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arXiv 2301.01461eess.SYcs.SY
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一种面向构网型与跟网型电源微电网二次控制的新型Koopman启发方法

A Novel Koopman-Inspired Method for the Secondary Control of Microgrids with Grid-Forming and Grid-Following Sources

Xun Gong, Xiaozhe Wang

AI总结:

本文提出一种无需预热训练的在线数据驱动Koopman启发辨识与控制方法,用于包含构网型和跟网型电源的微电网二次电压与频率控制,在多母线测试系统中验证了其在噪声、时延及大扰动下的有效性与鲁棒性。

摘要:

本文提出一种用于微电网二次电压和频率控制的在线数据驱动Koopman启发辨识与控制方法。与典型数据驱动方法不同,该方法无需预热训练,且能保证有界输入有界输出(BIBO)稳定性,在某些温和条件下甚至具有渐近稳定性。该方法自适应估计Koopman状态空间模型,从而执行有效的二次电压和频率控制,以应对微电网的非线性和不确定性。在4母线和13母线微电网测试系统(包含构网型和跟网型电源)中的案例研究表明,即使存在测量噪声和时延,在运行条件改变和受到大扰动(如微电网模式切换、发电/负荷变化)时,所提辨识与控制方法依然有效且鲁棒。

英文摘要:

This paper proposes an online data-driven Koopman-inspired identification and control method for microgrid secondary voltage and frequency control. Unlike typical data-driven methods, the proposed method requires no warm-up training yet with guaranteed bounded-input-bounded-output (BIBO) stability and even asymptotic stability under some mild conditions. The proposed method estimates the Koopman state space model adaptively so as to perform effective secondary voltage and frequency control that can handle microgrid nonlinearity and uncertainty. Case studies in the 4-bus and 13-bus microgrid test systems (with grid-forming and grid-following sources) demonstrate the effectiveness and robustness of the proposed identification and control method subject to the change of operating conditions and large disturbances (e.g., microgrid mode transitions, generation/load variations) even with measurement noises and time delays.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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