七阶电力系统混沌的新型对数滑模控制

IF 3.2 3区 计算机科学 Q2 AUTOMATION & CONTROL SYSTEMS
Qian Cao, Du Qu Wei
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引用次数: 0

摘要

在七阶电力系统中,由于系统参数和初始条件之间错综复杂的相互作用,混沌振荡会严重影响系统的稳定性。本研究介绍了一种专为此类高维系统量身定制的鲁棒混沌控制策略。该方法首先集成了一个储能设备的动态模型,专门用于吸收剩余的有功功率,从而构建了一个具有耦合动态的受控九阶电力系统。为了进一步提高稳定性和快速响应能力,我们提出了一种新颖的对数滑动模式控制 (LSMC) 表面。这一创新结合了自然对数,在降阶滑动模态系统的平衡点实现了高增益效果,促进了电力系统的快速再稳定。此外,我们还采用了超扭曲算法,以解决与传统滑模控制相关的固有颤振问题。理论分析和模拟实验验证了我们所提方法的有效性,证明了其迅速恢复系统稳定性的能力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Novel Logarithmic Sliding Mode Control of Chaos in Seventh-Order Power Systems

In seventh-order power systems, chaotic oscillations can significantly compromise system stability, arising from intricate interactions among system parameters and initial conditions. This study introduces a robust chaos control strategy tailored for such high-dimensional systems. The approach begins by integrating a dynamic model of an energy storage device, specifically designed to absorb surplus active power, thereby constructing a controlled ninth-order power system with coupled dynamics. To further enhance stability and rapid response, we propose a novel logarithmic sliding mode control (LSMC) surface. This innovation incorporates natural logarithms to achieve a high-gain effect at the equilibrium points of the reduced-order sliding mode system, facilitating rapid re-stabilization of the power system. Additionally, we employ the super-twisting algorithm to address the inherent chattering issues associated with traditional sliding mode control. Theoretical analyses and simulation experiments validate the effectiveness of our proposed method, demonstrating its capability to swiftly restore system stability.

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来源期刊
International Journal of Robust and Nonlinear Control
International Journal of Robust and Nonlinear Control 工程技术-工程:电子与电气
CiteScore
6.70
自引率
20.50%
发文量
505
审稿时长
2.7 months
期刊介绍: Papers that do not include an element of robust or nonlinear control and estimation theory will not be considered by the journal, and all papers will be expected to include significant novel content. The focus of the journal is on model based control design approaches rather than heuristic or rule based methods. Papers on neural networks will have to be of exceptional novelty to be considered for the journal.
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