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Title: Optimum design of a hybrid PV–CSP–LPG microgrid with Particle Swarm Optimization technique

Authors:
; ; ;
Publication Date:
Sponsoring Org.:
USDOE
OSTI Identifier:
1397812
Resource Type:
Journal Article: Publisher's Accepted Manuscript
Journal Name:
Applied Thermal Engineering
Additional Journal Information:
Journal Volume: 109; Journal Issue: PB; Related Information: CHORUS Timestamp: 2017-10-04 22:04:00; Journal ID: ISSN 1359-4311
Publisher:
Elsevier
Country of Publication:
United Kingdom
Language:
English

Citation Formats

Ghaem Sigarchian, Sara, Orosz, Matthew S., Hemond, Harry F., and Malmquist, Anders. Optimum design of a hybrid PV–CSP–LPG microgrid with Particle Swarm Optimization technique. United Kingdom: N. p., 2016. Web. doi:10.1016/j.applthermaleng.2016.05.119.
Ghaem Sigarchian, Sara, Orosz, Matthew S., Hemond, Harry F., & Malmquist, Anders. Optimum design of a hybrid PV–CSP–LPG microgrid with Particle Swarm Optimization technique. United Kingdom. doi:10.1016/j.applthermaleng.2016.05.119.
Ghaem Sigarchian, Sara, Orosz, Matthew S., Hemond, Harry F., and Malmquist, Anders. 2016. "Optimum design of a hybrid PV–CSP–LPG microgrid with Particle Swarm Optimization technique". United Kingdom. doi:10.1016/j.applthermaleng.2016.05.119.
@article{osti_1397812,
title = {Optimum design of a hybrid PV–CSP–LPG microgrid with Particle Swarm Optimization technique},
author = {Ghaem Sigarchian, Sara and Orosz, Matthew S. and Hemond, Harry F. and Malmquist, Anders},
abstractNote = {},
doi = {10.1016/j.applthermaleng.2016.05.119},
journal = {Applied Thermal Engineering},
number = PB,
volume = 109,
place = {United Kingdom},
year = 2016,
month =
}

Journal Article:
Free Publicly Available Full Text
Publisher's Version of Record at 10.1016/j.applthermaleng.2016.05.119

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  • The detection and estimation of gravitational wave signals belonging to a parameterized family of waveforms requires, in general, the numerical maximization of a data-dependent function of the signal parameters. Because of noise in the data, the function to be maximized is often highly multimodal with numerous local maxima. Searching for the global maximum then becomes computationally expensive, which in turn can limit the scientific scope of the search. Stochastic optimization is one possible approach to reducing computational costs in such applications. We report results from a first investigation of the particle swarm optimization method in this context. The method ismore » applied to a test bed motivated by the problem of detection and estimation of a binary inspiral signal. Our results show that particle swarm optimization works well in the presence of high multimodality, making it a viable candidate method for further applications in gravitational wave data analysis.« less