Dynamic reconfiguration of shipboard power systems using reinforcement learning

Das, Sanjoy; Bose, Sayak; Pal, Souvik; Schulz, Noel N.; Scoglio, Caterina; Natarajan, Balasubramaniam

doi:10.1109/tpwrs.2012.2207466

Cited by 33 publications

(22 citation statements)

References 25 publications

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“…Many efforts have been devoted to study the fault management of SPS [2]- [8], [10]- [18]. In the transient-time scale, their main objective is to maximize the weighted sum of restored loads [2]- [7], [11]- [13]. Additionally, there are other considerations including obtaining the correct order of switching [2], probability-based prediction of fault effects [3], minimizing the number or cost of switching actions [4], [5], real-time management [6], [7], etc.…”

Section: B Literature Reviewmentioning

confidence: 99%

Optimal Power Management for Failure Mode of MVDC Microgrids in All-Electric Ships

Han

Yuan

et al. 2019

IEEE Trans. Power Syst.

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Optimal power management of shipboard power system for failure mode (OPMSF) is a significant and challenging problem considering the safety of system and person. Many existing works focused on the transient-time recovery without consideration of the operating cost and the voyage plan. In this paper, the OPMSF problem is formulated considering the mid-time scheduling and the faults at bus and generator. Twoside adjustment methods including the load shedding and the reconfiguration are coordinated for reducing the fault effects. To address the formulated non-convex problem, the travel equality constraint and fractional energy efficiency operation indicator (EEOI) limitation are transformed into the convex forms. Then, considering the infeasibility scenario affected by faults, a further relaxation is adopted to formulate a new problem with feasibility guaranteed. Furthermore, a sufficient condition is derived to ensure that the new problem has the same optimal solution as the original one. Because of the mixed-integer nonlinear feature, an optimal management algorithm based on Benders decomposition (BD) is developed to solve the new one. Due to the slow convergence caused by the time-coupled constraints, a low-complexity near-optimal algorithm based on BD (LNBD) is proposed. The results verify the effectivity of the proposed methods and algorithms.

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Section: B Literature Reviewmentioning

confidence: 99%

Optimal Power Management for Failure Mode of MVDC Microgrids in All-Electric Ships

Han

Yuan

et al. 2019

IEEE Trans. Power Syst.

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“…Cumulative distribution function of the power delivered to loads is presented to showcase the system robustness against random fault scenarios. Dynamic technique is employed for automatic reconfiguration in [19], which produces not only the final reconfiguration, but also the correct order in which the switches are to be changed.…”

Section: Introductionmentioning

confidence: 99%

Resilience Control of DC Shipboard Power Systems

Liu

Chen

et al. 2018

IEEE Trans. Power Syst.

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Direct current (DC) network has been recognized as a promising technique, especially for shipboard power systems (SPSs). Fast resilience control is required for an SPS to survive after faults. Towards this end, this paper proposes the indices of survivability and functionality, based on which a two-phase resilience control method is derived. The on/off status of loads are determined in the first phase to maximize survivability, while the functionality of supplying loads are maximized in the second phase. Based on a comprehensive model of a DC shipboard power systems (DC-SPS), the two-phase method renders two mixedinteger non-convex problems. To make the problems tractable, we develop second-order-cone-based convex relaxations, thus converting the problems into mixed-integer convex problems. Though this approach does not necessarily guarantee feasible, hence global, solutions to the original non-convex formulations, we provide additional mild assumptions, which ensures that the convex relaxations are exact when line constraints are not binding. In the case of inexactness, we provide a simple heuristic approach to ensure feasible solutions. Numerical tests empirically confirm the efficacy of the proposed method.

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“…Reconfiguration of SPS is a typical discrete, nonlinear, NP complete combinatorial optimization problem with multiple objectives and multiple constraints [3]. The traditional methods based on how to reduce the network loss of the land power system do not work well on the ship [4].…”

Section: Introductionmentioning

confidence: 99%

A Novel Multi-Objective Discrete Particle Swarm Optimization with Elitist Perturbation for Reconfiguration of Ship Power System

Zhang

Sun

Guo

2017

Polish Maritime Research

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A novel multi-objective discrete particle swarm optimization with elitist perturbation strategy (EPSMODPSO) is proposed and applied to solve the reconfiguration problem of shipboard power system(SPS). The new algorithm uses the velocity to decide each particle to move one step toward positive or negative direction to update the position. An elitist perturbation strategy is proposed to improve the local search ability of the algorithm. Reconfiguration model of SPS is established with multiple objectives, and an inherent homogeneity index is adopted as the auxiliary estimating index. Test results of examples show that the proposed EPSMODPSO performs excellent in terms of diversityand convergence of the obtained Pareto optimal front. It is competent to solve network reconfiguration of shipboard power system and other multi-objective discrete optimization problems.

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Dynamic reconfiguration of shipboard power systems using reinforcement learning

Cited by 33 publications

References 25 publications

Optimal Power Management for Failure Mode of MVDC Microgrids in All-Electric Ships

Optimal Power Management for Failure Mode of MVDC Microgrids in All-Electric Ships

Resilience Control of DC Shipboard Power Systems

A Novel Multi-Objective Discrete Particle Swarm Optimization with Elitist Perturbation for Reconfiguration of Ship Power System

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