Multi-objective optimization of the energy capture and boost inductor mass in a module-integrated converter (MIC) photovoltaic energy system

Mirjafari, Mehran; Balog, Robert S.

doi:10.1109/apec.2012.6166097

Cited by 9 publications

(7 citation statements)

References 19 publications

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“…A model was proposed and the optimum solution obtained according to economic advantages and environmental impacts in [43]. More studies of MOO can be found in [40,44,45]. The methodology suggested by the authors in the present paper can be further applied to MOO under uncertain inputs.…”

Section: Introductionmentioning

confidence: 97%

A Comparative Study of Multiple-Criteria Decision-Making Methods under Stochastic Inputs

et al. 2016

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This paper presents an application and extension of multiple-criteria decision-making (MCDM) methods to account for stochastic input variables. More in particular, a comparative study is carried out among well-known and widely-applied methods in MCDM, when applied to the reference problem of the selection of wind turbine support structures for a given deployment location. Along with data from industrial experts, six deterministic MCDM methods are studied, so as to determine the best alternative among the available options, assessed against selected criteria with a view toward assigning confidence levels to each option. Following an overview of the literature around MCDM problems, the best practice implementation of each method is presented aiming to assist stakeholders and decision-makers to support decisions in real-world applications, where many and often conflicting criteria are present within uncertain environments. The outcomes of this research highlight that more sophisticated methods, such as technique for the order of preference by similarity to the ideal solution (TOPSIS) and Preference Ranking Organization method for enrichment evaluation (PROMETHEE), better predict the optimum design alternative.

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Section: Introductionmentioning

confidence: 97%

A Comparative Study of Multiple-Criteria Decision-Making Methods under Stochastic Inputs

et al. 2016

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“…About this task, different optimization algorithms are available, some of them are deterministic [9], others stochastic [10]. Due to their high performances, heuristic algorithms, such as genetic ones, are effective and largely used for the solution of multi-objective optimization problems.…”

Section: Multi-objective Optimal Design Of Photovoltaicmentioning

confidence: 99%

“…In [10,18], multi-objective optimization algorithms are used to minimize/maximize PV converter parameters. In detail, the DMPPT boost inductor size and mass are minimized and the energy captured by the PV module is maximized in [10].…”

Section: B Literature Reviewmentioning

confidence: 99%

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Multiobjective Optimal Design of Photovoltaic Synchronous Boost Converters Assessing Efficiency, Reliability, and Cost Savings

Adinolfi

Graditi

Siano

et al. 2015

IEEE Trans. Ind. Inf.

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Optimal design of switching converters for the integration\ud and optimal exploitation of renewable energy sources\ud (RES) represents a crucial issue often debated in the recent power\ud electronics literature. The design problem required to carry out\ud a multiobjective optimization characterized by simultaneous conflicting\ud objectives, such as efficiency, reliability, and price, where\ud the best compromise solution should be found by the decision\ud maker among Pareto-optimal solutions. In this paper, a novel\ud design method for distributed maximum power point tracking\ud (DMPPT) synchronous boost converter is proposed. The method\ud is based on nondominated sorting genetic algorithm with the aim\ud to obtain the best synchronous rectification (SR) boost topology\ud while considering different targets such as converter efficiency\ud and reliability maximization, as well as converter price minimization.\ud New weighted indices are also proposed for a more realistic\ud characterization of the devices

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“…For full-bridge and push-pull topologies, there is also one filter inductor present. Core and copper losses of magnetic components have been studied extensively in the literature [22][23][24][25][26]. In order to calculate core losses in an inductor or transformer in the presence of a AC non-sinusoidal waveform, an extension of the Steinmetz equation, called natural Steinmetz equation [22,27] is used:…”

Section: ) Magnetic Componentsmentioning

confidence: 99%

Multiobjective Optimization and Topology Selection for a Module-Integrated Inverter

Mirjafari

Harb²,

Balog

2015

IEEE Trans. Power Electron.

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As photovoltaic (PV) energy continues to gain market penetration, thanks in part to substantial cost reductions on the solar modules, attention is shifting to the balance-of-system performance and costs. This paper proposes an approach to explore the design limits of a module -integrated inverter by means of the component-level multi-objective optimization. A two-stage inverter along with an active filter to eliminate double-frequency content of the DC-link current is proposed for the topology of module-integrated inverter, and three candidate topologies were taken into account for the DC-DC stage. Efficiency, volume and reliability of the converters are taken as objective functions, and comprehensive component modeling is performed. Pareto frontiers reveal the limits of the system are illustrated, which help the designer to pick the most suitable topology and design for the entire converter. Experimental results show the validity of the modeling and optimization technique.

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Multi-objective optimization of the energy capture and boost inductor mass in a module-integrated converter (MIC) photovoltaic energy system

Cited by 9 publications

References 19 publications

A Comparative Study of Multiple-Criteria Decision-Making Methods under Stochastic Inputs

A Comparative Study of Multiple-Criteria Decision-Making Methods under Stochastic Inputs

Multiobjective Optimal Design of Photovoltaic Synchronous Boost Converters Assessing Efficiency, Reliability, and Cost Savings

Multiobjective Optimization and Topology Selection for a Module-Integrated Inverter

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