RESEARCH PAPER
An artificial neural network model supported with multi criteria decision making approaches for maintenance planning in hydroelectric power plants
1
Industrial Engineering Department Kırıkkale University 71451, Kırıkkale, Turkey
Publication date: 2020-09-30
Eksploatacja i Niezawodność – Maintenance and Reliability 2020;22(3):400-418
KEYWORDS
Artificial neural networkshydroelectric power plantsfailure period estimationmaintenance planningAHPTOPSIS
ABSTRACT
Power plants are the large-scale production facilities with the main purpose of realizing uninterrupted, reliable, efficient, economic and environmentally friendly energy generation. Maintenance is one of the critical factors in achieving these comprehensive
goals, which are called as sustainable energy supply. The maintenance processes carried out in order to ensure sustainable energy
supply in the power plants should be managed due to the costs arising from time requirement, the use of material and labor, and the
loss of generation. In this respect, it is critical that the fault dates are forecasted, and maintenance is performed without failure in
power plants consisting of thousands of equipment. In this context in this study, the maintenance planning problem for equipment
with high criticality level is handled in one of the large-scale hydroelectric power plants that meet the quintile of Turkey’s energy
demand as of the end of 2018. In the first stage, the evaluation criteria determined by the power plant experts are weighted by the
Analytical Hierarchy Process (AHP), which is an accepted method in the literature, in order to determine the criticality levels of
the equipment in terms of power plant at the next stage. In order to obtain the final priority ranking of the equipment in terms of
power plant within the scope of these weights, Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) is used
because of its advantages compared to other outranking algorithms. As a result of this solution, for the 14 main equipment groups
with the highest criticality level determined on the basis of the power plant, periods between two breakdowns are estimated, and
maintenance planning is performed based on these periods. In the estimation phase, an artificial neural network (ANN) model has
been established by using 11-years fault data for selected equipment groups and the probable fault dates are estimated by considering a production facility as a system without considering the sector for the first time in the literature. With the plan including
the maintenance activities that will be carried out before the determined breakdown dates, increasing the generation efficiency,
extending the economic life of the power plant, minimizing the generation costs, maximizing the plant availability rate and maximizing profit are aimed. The maintenance plan is implemented for 2 years in the power plant and the unit shutdowns resulting from
the selected equipment groups are not met and the mentioned goals are reached.
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| eISSN: | 2956-3860 |
| ISSN: | 1507-2711 |
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