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An evolutionary approach with reliability priority to design Scada systems for water reservoirs

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Abstract

In this paper, a new evolutionary method designs and improves the reliability of Supervisory Control and Data Acquisition (SCADA) of reservoir station systems in the water transfer network. The proposed mathematical model uses a reliability Block Diagram (RBD) and redundancy policies. Then a bi-objective non-linear mathematical RAP model considering cost and reliability optimizes the number of redundant components in each subsystem. A customized hybrid dynamic NSGA II mixed with the MOPSO algorithm solves the proposed RAP. The customized algorithm uses a dynamic repository to save the elites for each generation. These elites will form the final solutions. Also, the parameters will dynamically change with the progress of the algorithm. This approach was compared to the mathematical method, meta-heuristic method and it had a better performance. Finally, the mathematical relations of control centers and stations calculate the total reliability of the SCADA system concerning the k-out-of-n-systems regarding minimum stations for acceptable system performance.

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Notes

  1. Motor Operated Valve.

References

  • Agarwal M, Gupta R (2006) Genetic search for redundancy optimization in complex systems. J Qual Maint Eng 12(4):338–353

    Article  Google Scholar 

  • Agarwal M, Sharma VK (2010) Ant colony approach to constrained redundancy optimization in binary systems. Appl MathModel 34:992–1003

    MathSciNet  MATH  Google Scholar 

  • Angelov PP, Gu X (2019) Introduction. Empirical approach to machine learning. Studies in computational intelligence, vol 800. Springer, Cham. https://doi.org/10.1007/978-3-030-02384-3_1

    Chapter  Google Scholar 

  • Angelov PP, Gu X, Príncipe JC (2018) A generalized methodology for data analysis. IEEE Trans Cybern 48(10):2981–2993. https://doi.org/10.1109/TCYB.2017.2753880

    Article  Google Scholar 

  • Billinton R (1992) Reliability evaluation of engineering systems: concepts and techniques, 2nd edn. Springer, Boston

    Book  MATH  Google Scholar 

  • Branke J, Deb K, Miettinen K, Slowiński R (2008) Multi objective optimization: interactive and evolutionary approaches. Springer, Berlin

    Book  MATH  Google Scholar 

  • Cao D, Murat A, Chinnam RB (2013) Efficient exact optimization of multi-objective redundancy allocation problems in series-parallel systems. Reliab Eng Syst Saf 111:154–163

    Article  Google Scholar 

  • Chambari A, Rahmati SHA, Najafi AA, Karimi A (2012) A bi objective model to optimize reliability and cost of system with a choice of redundancy strategies. Comput Ind Eng 63(1):109–119

    Article  Google Scholar 

  • Chambri AH, Azimi P, Najafi AA (2021) A bi-objective simulation-based optimization algorithm for redundancy allocation problem in series-parallel systems. Expert Syst Appl 173:114745

    Article  Google Scholar 

  • Chern MS (1992) On the computational complexity of reliability redundancy allocation in a series system. Oper Res Lett 11:309–315

    Article  MathSciNet  MATH  Google Scholar 

  • Coello Coello CA, Lechuga MS (2002) MOPSO: a proposal for multiple objective particle swarm optimizations. In: Proceedings of the 2002 congress on evolutionary computation. pp. 1051–6.

  • Coit DW (2001) Cold standby redundancy optimization for non-repairable systems. IEEE Trans 33:471–478

    Google Scholar 

  • Coit DW, Konak A (2006) Multiple weighted objectives heuristic for the redundancy allocation problem. IEEE Trans Reliab 55(3):551–558

    Article  Google Scholar 

  • Deb K, Pratab A, Agrawal S, Meyarivan T (2002) A fast and elitist multi objective genetic algorithm: NSGA-II. IEEE Trans Evol Comput 6(2):182–197

    Article  Google Scholar 

  • Devi S, Garg D (2020) Hybrid genetic and particle swarm algorithm: redundancy allocation problem. Int J Syst Assur Eng Manag 11:313–319

    Article  Google Scholar 

  • Dolatshahi-Zand A, Khalili-Damghani K (2015) Design of SCADA water resource management control center by a bi-objective redundancy allocation problem and particle swarm optimization. Reliab Eng Syst Saf 133:11–21

    Article  Google Scholar 

  • Dolatshahi Zand A, Khalili-Damghani K (2021) Designing an intelligent control philosophy in reservoirs of water transfer networks in supervisory control and data acquisition system stations. Int J Autom Comput. https://doi.org/10.1007/s11633-021-1284-1

    Article  Google Scholar 

  • Ebrahimipour V, Asadzadeh S, Azadeh A (2013) An emotional learning—based fuzzy inference system for improvement of system reliability evaluation in redundancy allocation problem. Int J Adv Manuf Technol 66:1657–1672

    Article  Google Scholar 

  • Garg H, Rani M, Sharma SP (2013) An efficient two-phase approach for solving reliability–redundancy allocation problem using artificial bee colony technique. Comput Oper Res 40:2961–2969

    Article  MathSciNet  MATH  Google Scholar 

  • Gholinezhad H, Hamadani AZ (2017) A new model for the redundancy allocation problem with component mixing and mixed redundancy strategy. Reliab Eng Syst Saf 164:66–73

    Article  Google Scholar 

  • He P, Kaigui Wu, Jie Xu, Wen J, Jiang Z (2013) Multi-level redundancy allocation using two dimensional arrays encoding and hybrid genetic algorithm. Comput Ind Eng 64:69–83

    Article  Google Scholar 

  • Juran J, Godfrey B (1998) Jurans quality handbook, 5th edn. McGraw-Hill, New York

    Google Scholar 

  • Khalili-Damghani K, Amiri M (2012) Solving binary-state multi-objective reliability redundancy allocation series-parallel problem using epsilon-constraint, multi-start partial bound enumeration algorithm and DEA. Reliab Eng Syst Saf 103:35–39

    Article  Google Scholar 

  • Khalili-Damghani K, Abtahi AR, Tavana M (2013) A new multi-objective particle swarm optimization method for solving reliability redundancy allocation problems. Reliab Eng Syst Saf 111:58–75

    Article  Google Scholar 

  • Kulturel-Konak S, Smith AE, Coit DW (2003) Efficiently solving the redundancy allocation problem using tabu search. IIE Trans 35(6):515–526

    Article  Google Scholar 

  • Lai CM, Yeh WC (2016) Two-stage simplified swarm optimization for the redundancy allocation problem in a multi-state bridge system. Reliab Eng Syst Saf 156:148–158

    Article  Google Scholar 

  • Liang YC, Lo MH (2010) Multi-objective redundancy allocation optimization using a variable neighborhood search algorithm. J Heuristics 16:511–535

    Article  MATH  Google Scholar 

  • Liang YC, Smith AE (2004) An ant colony optimization algorithm for the redundancy allocation problem (RAP). IEEE Trans Reliab 53(3):417–423

    Article  Google Scholar 

  • Lins ID, Droguett EL (2011) Redundancy allocation problems considering systems with imperfect repairs using multi-objective genetic algorithms and discrete event simulation. Simul Model Pract Theor 19:362–381

    Article  Google Scholar 

  • Lughofer E, Angelov P, Zhou X (2007) Evolving single- and multi-model fuzzy classifiers with FLEXFIS-class. IEEE International Fuzzy Systems Conference. pp. 1–6. https://doi.org/10.1109/FUZZY.2007.4295393.

  • Mahapatra GS, Roy TK (2011) Optimal redundancy allocation in series-parallel system using generalized fuzzy number. Tamsui Oxford J Inf Math Sci 27(1):1–20

    MathSciNet  MATH  Google Scholar 

  • Mavrotas G (2009) Effective implementation of the ε-constraint method in multi-objective mathematical programming problems. Appl Math Comput 213(2):455–465

    Article  MathSciNet  MATH  Google Scholar 

  • Mehta BR, Jaganmohan Reddy Y (2014) Industrial process automation systems, 1st edn. Butterworth-Heinemann, Oxford, pp 237–241

    Google Scholar 

  • Misra KB, Ljubojevic MD (1973) Optimal reliability design of a system: a new look. IEEE Trans Reliab 22:255–258

    Article  Google Scholar 

  • Nahas N, Nourelfath M, Ait-Kadi D (2007) Coupling ant colony and the degraded ceiling algorithm for the redundancy allocation problem of series–parallel systems. Reliab Eng Syst Saf 92:211–222

    Article  Google Scholar 

  • Onishi J, Kimura S, James RJW, Nakagawa Y (2007) Solving the redundancy allocation problem with a mix of components using the improved surrogate constraint method. IEEE Trans Reliab 56(1):94–101

    Article  Google Scholar 

  • Petruzella FD (2011) Programmable logic controllers, 4th edn. McGraw-Hill, New York, pp 142–157

    Google Scholar 

  • Ramirez-Marquez JE, Coit DW (2004) A Heuristic for solving the redundancy allocation problem for multi-state series-parallel systems. Reliab Eng Syst Saf 83:341–349

    Article  Google Scholar 

  • Ramirez-Marquez JE, Coit DW, Konak A (2004) Redundancy allocation for series-parallel systems using a max-min approach. IIE Trans 36(9):891–8988. https://doi.org/10.1080/07408170490473097

    Article  Google Scholar 

  • Safari J (2012) Multi-objective reliability optimization of series-parallel systems with a choice of redundancy strategies. Reliab Eng Syst Saf 108:10–20

    Article  Google Scholar 

  • Safari J, Tavakkoli-Moghaddam R (2010) A redundancy allocation problem with the choice of redundancy strategies by a memetic algorithm. J Ind Eng Int 6(11): 6–16. http://jiei.azad.ac.ir/article_511023.html

  • Samanta A, Basu K (2019) Multi-objective reliability redundancy allocation problem considering two types of common cause failures. Int J Syst Assur Eng Manag 10:369–383

    Article  Google Scholar 

  • Sheikhalishahi M, Ebrahimipour V, Shiri H, Zaman H, Jeihoonian M (2013) A hybrid GA–PSO approach for reliability optimization in redundancy allocation problem. Int J Adv Manuf Technol 68:317–338

    Article  Google Scholar 

  • Sieh H (2002) A linear approximation for redundant reliability problems with multiple component choices. Comput Ind Eng 44:91–103

    Google Scholar 

  • Tan Y, Deng G (2013) Hybrid particle swarm optimization with differential evolution and chaotic local search to solve reliability-redundancy allocation problems. J Central South Univ 20:1572–1581

    Article  Google Scholar 

  • Yeh WC, Su YZ, Gao XZ, Hu CF, Wang J, Huang CL (2021) Simplified swarm optimization for bi-objection active reliability redundancy allocation problems. Appl Soft Comput 106:107321

    Article  Google Scholar 

  • Zhao J-H, Liu Z, Dao T-M (2007) Reliability optimization using multi objective ant colony system approaches. Reliab Eng Syst Saf 92(1):109–120

    Article  Google Scholar 

  • Zio E, Bazzo R (2011) Level diagrams analysis of Pareto front for multi objective system redundancy allocation. Reliab Eng Syst Saf 96:569–580

    Article  Google Scholar 

Download references

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Correspondence to Kaveh Khalili-Damghani.

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Zand, A.D., Khalili-Damghani, K. & Raissi, S. An evolutionary approach with reliability priority to design Scada systems for water reservoirs. Evolving Systems 13, 499–517 (2022). https://doi.org/10.1007/s12530-022-09438-0

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