Please use this identifier to cite or link to this item: https://doi.org/10.1007/978-3-642-38703-6_40
Title: Optimal power flow solution using self-evolving brain-storming inclusive teaching-learning-based algorithm
Authors: Krishnanand, K.R.
Hasani, S.M.F.
Panigrahi, B.K.
Panda, S.K. 
Keywords: Brain-Storming Optimization
Non-dominated sorting
Optimal power flow
Teaching-learning-based optimization
Issue Date: 2013
Source: Krishnanand, K.R.,Hasani, S.M.F.,Panigrahi, B.K.,Panda, S.K. (2013). Optimal power flow solution using self-evolving brain-storming inclusive teaching-learning-based algorithm. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 7928 LNCS (PART 1) : 338-345. ScholarBank@NUS Repository. https://doi.org/10.1007/978-3-642-38703-6_40
Abstract: In this paper, a new hybrid self-evolving algorithm is presented with its application to a highly nonlinear problem in electrical engineering. The optimal power flow problem described here focuses on the minimization of the fuel costs of the thermal units while maintaining the voltage stability at each of the load buses. There are various restrictions on acceptable voltage levels, capacitance levels of shunt compensation devices and transformer taps making it highly complex and nonlinear. The hybrid algorithm discussed here is a combination of the learning principles from Brain Storming Optimization algorithm and Teaching-Learning-Based Optimization algorithm, along with a self-evolving principle applied to the control parameter. The strategies used in the proposed algorithm makes it self-adaptive in performing the search over the multi-dimensional problem domain. The results on an IEEE 30 Bus system indicate that the proposed algorithm is an excellent candidate in dealing with the optimal power flow problems. © 2013 Springer-Verlag Berlin Heidelberg.
Source Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
URI: http://scholarbank.nus.edu.sg/handle/10635/71294
ISBN: 9783642387029
ISSN: 03029743
DOI: 10.1007/978-3-642-38703-6_40
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