The following information was submitted:
Transactions: WSEAS TRANSACTIONS ON SYSTEMS
Transactions ID Number: 53-172
Full Name: S. Muthu Vijaya Pandian
Position: Associate Professor
Age: ON
Sex: Male
Address: Dept of E.E.E,V.L.B.Janakiammal College of Engineering and Technology,
Country: INDIA
Tel: +91-9865259633
Tel prefix:
Fax: +9104222607152
E-mail address: ajay_vijay@rediffmail.com
Other E-mails: dr.thanushkodidirector@gmail.com
Title of the Paper: An EP based EPSO for ED problem with valve-point loading
Authors as they appear in the Paper: S.Muthu Vijaya Pandian, K.Thanushkodi
Email addresses of all the authors: ajay_vijay@rediffmail.com, dr_thanush@rediffmail.com.
Number of paper pages: 10
Abstract: Economic dispatch (ED) is one of the most important optimization problems in a power system. The objective of ED is that the sharing of power demand among the online generators keeping minimum cost of generation as a constraint. The aim of the paper is to operate an electric power system most economically within its security limits. This thesis mainly focuses on minimizing the total fuel cost of all generators of the power system. This paper proposes the following two hybrid algorithms to solve non- convex economic dispatch problem. i) A Hybrid of Neural Network (NN) and Efficient particle swarm optimization (EPSO) is termed as NN-EPSO. ii)A Hybrid of Evolutionary Programming (EP) and Efficient particle swarm optimization (EPSO) is termed as EP-EPSO. In order to exploit the promising solution region, a simple local random search EP procedure is integrated with EPSO to form the proposed hybrid algorithm. The proposed techniques are tested on standard test syste!
ms available in the literature. The performance of proposed EP-EPSO is compared with i) NPSO-LRS, ii)GA-SA , iii)DEC-SQP, iv) ICA-PSO and v)DE/BBO. It is observed that the EP-EPSO has higher convergence rate, advanced quality and better optimal cost when compared to the other techniques. ED problems considered have been solved including transmission losses with and without valve-point loading effects.
Keywords: Economic load dispatch, Efficient particle swarm optimization, Evolutionary Programming, Neural networks, Valve-point loading and power balance constraints
EXTENSION of the file: .pdf
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