一种用于实际经济负荷调度的增强时有效粒子群智能

Mr. G. Loganathan, Mr. D. Rajkumar, M. Vigneshwaran, Mr. R. Senthilkumar
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引用次数: 9

摘要

电力需求对火电厂的经济运行起着至关重要的作用,经济负荷调度是发电侧需要考虑的一个重要问题。经济调度问题用光滑可微或分段二次目标函数来表示,这与经典的优化方法相同。尽管如此,由于阀点效应,实际输入输出特性变成了一个高阶非线性和不连续的特性,从而导致非凸、非光滑的燃料成本函数。即使是最流行的PSO技术在某些应用中也表现出更优化的价值。电力系统就是这样一个应用,自然启发算法比lambda迭代法、梯度法等传统方法给出了更好的结果。本文阐述了粒子群优化算法在几分之一秒内就能较好地解决经济实用的经济负荷调度问题(带阀点效应)。在此情况下,分别考虑3台发电机系统、13台发电机系统和20台发电机系统,提出了有效的粒子群智能,并利用该智能找到了每个发电机的最优功率值,从而给出了最优成本。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An enhanced time effective particle swarm intelligence for the practical economic load dispatch
Economic load dispatch is an important problem to be considered in the generation side where the power demand plays the vital role for the economic operation of thermal power plants. The Economic dispatch problem is formulated by a smooth differentiable or piecewise quadratic objective function, which is the same approach used by classical optimization methods. Even though, due to the valve-point effects, the real input output characteristics becomes a higher order nonlinear and discontinuous one which results in a non-convex, non-smooth fuel cost function. Even the most popular PSO technique also presents decade to have a more optimized value in certain applications. Power system is one such application where the naturally inspired algorithms are giving better results than the conventional methods like lambda iteration method , gradient method etc. In this paper, particle swarm optimization which solves economic and practical economic load dispatch problem (with valve point effect) with better optimized results in a little fraction of seconds is explained. In this case, 3 generator system, 13 generator system and 20 generator systems are considered and the optimal values of power of each generator which gives optimal cost was found by this time effective particle swarm intelligence is proposed in this paper.
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