Abstract
Smart Ships represent the next generation of ships that use ICT to connect all the devices on board for integrated monitoring and safety management. In such a context, the Shipboard Power System (SPS) is critical to the survival and safety of the ship because many accidents are due to electrical failures. The SPS reconfiguration consists of a variation of the electrical topology to supply energy to critical services successfully. The proposed reconfiguration procedure uses an autonomous and mission-oriented approach, and it employs a generic-purpose self-adaptive Fault Management System.
It delivers a set of possible runtime solutions that properly consider the current ship mission and operating scenario while dealing with multiple failures.
Solutions, achieving a partial reconfiguration of the system, are considered when a full recovery strategy is not available according to the current ship conditions.
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Notes
- 1.
Goals are classified by different priority depending on the specific context. Thus, the reconfiguration system always prefers to address higher priority goals.
References
Ackoff, R.L.: Towards a system of systems concepts. Manage. Sci. 17(11), 661–671 (1971)
Bose, S., Pal, S., Natarajan, B., Scoglio, C.M., Das, S., Schulz, N.N.: Analysis of optimal reconfiguration of shipboard power systems. IEEE Trans. Power Syst. 27(1), 189–197 (2011)
Brun, Y., et al.: Engineering self-adaptive systems through feedback loops. In: Cheng, B.H.C., de Lemos, R., Giese, H., Inverardi, P., Magee, J. (eds.) Software Engineering for Self-Adaptive Systems. LNCS, vol. 5525, pp. 48–70. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-02161-9_3
Butler-Purry, K.: Multi-agent technology for self-healing shipboard power systems. In: Proceedings of the 13th International Conference on Intelligent Systems Application to Power Systems. IEEE (2005). 5–pp.
Cheng, B.H.C., et al.: Using models at runtime to address assurance for self-adaptive systems. In: Bencomo, N., France, R., Cheng, B.H.C., Aßmann, U. (eds.) Models@run.time. LNCS, vol. 8378, pp. 101–136. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-08915-7_4
Cheng, B.H.C., et al.: Software engineering for self-adaptive systems: a research roadmap. In: Cheng, B.H.C., de Lemos, R., Giese, H., Inverardi, P., Magee, J. (eds.) Software Engineering for Self-Adaptive Systems. LNCS, vol. 5525, pp. 1–26. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-02161-9_1
Cossentino, M., Lodato, C., Lopes, S., Sabatucci, L.: MUSA: a middleware for user-driven service adaptation. In: Proceedings of the 16th Workshop “From Objects to Agents”, Naples, Italy, 17–19 June 2015
Cossentino, M., Lopes, S., Renda, G., Sabatucci, L., Zaffora, F.: A metamodel of a multi-paradigm approach to smart cyber-physical systems development. In: Proceedings of the 20th Workshop “From Objects to Agents”, Parma, Italy, 26–28 June (2019)
Cossentino, M., Sabatucci, L., Lopes, S.: Partial and full goal satisfaction in the MUSA middleware. In: Slavkovik, M. (ed.) EUMAS 2018. LNCS (LNAI), vol. 11450, pp. 15–29. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-14174-5_2
de Lemos, R., et al.: Software engineering for self-adaptive systems: a second research roadmap. In: de Lemos, R., Giese, H., Müller, H.A., Shaw, M. (eds.) Software Engineering for Self-Adaptive Systems II. LNCS, vol. 7475, pp. 1–32. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-35813-5_1
Feliachi, A., Schoder, K., Ganesh, S., Lai, H.J.: Distributed control agents approach to energy management in electric shipboard power system. In: 2006 IEEE Power Engineering Society General Meeting. IEEE (2006). 6–pp.
Luck, M., McBurney, P., Shehory, O., Willmott, S.: Agent technology: computing as interaction (a roadmap for agent based computing). University of Southampton (2005)
Ouyang, L., Li, Y., Tan, Y., Xiao, J., Cao, Y.: Reconfiguration optimization of DC zonal distribution network of shipboard power system. In: 2016 IEEE Transportation Electrification Conference and Expo, Asia-Pacific (ITEC Asia-Pacific), pp. 444–448. IEEE (2016)
Qureshi, N.A., Perini, A., Ernst, N.A., Mylopoulos, J.: Towards a continuous requirements engineering framework for self-adaptive systems. In: 2010 First International Workshop on Requirements@ Run. Time, pp. 9–16. IEEE (2010)
Rao, A.S., Georgeff, M.P., et al.: BDI agents: from theory to practice. In: ICMAS, vol. 95, pp. 312–319 (1995)
Rushby, J.: Software verification and system assurance. In: 2009 Seventh IEEE International Conference on Software Engineering and Formal Methods, pp. 3–10. IEEE (2009)
Rybakov, V.: Linear temporal logic with until and next, logical consecutions. Ann. Pure Appl. Log. 155(1), 32–45 (2008)
Sabatucci, L., Cossentino, M.: From means-end analysis to proactive means-end reasoning. In: Proceedings of the 10th International Symposium on Software Engineering for Adaptive and Self-Managing Systems, pp. 2–12. IEEE Press (2015)
Sabatucci, L., Lodato, C., Lopes, S., Cossentino, M.: Towards self-adaptation and evolution in business process. In: AIBP@ AI* IA, pp. 1–10. Citeseer (2013)
Sabatucci, L., Lodato, C., Lopes, S., Cossentino, M.: Highly customizable service composition and orchestration. In: Dustdar, S., Leymann, F., Villari, M. (eds.) ESOCC 2015. LNCS, vol. 9306, pp. 156–170. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24072-5_11
Sabatucci, L., Lopes, S., Cossentino, M.: MUSA 2.0: a distributed and scalable middleware for user-driven service adaptation. In: De Pietro, G., Gallo, L., Howlett, R.J., Jain, L.C. (eds.) KES-IIMSS 2017. SIST, vol. 76, pp. 492–501. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-59480-4_49
Sabatucci, L., Ribino, P., Lodato, C., Lopes, S., Cossentino, M.: GoalSPEC: a goal specification language supporting adaptivity and evolution. In: Cossentino, M., El Fallah Seghrouchni, A., Winikoff, M. (eds.) EMAS 2013. LNCS (LNAI), vol. 8245, pp. 235–254. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-45343-4_13
Sabatucci, L., Cossentino, M., De Simone, G., Lopes, S.: Self-reconfiguration of shipboard power systems. In: Proceedings of the 3rd eCAS Workshop on Engineering Collective Adaptive Systems, Trento, Italy. IEEE (2018)
Sabatucci, L., De Simone, G., Cossentino, M.: Shipboard power system reconfiguration - a self-adaptation exemplar. In: Proceedings of the 4th International Workshop on Software Engineering for Smart Cyber-Physical Systems (SEsCPS 2018), Gothenburg, Sweden (2018)
Shariatzadeh, F., Kumar, N., Srivastava, A.K.: Optimal control algorithms for reconfiguration of shipboard microgrid distribution system using intelligent techniques. IEEE Trans. Ind. Appl. 53(1), 474–482 (2016)
Solanki, J.M., Schulz, N.N., Gao, W.: Reconfiguration for restoration of power systems using a multi-agent system. In: Proceedings of the 37th Annual North American Power Symposium, pp. 390–395 (2005)
Weiß, G.: Adaptation and learning in multi-agent systems: some remarks and a bibliography. In: Weiß, G., Sen, S. (eds.) IJCAI 1995. LNCS, vol. 1042, pp. 1–21. Springer, Heidelberg (1996). https://doi.org/10.1007/3-540-60923-7_16
Xue, W., Fu, Y.: A two-step method for reconfiguration of shipboard power system. In: 2011 IEEE Electric Ship Technologies Symposium, pp. 167–172. IEEE (2011)
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Cossentino, M., Lopes, S., Renda, G., Sabatucci, L., Zaffora, F. (2020). Smartness and Autonomy for Shipboard Power Systems Reconfiguration. In: Mazal, J., Fagiolini, A., Vasik, P. (eds) Modelling and Simulation for Autonomous Systems. MESAS 2019. Lecture Notes in Computer Science(), vol 11995. Springer, Cham. https://doi.org/10.1007/978-3-030-43890-6_26
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