DocumentCode :
3305446
Title :
FELM based intelligent optimal switching capacitor placement
Author :
Ravichandran, K.S. ; Alsheyuhi, S.S.S.
Author_Institution :
Sch. of Comput., SASTRA Univ., Thanjavur, India
Volume :
1
fYear :
2011
fDate :
26-28 July 2011
Firstpage :
366
Lastpage :
371
Abstract :
Minimizing the total active power loss and voltage drop are the two important challenges for power transmission and distribution in an unbalanced power flow problem for any power distribution system. This can be achieved by installing the proper sized switching capacitor at proper place in a power distribution system. This paper deals with the design of distributed power systems and optimal switching capacitor placements based on the Fuzzy-Extreme Learning Machine (FELM). The intelligent power automation system requires fast determination of site and size of the switchable capacitors in the system bus through the centralized control units based on the system load. But in case of conventional, ANN and other models available on the literature they are time consuming and less performing under a dynamic environment. To overcome this problem, we propose FELM mechanism to obtain an optimal switching capacitor placement in power distributed system. Fuzzy logic and its inference systems are used in ELM and the resultant FELM is to be used to find the site and size dynamically. Finally, the results are compared with a standard 34-bus test system with other models, with respect to the capacitor placement on the networks, savings and the computational time.
Keywords :
centralised control; fuzzy control; learning (artificial intelligence); neurocontrollers; power capacitors; power distribution control; power transmission control; ANN; FELM based intelligent optimal switching capacitor placement; active power loss; centralized control units; dynamic environment; fuzzy logic; fuzzy-extreme learning machine; inference system; intelligent power automation system; power distribution; power transmission; unbalanced power flow problem; voltage drop; Artificial neural networks; Capacitors; Load flow; Load modeling; Switches; Training; Artificial Neural Networks (ANN); Extreme Learning Machine; Fuzzy Logic; Intelligent power automation system component; Power Flow Problem;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Fuzzy Systems and Knowledge Discovery (FSKD), 2011 Eighth International Conference on
Conference_Location :
Shanghai
Print_ISBN :
978-1-61284-180-9
Type :
conf
DOI :
10.1109/FSKD.2011.6019579
Filename :
6019579
Link To Document :
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