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Teaching Learning Based Optimization algorithm for reactive power planning

机译:基于教学学习的无功规划优化算法

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Reactive power planning is one of the most challenging problem for efficient and source operation of an interconnected power network. It requires effective and optimum co-ordination of all the reactive power sources present in the network. Recently, Teaching Learning Based Optimization (TLBO) algorithm is evolved and finds its application in the field of engineering optimization. In the proposed work TLBO based optimization algorithm is used for reactive power planning and applied in IEEE 30 and IEEE 57 bus system. The results obtained by this method are compared with the results obtained by other optimization techniques like PSO (Particle swarm optimization), Krill heard, HSA (Harmony search algorithm) and BB-BC (Big Bang-Big Crunch). At the end, TLBO appears as the most effective method for reactive power planning among all the methods discussed and can be considered as one of the standard method for reactive power optimization. (C) 2016 Elsevier Ltd. All rights reserved.
机译:无功功率规划是互连电源网络高效和源运行的最具挑战性的问题之一。它需要有效且最佳地协调网络中存在的所有无功功率源。最近,基于教学的学习优化(TLBO)算法得到了发展,并在工程优化领域得到了应用。在提出的工作中,基于TLBO的优化算法用于无功功率规划,并应用于IEEE 30和IEEE 57总线系统。将通过此方法获得的结果与通过其他优化技术(例如PSO(粒子群优化),Krill Listen,HSA(Harmony搜索算法)和BB-BC(Big Bang-Big Crunch)等获得的结果进行比较。最后,在所有讨论的方法中,TLBO似乎是最有效的无功功率规划方法,可以视为无功功率优化的标准方法之一。 (C)2016 Elsevier Ltd.保留所有权利。

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