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Dynamic economic load dispatch using hybrid swarm intelligence based harmony search algorithm
Authors:V Ravikumar Pandi  Bijaya Ketan Panigrahi
Affiliation:1. School of Automation, Guangdong University of Technology, Guangzhou, 510006, China;2. CSG Power Generation Company, China Southern Power Grid Co Ltd, Guangzhou, 510630, China;1. Department of Basic Engineering Science, Faculty of Engineering, Shebin El Kom, Menoufia University, Egypt;2. Department of Mathematics and Statistics, Faculty of Sciences, Taif University, Saudi Arabia;1. Department of Electrical Engineering, Jahrom University, Jahrom, Iran;2. Department of Electrical and Computer Engineering, Texas A&M University, USA;3. Department of Electrical Engineering, Faculty of Engineering, University of Isfahan, Isfahan 81746-73441, Iran;4. National Grid ESO, Warwick CV34 6DA, UK;5. School of Electrical and Electronic Engineering, The University of Manchester, Manchester M13 9PL, UK
Abstract:This paper presents the hybrid harmony search algorithm with swarm intelligence (HHS) to solve the dynamic economic load dispatch problem. Harmony Search (HS) is a recently developed derivative-free, meta-heuristic optimization algorithm, which draws inspiration from the musical process of searching for a perfect state of harmony. This work is an attempt to hybridize the HS algorithm with the powerful population based algorithm PSO for a better convergence of the proposed algorithm. The main aim of dynamic economic load dispatch problem is to find out the optimal generation schedule of the generators corresponding to the most economical operating point of the system over the considered timing horizon. The proposed algorithm also takes care of different constraints like power balance, ramp rate limits and generation limits by using penalty function method. Simulations were performed over various standard test systems with 5 units, 10 units and 30 units and a comparative study is carried out with other recently reported results. The findings affirmed the robustness and proficiency of the proposed methodology over other existing techniques.
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