论文标题
通过集合Koopman建模的微电网次级电压和频率控制的在线数据驱动方法
An Online Data-Driven Method for Microgrid Secondary Voltage and Frequency Control with Ensemble Koopman Modeling
论文作者
论文摘要
低惯性，非线性和高水平的不确定性（变化的拓扑和操作条件）对微电网（MG）全系统操作构成挑战。本文提出了一种在线自适应Koopman操作员最佳控制（AKOOC）方法，用于MG次级电压和频率控制。与渴望数据且缺乏保证稳定性的典型数据驱动的方法不同，拟议的Akooc不需要热身训练，但在某些轻度条件下，有保证的有限输入输出（BIBO）稳定性甚至渐近稳定性。提出的AKOOC是基于集合Koopman状态空间建模开发的，具有完整的函数，可以将线性和非线性碱基均组合在一起，而无需事件检测或切换。还开发了一种迭代学习方法来利用模型参数，以确保设计控制的有效性和适应性。在4-BUS（具有详细的内环控制）和34 BUS MG系统中的模拟研究显示了改善的建模准确性和控制性，即使在时间延迟，测量噪声和缺失的测量值下，该方法的有效性也会发生各种操作条件的有效性。
Low inertia, nonlinearity and a high level of uncertainty (varying topologies and operating conditions) pose challenges to microgrid (MG) systemwide operation. This paper proposes an online adaptive Koopman operator optimal control (AKOOC) method for MG secondary voltage and frequency control. Unlike typical data-driven methods that are data-hungry and lack guaranteed stability, the proposed AKOOC requires no warm-up training yet with guaranteed bounded-input-bounded-output (BIBO) stability and even asymptotical stability under some mild conditions. The proposed AKOOC is developed based on an ensemble Koopman state space modeling with full basis functions that combines both linear and nonlinear bases without the need of event detection or switching. An iterative learning method is also developed to exploit model parameters, ensuring the effectiveness and the adaptiveness of the designed control. Simulation studies in the 4-bus (with detailed inner-loop control) MG system and the 34-bus MG system showed improved modeling accuracy and control, verifying the effectiveness of the proposed method subject to various changes of operating conditions even with time delay, measurement noise, and missing measurements.
