Accounting for &amp;#x201C;mission&amp;#x201D; during co-optimization of system designs

Zhang, Tianlei; Conklin, Grant; Zhang, Yucheng; Dougal, Roger A.

doi:10.1109/syscon.2012.6189520

Cited by 1 publication

(5 citation statements)

References 17 publications

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“…where Pgout, k is the active power output of GTG k; pfk is the power factor of GTG k; uk is the online status (0=offline, 1=online) of GTG k; and vk is the power factor status (0=lagging, 1=leading) of GTG k. fEDP is the function calcuating the minimum fuel consumption [8], and QOS is the QOS metric in (1).…”

Section: B Simulation Results and Analysismentioning

confidence: 99%

“…where w is the inertia weight of PSO to calculate velocities of particles at each iteration [8]. We also adopt a dynamic value of w, changing from w max to w min based on the number of iterations, to improve the searching performance.…”

Section: A Methods For Handling Discrete Binary Variables In Sopsomentioning

confidence: 99%

“…The EDP for shipboard power generating plant has been developed in our previously published work as a constrained mixed-integer optimization problem [8]. Both the cost function and constraints are formulated with this variable set.…”

Section: A the Edpmentioning

confidence: 99%

“…However, system redundancy constraint in [8] only guaranteed the least number of GTGs that must be in service. Thus in most cases the GTG with the lowest power rating is always kept on to avoid single-point failure while minimizing the fuel consumption.…”

Section: A the Edpmentioning

confidence: 99%

“…In this section, we improve our previously developed single-objective PSO (SOPSO) [8] in terms of its method for handling discrete binary variables for independent optimization tasks of the EDP and QOS introduced in Section II. Also, we develop a MOPSO to solve the multi-objective optimization of these problems.…”

Section: Improved Features Of Sopso and Mopsomentioning

confidence: 99%

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Accounting for system-level controls during early-stage design

Zhang

Dougal

Zhang

2013

2013 IEEE Electric Ship Technologies Symposium (ESTS)

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Designers of shipboard power systems must consider many tradeoffs when selecting generators in the early-stage setbased design. This is not just an "equipment problem" based on physical design requirements such as limits of size and weight. The operating strategy for generators must be considered when evaluating the goodness of the design. To this end, this paper develops a design approach to impose system-level controls onto generating plants to evaluate their optimality. Two critical design metrics-fuel consumption and quality of service (QOS) are defined to incorporate these control variables, and cooptimized to reflect mission-oriented system performance. The minimum fuel consumption is computed based on a new concept of economic dispatch algorithm, which takes active and reactive power balance, and system redundancy requirements into account. QOS metric is defined to reflect system-level control strategies based on the classical concept of mean-time-betweenfailure, which is defined to depend inversely on the power produced. We develop a multi-objective particle swarm optimization to effectively locate the Pareto fronts of system performance for all design alternatives over a whole given mission. Thus a set of optimal design alternatives can be rapidly selected to represent the non-dominated optimal compromises of the two metrics. This design approach is demonstrated and compared with a single-objective design approach in a design example of shipboard generating plant design. And more optimal alternatives are able to be found.I.

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Section: B Simulation Results and Analysismentioning

confidence: 99%

Section: A Methods For Handling Discrete Binary Variables In Sopsomentioning

confidence: 99%

Section: A the Edpmentioning

confidence: 99%

Section: A the Edpmentioning

confidence: 99%

Section: Improved Features Of Sopso and Mopsomentioning

confidence: 99%

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