Hidroelektrik santrallarda bakım çizelgeleme için hibrid bir model önerisi

Yıl 2020, , 1815 - 1828, 21.07.2020

Evrencan Özcan , Tuğba Danışan Tamer Eren

https://doi.org/10.17341/gazimmfd.602774

Cited By: 5

Öz

Bakım,
endüstriyel işletmelerde üretim, personel ve malzeme ile eş zamanlı yönetilmesi
gereken önemli bir prosestir. Bu önemli prosesin kritik aşamalarının başında bakım
planlaması gelmektedir. Bakım planlaması için gerekli olan iki aşama
bulunmaktadır. İlk aşama bakım stratejilerinin belirlenmesi ve ikinci aşama ise
bakım çizelgelerinin oluşturulmasıdır. Bu çalışma, bakım planlamasının ilk
adımını oluşturan bakım strateji seçiminin gerçekleştirildiği bir çalışmanın
devamı olarak bakım çizelgelemesi için yapılmıştır. Bakım strateji seçimi için
gerçekleştirilen ilk adımdaki çalışmada, Türkiye’deki büyük ölçekli bir
hidroelektrik santralda yer alan 1330 elektriksel ekipman incelenmiş ve santral
açısından kritiklik seviyesi belirlenmiştir. Analitik Hiyerarşi Prosesi (AHP), Technique
for Order Preference by Similarity to Ideal Solution (TOPSIS) ve Tam Sayılı
Programlama (TP) yöntemleri kullanılmıştır. Çalışma sonucunda bir zaman
çizelgesi doğrultusunda gerçekleştirilebilecek olan, periyodik bakım
stratejisinin uygulanabileceği kritik elektriksel 7 ana ekipman grubu
belirlenmiştir. Elde edilen bu sonuçlar bakım planlamasındaki bakım çizelgeleme
aşamasını oluşturan bu çalışmada kullanılmıştır ve periyodik bakım
stratejisinin uygulanabileceği bu kritik elektriksel 7 ana ekipman grubu için
bakım çizelgesi oluşturulmuştur. Bakım çizelgeleme için yapılan bu çalışmanın ilk
aşamasında santralın bir yıllık üretim tahmini Yapay Sinir Ağı (YSA) yöntemi
ile gerçekleştirilmiş ve bu tahmin sonucunda elde edilen verilerden
çalışma-bakım süreleri hesaplanmıştır. Hesaplanan bu süreler TP modeline dahil
edilerek beş farklı periyodik bakım türü çizelgelenmiştir. 

Anahtar Kelimeler

Bakım çizelgeleme, üretim tahmini, hidroelektrik santral, tam sayılı programlama, yapay sinir ağı

Destekleyen Kurum

Kırıkkale Üniversitesi Bilimsel Araştırma Projeleri Koordinasyon Birimi

Proje Numarası

2018/008

Teşekkür

Bu çalışma Kırıkkale Üniversitesi Bilimsel Araştırma Projeleri Koordinasyon Birimi (BAP) tarafından 2018/008 numaralı proje ile desteklenmiştir. Desteklerinden dolayı BAP Birimine teşekkürlerimizi sunarız.

Kaynakça

• [1] TEİAŞ. Kurulu Güç Raporları Haziran 2019. https://www.teias.gov.tr/sites/default/files/2019-07/KURULU%20G%C3%9C%C3%87%20%C4%B0NTERNET%20HAZ%C4%B0RAN%20AYI_0.pdfErişim tarihi Ağustos 3, 2019.
• [2] Özcan E.C., Bakım Yönetim Sistemi: Kurulum ve İşletme Esasları, Elektrik Üretim A.Ş. Yayınları, Ankara, Türkiye, 2016.
• [3] Özcan E.C., Yumuşak R., Eren T., Risk based maintenance in the hydroelectric power plants, Energies, 12(8), 1502, 2019.
• [4] Özcan E.C., Ünlüsoy S., Eren T., A combined goal programming–AHP approach supported with TOPSIS for maintenance strategy selection in hydroelectric power plants, Renewable Sustainable Energy Rev., 78, 1410-1423, 2017.
• [5] Özcan E.C., Danışan T., Eren T., Hidroelektrik santralların en kritik elektriksel ekipman gruplarının bakım stratejilerinin optimizasyonu için matematiksel bir model önerisi, Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi, basımda, 2019.
• [6] White H., Learning in artificial neural networks: A statistical perspective, Neural Comput., 1(4), 425-464, 1989.
• [7] Ripley B.D., Statistical aspects of neural networks, Networks and chaos statistical and probabilistic aspects, 50, 40-123, 1993.
• [8] Cheng B., Titterington D.M., Neural networks: A review from a statistical perspective, Statistical Science, 2-30, 1994.
• [9] Liao S.H., Wen C.H., Artificial neural networks classification and clustering of methodologies and applications–literature analysis from 1995 to 2005, Expert Syst. Appl., 32(1), 1-11, 2007.
• [10] Al-Shayea Q.K., Artificial neural networks in medical diagnosis, International Journal of Computer Science Issues, 8(2), 150-154, 2011.
• [11] Yuce B., Rezgui Y., Mourshed M., ANN–GA smart appliance scheduling for optimised energy management in the domestic sector, Energy Build., 111, 311-325, 2016.
• [12] Voyant C., Notton G., Kalogirou S., Nivet M.L., Paoli C., Motte F., Fouilloy A., Machine learning methods for solar radiation forecasting: A reviews, Renewable Energy, 105, 569-582, 2017.
• [13] Notton G., Voyant C., Fouilloy A., Duchaud J.L., Nivet M.L., Some applications of ANN to solar radiation estimation and forecasting for energy applications, Applied Sciences, 9(1), 209, 2019.
• [14] Dougherty M., A review of neural networks applied to transport, Transportation Research Part C: Emerging Technologies, 3(4), 247-260, 1995.
• [15] Shankaracharya D.O., Samanta S., Vidyarthi A.S., Computational intelligence in early diabetes diagnosis: a reviews, The review of diabetic studies: RDS, 7(4), 252, 2010.
• [16] Wong B.K., Selvi Y., Neural network applications in finance: A review and analysis of literature (1990–1996), Information & Management, 34(3), 129-139, 1998.
• [17] Kalogirou S.A., Artificial neural networks in renewable energy systems applications: A reviews, Renewable Sustainable Energy Rev., 5(4), 373-401, 2001.
• [18] Amasyali K., El-Gohary N.M., A review of data-driven building energy consumption prediction studies, Renewable Sustainable Energy Rev., 81, 1192-1205, 2018.
• [19] Muralitharan K., Sakthivel R., Vishnuvarthan R., Neural network based optimization approach for energy demand prediction in smart grid. Neurocomputing, 273, 199-208, 2018.
• [20] Suganthi L., Samuel A.A., Energy models for demand forecasting—A reviews, Renewable Sustainable Energy Rev., 16(2), 1223-1240, 2012.
• [21] Weron R., Electricity price forecasting: A review of the state-of-the-art with a look into the future, International Journal of Forecasting, 30(4), 1030-1081, 2014.
• [22] Wang Z., Srinivasan R.S., A review of artificial intelligence based building energy use prediction: Contrasting the capabilities of single and ensemble prediction models, Renewable Sustainable Energy Rev., 75, 796-808, 2017.
• [23] Gandelli A., Grimaccia F., Leva S., Mussetta M., Ogliari, E., Hybrid model analysis and validation for PV energy production forecasting, In 2014 International Joint Conference on Neural Networks (IJCNN), IEEE, 1957-1962, July, 2014.
• [24] Li Z., Rahman S.M., Vega R., Dong B., A hierarchical approach using machine learning methods in solar photovoltaic energy production forecasting, Energies, 9(1), 55, 2016.
• [25] Dolara A., Grimaccia F., Leva S., Mussetta M., Ogliari E., A physical hybrid artificial neural network for short term forecasting of PV plant power output, Energies, 8(2), 1138-1153, 2015.
• [26] Yamayee Z.A., Maintenance scheduling: description, literature survey, and interface with overall operations scheduling, IEEE Transactions on Power Apparatus and Systems, (8), 2770-2779, 1982.
• [27] Khalid A., Ioannis K., A survey of generator maintenance scheduling techniques, Global Journal of Researches in Engineering, 12(1), 10-17, 2012.
• [28] Froger A., Gendreau M., Mendoza J.E., Pinson É., Rousseau L.M., Maintenance scheduling in the electricity industry: A reviews. Eur. J. Oper. Res., 251(3), 695-706, 2016.
• [29] Huang S.J., Generator maintenance scheduling: a fuzzy system approach with genetic enhancement, Electr. Power Syst. Res., 41(3), 233-239, 1997.
• [30] Frost D., Dechter R., Optimizing with constraints: a case study in scheduling maintenance of electric power units, Lect. Notes Comput. Sci., 469-469, 1998.
• [31] Chattopadhyay D., A practical maintenance scheduling program mathematical model and case study, IEEE Trans. Power Syst., 13(4), 1475-1480, 1998.
• [32] Anghinolfi D., Gambardella L.M., Montemanni R., Nattero C., Paolucci M., Toklu N.E., A matheuristic algorithm for a large-scale energy management problem, In International Conference on Large-Scale Scientific Computing, Springer, Berlin, Heidelberg. 173-181, June, 2011.
• [33] Lindner B.G., Brits R., Van Vuuren J.H., Bekker J., Tradeoffs between levelling the reserve margin and minimising production cost in generator maintenance scheduling for regulated power systems, Int. J. Electr. Power Energy Syst., 101, 458-471, 2018.
• [34] Dopazo J.F., Merrill H.M., Optimal generator maintenance scheduling using integer programming, IEEE Transactions on Power Apparatus and Systems, 94(5), 1537-1545, 1975.
• [35] Jost V., Savourey D., A 0–1 integer linear programming approach to schedule outages of nuclear power plants, Journal of Scheduling, 16(6), 551-566, 2013.
• [36] Al-Khamis T.M., Vemuri S., Lemonidis L., Yellen J., Unit maintenance scheduling with fuel constraints, In Power Industry Computer Application Conference, IEEE, Conference Proceedings 113-119, May, 1991.
• [37] Dahal K.P., Aldridge C.J., McDonald J.R., Generator maintenance scheduling using a genetic algorithm with a fuzzy evaluation function, Fuzzy Sets Syst., 102(1), 21-29, 1999.
• [38] Dahal K.P., Chakpitak N., Generator maintenance scheduling in power systems using metaheuristic-based hybrid approaches, Electr. Power Syst. Res., 77(7), 771-779, 2007.
• [39] Fetanat A., Shafipour G., Generation maintenance scheduling in power systems using ant colony optimization for continuous domains based 0–1 integer programming, Expert Syst. Appl., 38(8), 9729-9735, 2011.
• [40] Fourcade F., Johnson E., Bara M., Cortey-Dumont P., Optimizing nuclear power plant refueling with mixed-integer programming, Eur. J. Oper. Res., 97(2), 269-280, 1997.
• [41] Canto S.P., Application of Benders’ decomposition to power plant preventive maintenance scheduling, Eur. J. Oper. Res., 184(2), 759-777, 2008.
• [42] Mollahassani-Pour M., Abdollahi A., Rashidinejad M., Application of a novel cost reduction index to preventive maintenance scheduling, Int. J. Electr. Power Energy Syst., 56, 235-240, 2014.
• [43] Wang C., Wang Z., Short-term transmission line maintenance scheduling with wind energy integration, In Power & Energy Society General Meeting, IEEE, 1-5, July, 2017.
• [44] Eygelaar J., Lötter D.P., van Vuuren J.H., Generator maintenance scheduling based on the risk of power generating unit failure, Int. J. Electr. Power Energy Syst. , 95, 83-95, 2018.
• [45] Mazidi P., Tohidi Y., Ramos A., Sanz-Bobi M.A., Profit-maximization generation maintenance scheduling through bi-level programming, Eur. J. Oper. Res., 264(3), 1045-1057, 2018.
• [46] Behnia H., Akhbari M., Generation and transmission equipment maintenance scheduling by transmission switching and phase shifting transformer, International Journal of Numerical Modelling: Electronic Networks, Devices and Fields, 32(1), 2483, 2019.
• [47] Lv C., Wang J., You S., Zhang Z., Short‐term transmission maintenance scheduling based on the Benders decomposition, Int. Trans. Electr. Energy Syst., 25(4), 697-712, 2015.
• [48] Abirami M., Ganesan S., Subramanian S., Anandhakumar R., Source and transmission line maintenance outage scheduling in a power system using teaching learning based optimization algorithm, Appl. Soft Comput., 21, 72-83, 2014.
• [49] Baskar S., Subbaraj P., Rao M.V.C., Tamilselvi S., Genetic algorithms solution to generator maintenance scheduling with modified genetic operators, IEE Proceedings-Generation, Transmission and Distribution, 150(1), 56-60, 2003.
• [50] Yare Y., Venayagamoorthy G.K., Optimal maintenance scheduling of generators using multiple swarms-MDPSO framework, Eng. Appl. Artif. Intell., 23(6), 895-910, 2010.
• [51] Reihani E., Sarikhani A., Davodi M., Davodi M., Reliability based generator maintenance scheduling using hybrid evolutionary approach, Int. J. Electr. Power Energy Syst., 42(1), 434-439, 2012.
• [52] Zhong S., Pantelous A.A., Beer M., Zhou J., Constrained non-linear multi-objective optimisation of preventive maintenance scheduling for offshore wind farms, Mech. Syst. Sig. Process., 104, 347-369, 2018.
• [53] Zhu J., Yan W., Lin Y., Yu P., Xiong X., A new multi-objective immune algorithm for generation and transmission equipment maintenance scheduling, Int. J. Power Energy Syst., 37(3), 2017.
• [54] Zhu J., Xuan P., Xie P., Hong C., Yan W., Generation and transmission equipment maintenance scheduling with load transfer, In Power & Energy Society General Meeting, IEEE, 1-5, July, 2017.
• [55] El-Amin I., Duffuaa S., Abbas M., A tabu search algorithm for maintenance scheduling of generating units, Electr. Power Syst. Res., 54(2), 91-99, 2000.
• [56] Foong W.K., Simpson A.R., Maier H.R., Stolp S., Ant colony optimization for power plant maintenance scheduling optimization—a five-station hydropower system, Annals of Operations Research, 159(1), 433-450, 2008.
• [57] Suresh K., Kumarappan N., Hybrid improved binary particle swarm optimization approach for generation maintenance scheduling problem, Swarm Evol. Comput., 9, 69-89, 2013.
• [58] Burke E.K., Smith, A.J., Hybrid evolutionary techniques for the maintenance scheduling problem, IEEE Trans. Power Syst., 15(1), 122-128, 2000.
• [59] Gardi F., Nouioua K., Local search for mixed-integer nonlinear optimization: a methodology and an application, In European Conference on Evolutionary Computation in Combinatorial Optimization, Springer, Berlin, Heidelberg, 167-178, April, 2011.
• [60] Min C.G., Kim M.K., Park J.K., Yoon Y.T., Game-theory-based generation maintenance scheduling in electricity markets, Energy, 55, 310-318, 2013.
• [61] El-Sharkh M.Y., El-Keib A.A., Chen H., A fuzzy evolutionary programming-based solution methodology for security-constrained generation maintenance scheduling, Electr. Power Syst. Res., 67(1), 67-72, 2003.
• [62] Bisanovic S., Hajro M., Dlakic M., A profit-based maintenance scheduling of thermal power units in electricity market, International Journal of Electrical and Electronics Engineering, 5(3), 156-164, 2011.
• [63] Kralj B., Petrovic R., A multiobjective optimization approach to thermal generating units maintenance scheduling, Eur. J. Oper. Res., 84(2), 481-493, 1995.
• [64] Mazidi P., Tohidi Y., Sanz-Bobi M.A., Strategic maintenance scheduling of an offshore wind farm in a deregulated power system, Energies, 10(3), 313, 2017.
• [65] Lei X., Sandborn P.A., Maintenance scheduling based on remaining useful life predictions for wind farms managed using power purchase agreements, Renewable Energy, 116, 188-198, 2018.
• [66] Bangalore P., Patriksson M., Analysis of SCADA data for early fault detection, with application to the maintenance management of wind turbines, Renewable Energy, 115, 521-532, 2018.
• [67] Kovacs A., Erdős G., Viharos Z.J., Monostori L., A system for the detailed scheduling of wind farm maintenance, CIRP Annals-Manufacturing Technology, 60(1), 497-501, 2011.
• [68] Khemmoudj M.O.I., Porcheron M., Bennaceur H., When constraint programming and local search solve the scheduling problem of electricité de france nuclear power plant outages, In International Conference on Principles and Practice of Constraint Programming, Springer, Berlin, Heidelberg, 271-283, September, 2006.
• [69] Gorge A., Lisser A., Zorgati R., Stochastic nuclear outages semidefinite relaxations, Computational Management Science, 9(3), 363-379, 2012.
• [70] Helseth A., Fodstad M., Mo B., Optimal hydropower maintenance scheduling in liberalized markets, IEEE Trans. Power Syst., 33(6), 6989-6998, 2018.
• [71] Rodriguez J.A., Anjos M.F., Côté P., Desaulniers G., Milp formulations for generator maintenance scheduling in hydropower systems, IEEE Trans. Power Syst., 33(6), 6171-6180, 2018.
• [72] Chattopadhyay D., A game theoretic model for strategic maintenance and dispatch decisions, IEEE Trans. Power Syst., 19(4), 2014-2021, 2004.
• [73] Barot H., Bhattacharya K., Security coordinated maintenance scheduling in deregulation based on genco contribution to unserved energy, IEEE Trans. Power Syst., 23(4), 1871-1882, 2008.
• [74] Lindner B.G., Brits R., van Vuuren J.H., Bekker J., Tradeoffs between levelling the reserve margin and minimising production cost in generator maintenance scheduling for regulated power systems, Int. J. Electr. Power Energy Syst., 101, 458-471, 2018.
• [75] Naebi Toutounchi A., Seyed Shenava S.J., Taheri S.S., Shayeghi H., MPEC approach for solving preventive maintenance scheduling of power units in a market environment, Trans. Inst. Meas. Control, 40(2), 436-445, 2018.
• [76] McCulloch W.S., Pitts W., A logical calculus of the ideas immanent in nervous activity, The Bulletin of Mathematical Biophysics, 5(4), 115-133, 1943.
• [77] Hebb D.O., The organization of behavior, 1949.
• [78] Rosenblatt F., The perceptron: a probabilistic model for information storage and organization in the brain, Psychological Review, 65(6), 386, 1958.
• [79] Rumelhart D.E., Hinton G.E., Williams R.J., Learning internal representation by backpropagating errors, DE Rumelhart, & JL McCleland Parallel Distributed Processing: Explorations in the Microstructure of Cognition, 1, 1986.
• [80] Haykin S., Neural networks and learning machines, Upper Saddle River: Pearson education,3, 2009.
• [81] Haykin S., Neural networks: a comprehensive foundation, Prentice Hall PTR, 1994.
• [82] Jünger M., Liebling T.M., Naddef D., Nemhauser G.L., Pulleyblank W.R., Reinelt G., Wolsey L.A., 50 years of integer programming 1958-2008: from the early years to the state-of-the-art, Springer Science & Business Media, 2009.
• [83] Taha H.A., Integer programming: theory, applications and computations, Academic Press, 2014.