Spelling suggestions: "subject:"power transmission."" "subject:"lower transmission.""
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Overcurrent protection for interconnected power systemsWhiting, J. P. January 1978 (has links)
No description available.
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Subjective evaluation of companded frequency modulation and delta modulation single channel per carrier satellite communication systemsAbachi, N. Y. January 1982 (has links)
No description available.
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Slotted cylindrical ring (SCR) resonator for compact microwave filtersLim, Yong Chuan Mark January 2001 (has links)
No description available.
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Topics in random signal theoryO'Reilly, J. J. January 1982 (has links)
No description available.
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The modelling of microwave attenuation and de-polarisation in rain-filled mediaUpton, S. A. J. January 1983 (has links)
No description available.
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Microwave mixers and modulators using multilayer technologyNg, Choon Yong January 2003 (has links)
No description available.
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Expert system applications to electrical power system voltage control and securityAbdelaal, Ashraf Khalil Ibrahim January 2001 (has links)
No description available.
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Applications of artificial neural networks to power systems network reduction and static security assessmentPeng, Liangjian January 1997 (has links)
No description available.
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The use of intelligent systems in control and management of power systemsElders, Ian Michael January 2002 (has links)
No description available.
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Power transmission planning using heuristic optimisation techniques : deterministic crowding genetic algorithms and ant colony search methodsChebbo, Hind Munzer January 2000 (has links)
The goal of transmission planning in electric power systems is a robust network which is economical, reliable, and in harmony with its environment taking into account the inherent uncertainties. For reasons of practicality, transmission planners have normally taken an incremental approach and tended to evaluate a relatively small number of expansion alternatives over a relatively short time horizon. In this thesis, two new planning methodologies namely the Deterministic Crowding Genetic Algorithm and the Ant Colony System are applied to solve the long term transmission planning problem. Both optimisation techniques consider a 'green field' approach, and are not constrained by the existing network design. They both identify the optimal transmission network over an extended time horizon based only on the expected pattern of electricity demand and generation sources. Two computer codes have been developed. An initial comparative investigation of the application of Ant Colony Optimisation and a Genetic Algorithm to an artificial test problem has been undertaken. It was found that both approaches were comparable for the artificial test problem.
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