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A self-adaptive chaotic particle swarm algorithm for short term hydroelectric system scheduling in deregulated environment

机译：管制环境下短期水电系统调度的自适应混沌粒子群算法

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摘要

This paper proposes a short term hydroelectric plant dispatch model based on the rule of maximizing the benefit. For the optimal dispatch model, which is a large scale nonlinear planning problem with multi- constraints and multi-variables, this paper proposes a novel self-adaptive chaotic particle swarm optimization algorithm to solve the short term generation scheduling of a hydro-system better in a deregulated environment. Since chaotic mapping enjoys certainty, ergodicity and the stochastic property, the proposed approach introduces chaos mapping and an adaptive scaling term into the particle swarm optimization algorithm, which increases its convergence rate and resulting precision. The new method has been examined and tested on a practical hydro-system. The results are promising and show the effectiveness and robustness of the proposed approach in comparison with the traditional particle swarm optimization algorithm.
机译：本文基于收益最大化的原则，提出了短期水电厂调度模型。针对最优调度模型，它是一个具有多约束多变量的大规模非线性规划问题，提出了一种新的自适应混沌粒子群优化算法，以更好地解决水电系统短期发电调度问题。放松管制的环境。由于混沌映射具有确定性，遍历性和随机性，因此该方法将混沌映射和自适应缩放项引入了粒子群优化算法中，提高了收敛速度，提高了精度。新方法已经在实际的水力系统上进行了检验和测试。结果是有希望的，并且与传统的粒子群优化算法相比，该方法的有效性和鲁棒性。

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