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Teaching-learning-based optimization algorithm for multi-area economic dispatch

机译:基于教学的多区域经济调度优化算法

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摘要

This paper presents teaching-learning-based optimization algorithm for solving MAED (multi-area economic dispatch) problem with tie line constraints considering transmission losses, multiple fuels, valve-point loading and prohibited operating zones. TLBO (teaching-learning-based optimization) is one of the recently proposed population based algorithms which simulates the teaching-learning process of the class room. It is a very simple and robust global optimization technique. The effectiveness of the proposed algorithm has been verified on three different test systems, both small and large, involving varying degree of complexity. Compared with differential evolution, evolutionary programming and real coded genetic algorithm, considering the quality of the solution obtained, the proposed algorithm seems to be a promising alternative approach for solving the MAED problems in practical power system.
机译:本文提出了一种基于教学的优化算法,该算法用于解决具有连接线约束的MAED(多区域经济调度)问题,该问题考虑了传输损失,多种燃料,阀点负载和禁止的操作区域。 TLBO(基于教学学习的优化)是最近提出的基于人口的算法之一,它模拟了课堂的教学过程。这是一种非常简单且强大的全局优化技术。该算法的有效性已在大小不同的三个不同的测试系统上得到验证,涉及不同程度的复杂性。与差分进化,进化规划和实数编码遗传算法相比,考虑到所获得解的质量,所提出的算法似乎是解决实际电力系统中MAED问题的一种有前途的替代方法。

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