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An artificial neural network model supported with multi criteria decision making approaches for maintenance planning in hydroelectric power plants

Treść / Zawartość
Identyfikatory
Warianty tytułu
PL
Planowanie utrzymania ruchu w elektrowniach wodnych w oparciu o model sztucznej sieci neuronowej wsparty wielokryterialnymi metodami podejmowania decyzji
Języki publikacji
EN
Abstrakty
EN
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.
PL
Elektrownie to zakłady produkcyjne o dużej skali, których głównym celem jest nieprzerwane, niezawodne, wydajne, rentowne oraz przyjazne dla środowiska wytwarzanie energii. Utrzymanie ruchu stanowi jeden z kluczowych czynników pozwalających na osiągnięcie tych szeroko zakrojonych celów, które określa się wspólnym mianem zrównoważonych dostaw energii. W elektrowniach, procesami utrzymania ruchu, realizowanymi w celu zapewnienia zrównoważonych dostaw energii, zarządza się z uwzględnieniem kosztów związanych z wymogami czasowymi, kosztów materiałów i robocizny oraz strat wytwarzania energii. Ponieważ elektrownie wykorzystują tysiące różnych urządzeń, niezwykle ważne jest prognozowanie dat wystąpienia uszkodzeń oraz zapewnienie bezawaryjnego utrzymania ruchu. W przedstawionych badaniach, rozważano problem planowania utrzymania ruchu sprzętu o wysokim poziomie krytyczności na przykładzie jednej z dużych elektrowni wodnych, która na koniec 2018 r. pokrywała jedną piątą zapotrzebowania Turcji na energię elektryczną. W pierwszym etapie badań, kryteria oceny określone przez ekspertów zatrudnionych w elektrowni ważono za pomocą powszechnie stosowanej w literaturze metody procesu hierarchii analitycznej (AHP) w celu ustalenia poziomów krytyczności poszczególnych elementów wyposażenia elektrowni. Aby opracować ostateczny ranking priorytetowości elementów wyposażenia elektrowni na podstawie określonych wcześniej wag, zastosowano technikę TOPSIS, która polega na porządkowaniu preferencji na podstawie podobieństwa do idealnego rozwiązania. Techniki tej użyto ze względu na jej zalety, których nie mają inne algorytmy oparte na relacji przewyższania (ang. outranking algorithms). Na podstawie wyników otrzymanych dla 14 głównych grup urządzeń o najwyższym poziomie krytyczności, określonym na podstawie danych pochodzących z elektrowni, oszacowano czasy pomiędzy dwiema awariami, a na ich podstawie zaplanowano działania konserwacyjne. W fazie szacowania, opracowano model sztucznej sieci neuronowej (ANN) w oparciu o dane o uszkodzeniach, które wystąpiły w ostatnich 11 latach działania elektrowni, dla wybranych grup urządzeń. Przewidywane daty wystąpienia uszkodzeń szacowano, po raz pierwszy w literaturze, biorąc pod uwagę zakład produkcyjny jako system, bez uwzględnienia sektora produkcyjnego. Plan obejmuje działania konserwacyjne, które mają być przeprowadzone przed przewidywanymi datami awarii, w celu zwiększenia wydajności wytwarzania energii, przedłużenia żywotności elektrowni, minimalizacji kosztów wytwarzania energii, maksymalizacji wskaźnika dostępności elektrowni oraz maksymalizacji zysków. Opracowany plan konserwacji wdrażano w omawianej elektrowni przez 2 lata. W tym okresie nie odnotowano przerw w pracy jednostek wytwórczych spowodowanych awarią rozważanych grup urządzeń, co oznacza, że wspomniane cele zostały osiągnięte.
Rocznik
Strony
400--418
Opis fizyczny
Bibliogr. 92 poz., rys., tab.
Twórcy
  • Industrial Engineering Department Kırıkkale University 71451, Kırıkkale, Turkey
  • Industrial Engineering Department Kırıkkale University 71451, Kırıkkale, Turkey
  • Industrial Engineering Department Kırıkkale University 71451, Kırıkkale, Turkey
autor
  • Industrial Engineering Department Kırıkkale University 71451, Kırıkkale, Turkey
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Bibliografia
Identyfikator YADDA
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