Static output feedback LFC for semi-Markov type interconnected multi-area power systems: A non-fragile PI strategy

IF 3.2 3区 计算机科学 Q2 AUTOMATION & CONTROL SYSTEMS
Xiaoqing Li, Yulong Li, Jun Cheng, Kaibo Shi, Kun Qiu
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引用次数: 0

Abstract

This article addresses the static output feedback load frequency control (LFC) problem for semi-Markov type interconnected multi-area power systems (IMAPSs) through non-fragile proportional-integral (PI) strategy. Primarily, by roundly exploiting the dynamical properties and considering random mutations of the IMAPSs, the semi-Markov jump process (MJP) is elaborately scheduled for establishing the dynamical model with precision. Subsequently, on account of the characteristic of geographical isolation, a memory-like sampled-data LFC mechanism considering time-varying transmission delay for the semi-Markov jump IMPASs is formulated accordingly. Additionally, the non-fragile issue is explored for the PI controller gain fluctuations to meet the actual operation scenario. After that, a mode-dependent two-side polynomial-type looped Lyapunov functional is constructed for enhancing flexibility. Then, in virtue of the stochastic analysis technique, some relaxed conditions guaranteeing stochastic stability with prescribed H $$ {H}_{\infty } $$ performance are thereby derived in the shape of linear matrix inequalities (LMIs). Ultimately, a numerical example is carried out to validate the efficacy of the developed control methodology.

半马尔可夫型互联多区域电力系统的静态输出反馈 LFC:非脆弱 PI 策略
本文通过非脆弱的比例积分(PI)策略解决了半马尔可夫型多区域互联电力系统(IMAPS)的静态输出反馈负载频率控制(LFC)问题。首先,通过充分利用 IMAPS 的动态特性并考虑随机突变,精心安排半马尔可夫跃迁过程(MJP)以建立精确的动态模型。随后,考虑到地理隔离的特点,相应地为半马尔可夫跃迁 IMPAS 制定了考虑时变传输延迟的类记忆采样数据 LFC 机制。此外,还探讨了 PI 控制器增益波动的非脆弱性问题,以满足实际运行情况的需要。随后,构建了一个与模式相关的双侧多项式型循环 Lyapunov 函数,以提高灵活性。然后,利用随机分析技术,以线性矩阵不等式(LMI)的形式推导出一些保证随机稳定性和规定性能的松弛条件。最后,通过一个数值实例验证了所开发控制方法的有效性。
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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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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