A novel approach to optimize the maintenance strategies: A case in the hydroelectric power plant


Özcan E., Yumuşak R., Eren T.

Eksploatacja i Niezawodnosc, cilt.23, sa.2, ss.324-337, 2021 (SCI-Expanded, Scopus) identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 23 Sayı: 2
  • Basım Tarihi: 2021
  • Doi Numarası: 10.17531/ein.2021.2.12
  • Dergi Adı: Eksploatacja i Niezawodnosc
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.324-337
  • Anahtar Kelimeler: AHP, COPRAS, Integer programming, Maintenance management, Maintenance strategy optimization, Multi-criteria decision mak-ing
  • Sivas Cumhuriyet Üniversitesi Adresli: Hayır

Özet

Countries need to develop sustainable energy policies based on the principles of environ-mental sensitivity, reliability, efficiency, economy and uninterrupted service and to maintain their energy supply in order to increase their global competitiveness. In addition to this impact of sustainable energy supply on the global world, maintenance processes in power plants require high costs due to allocated time, materials and labor, and generation loss. Thus, the maintenance needs to be managed within a system. This makes analytical and fea-sible maintenance planning a necessity in power plants. In this context, this study focuses on maintenance strategy optimization which is the first phase of maintenance planning for one of the large-scale hydroelectric power plants with a direct effect on Turkey's energy supply security with its one fifth share in total generation. In this study, a new model is proposed for the maintenance strategy optimization problem considering the multi-objective and multi-criteria structure of hydroelectric power plants with hundreds of complex equipment and the direct effect of these equipment on uninterrupted and cost-effective electricity generation. In the model, two multi-criteria decision-making methods, AHP and COPRAS methods, are integrated with integer programming method and optimal maintenance strategies are obtained for 571 equipment.