基于黑盒的逆变器电力系统增量降阶建模框架

IF 5.2 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC IEEE Open Journal of the Industrial Electronics Society Pub Date : 2023-11-07 DOI:10.1109/OJIES.2023.3330894
Weihua Zhou;Jef Beerten
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引用次数: 0

摘要

基于状态空间模型(SSM)的特征值法由于具有参与因子分析和定位振荡源的能力,被广泛应用于逆变器插电系统的稳定性评估。然而,逆变器可能的内部机密性阻碍了其ssm的推导。此外,当涉及复杂的传输网络拓扑结构和各种传输电缆时,传统的系统SSM推导过程繁琐,可能导致系统SSM的高阶。为此,本文提出了一个基于黑盒的增量降阶建模框架。利用矩阵拟合算法分别从逆变器和传输电缆的dq域导纳频率响应和abc域阻抗频率响应中提取降阶ssm。然后,本文提出的SSM算子以类似于基于阻抗模型算子的递归组件阻抗聚合的方式,递归地组装组件拟合的SSM,同时保持单个组件的动态性。仿真结果表明,所提出的状态空间建模框架能够在组件建模阶段较好地识别黑箱设备的状态空间模型,简化子系统/组件集成阶段的装配流程,减轻系统参与因素分析阶段的计算负担。
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Black Box-Based Incremental Reduced-Order Modeling Framework of Inverter-Based Power Systems
Due to the capability to perform participation factor analysis and oscillation origin location, the state–space model (SSM)-based eigenvalue method has been widely used for stability assessment of inverter-penetrated power systems. However, possible internal confidentiality of inverters impedes the derivation of their SSMs. In addition, conventional derivation procedure of system SSM can be tedious when complicated transmission network topology and various transmission cables are involved, which may result in a high-order system SSM. To this end, this article presents a black box-based incremental reduced-order modeling framework. The reduced-order SSMs of the inverters and transmission cables are extracted from their $dq$ -domain admittance frequency responses and $abc$ -domain impedance frequency responses, respectively, by the matrix fitting algorithm. Then, the SSM operators proposed in this article recursively assemble the components' fitted SSMs in the similar manner as the impedance model operator-based recursive components' impedance aggregation, while preserving the dynamics of individual components. Simulation results show that the presented state–space modeling framework can properly identify the state–space models of black-box devices at component modeling stage, simplify assembling procedure at subsystems/components integration stage, and release computational burden at system participation factor analysis stage.
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来源期刊
IEEE Open Journal of the Industrial Electronics Society
IEEE Open Journal of the Industrial Electronics Society ENGINEERING, ELECTRICAL & ELECTRONIC-
CiteScore
10.80
自引率
2.40%
发文量
33
审稿时长
12 weeks
期刊介绍: The IEEE Open Journal of the Industrial Electronics Society is dedicated to advancing information-intensive, knowledge-based automation, and digitalization, aiming to enhance various industrial and infrastructural ecosystems including energy, mobility, health, and home/building infrastructure. Encompassing a range of techniques leveraging data and information acquisition, analysis, manipulation, and distribution, the journal strives to achieve greater flexibility, efficiency, effectiveness, reliability, and security within digitalized and networked environments. Our scope provides a platform for discourse and dissemination of the latest developments in numerous research and innovation areas. These include electrical components and systems, smart grids, industrial cyber-physical systems, motion control, robotics and mechatronics, sensors and actuators, factory and building communication and automation, industrial digitalization, flexible and reconfigurable manufacturing, assistant systems, industrial applications of artificial intelligence and data science, as well as the implementation of machine learning, artificial neural networks, and fuzzy logic. Additionally, we explore human factors in digitalized and networked ecosystems. Join us in exploring and shaping the future of industrial electronics and digitalization.
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