maintenance activities for three critical mill components can be increased for 10%.