Solution to Economic Load Dispatch using Ant Colony Search based-Teaching Learning Optimization

Karri Ravikumar Reddy, Y. Rao, Mulaswaminaidu Madepalli, U. Rao, S. Arumugam, G. V. Rao
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Abstract

The primary objective of this paper is to minimize power production cost by optimal allocation of generators with an equal constraint of load demand using the proposed Ant colony search based-TLBO. The Ant colony search based-TLBO algorithm furnishes sophisticated harmony between exploitation and exploration. Economical load dispatch is a non-linear problem, it contains several inequality constraints, and valve point loading are the causes, to need the optimization techniques if the function is linear several iterative methods are available and for non- linear functions also possible to apply various techniques but the main drawback in the generation cost curve functions the curve shape is not fixed due to valve point loading. In this paper, the ant colony search based-TLBO technique is proposed, and to test the stability of the proposed algorithm three different test cases are considered here:i) The standard IEEE-30 bus systemii) DG-based Industrial Corridor.iii) Gold-Copper Mine Power SystemAll these test cases have different numbers of generators as well as load centers. This is a multi-objective function and the proposed algorithm gives the optimal solution with very little time, high convergence rate, and the number of algorithm variables is very less used in it.
基于蚁群搜索的教学优化求解经济负荷调度
本文的主要目标是利用蚁群搜索的tlbo算法,在负载需求约束相等的情况下,通过优化分配发电机组,使发电成本最小化。基于蚁群搜索的tlbo算法实现了开发与探索的高度协调。经济负荷调度是一个非线性问题,它包含若干不等式约束,而阀点负荷是其原因,需要优化技术,如果函数是线性的,有几种迭代方法可用,对于非线性函数也可以应用各种技术,但发电成本曲线函数的主要缺点是由于阀点负荷导致曲线形状不固定。本文提出了基于蚁群搜索的tlbo算法,并考虑了三种不同的测试用例:1)标准IEEE-30总线系统;2)基于dg的工业走廊;3)金铜矿电力系统。这些测试用例具有不同数量的发电机和负载中心。这是一个多目标函数,所提出的算法以极短的时间和较高的收敛速度给出了最优解,并且使用的算法变量数量很少。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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