Abstract
The research presented in this paper compares two different computational approaches to topology synthesis: graph grammars (GG) and a genetic algorithm (GA). Both are applied to the domain of solar panel networks, which are configured to maximize power under a variety of sunlight conditions. Networks mostly connected in series create high voltages but are susceptible to shading. On the other hand, highly parallel topologies tend to create too low voltages. Thus, there is an opportunity to optimize photovoltaic (PV) array topologies given sunlight conditions. The paper describes methods to model the PV topology optimization problem using both a synthesis and an optimization approach. The focus is on computationally representing the design problem as well as the problem specific knowledge. Implementations of two different grammar rule sets in the GG are compared to four different encodings of the design problem in the GA. Results are shown for both approaches and a discussion of their advantages and drawbacks is given.
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References
Gao L, Dougal RA, Liu S, Iotova AP (2009) Parallel-connected solar PV system to address partial and rapidly fluctuating shadow conditions. IEEE Trans Industr Electron 56(5):1548–1556
Esram T, Chapman PL (2007) Comparison of photovoltaic array maximum power point tracking techniques. IEEE Trans Energy Convers 22(2):439–449
Cagan J, Campbell MI, Finger S, Tomiyama T (2005) A framework for computational design synthesis: model and applications. J Comput Inf Sci Eng 5(3):171
Patel H, Agrawal V (2008) Maximum power point tracking scheme for PV systems operating under partially shaded conditions. IEEE Trans Industr Electron 55(4):1689–1698
Schmidt LC, Shetty H, Chase SC (2000) A graph grammar approach for structure synthesis of mechanisms. J Mech Des 122(4):371–376
Lin Y-s, Shea K, Johnson A, Coultate J, Pears J (2009) A method and software tool for automated gearbox synthesis. ASME, San Diego
Patel J, Campbell MI (2010) An approach to automate and optimize concept generation of sheet metal parts by topological and parametric decoupling. J Mech Des 132(5):051001
Helms B, Shea K, Hoisl F (2009) A framework for computational design synthesis based on graph-grammars and function-behavior-structure. ASME, San Diego
Kurtoglu T, Campbell MI (2006) A graph grammar based framework for automated concept generation. DESIGN
Helms B, Shea K (2010) Booggie—an object-oriented graph-grammar implementation for engineering design synthesis. In: Gero JS (ed) Fourth international conference on design computing and cognition dcc’10. Springer, Stuttgart
Campbell MI (2011) The official graph synth site. University of Austin, Texas [cited 2012 03.01.2012]. Available from: http://www.graphsynth.com
Chakrabarti A, Shea K, Stone R, Cagan J, Campbell M, Hernandez NV et al (2011) Computer-based design synthesis research: an overview. J Comput Inf Sci Eng 11(2):021003
Mitchell M (1996) An introduction to genetic algorithms. In: Press M (ed) MIT Press, Cambridge
Acknowledgments
The authors would like to acknowledge the Technische Universität München—Institute for Advanced Study, funded by the German Excellence Initiative.
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Königseder, C., Shea, K., Campbell, M.I. (2013). Comparing a Graph-Grammar Approach to Genetic Algorithms for Computational Synthesis of PV Arrays. In: Chakrabarti, A. (eds) CIRP Design 2012. Springer, London. https://doi.org/10.1007/978-1-4471-4507-3_11
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DOI: https://doi.org/10.1007/978-1-4471-4507-3_11
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