Grid-Forming Inverters: A Comparative Study of Different Control Strategies in Frequency and Time Domains

IF 5.2 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC
Nabil Mohammed;Harith Udawatte;Weihua Zhou;David J. Hill;Behrooz Bahrani
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引用次数: 0

Abstract

Grid-forming inverters (GFMIs) are anticipated to play a leading role in future power systems. In contrast to their counterpart grid-following inverters, which employ phase-locked loops for synchronization with the grid voltage and rely on stable grid connections, GFMIs primarily employ the power-based synchronization concept to form the voltage. Hence, they can not only stably operate in regions of the grid characterized by low strength but also provide critical ancillary services to power systems, including voltage, frequency, and inertia support. Several control strategies have been employed for GFMIs, making it crucial to comprehend their stability characteristics for the analysis of small-signal stability and low-frequency oscillations. This article examines the performance of GFMIs when equipped with four different control strategies, namely, droop-based GFMI, virtual synchronous generator (VSG)-based GFMI, compensated generalized VSG (CGVSG)- based GFMI, and adaptive VSG (AVSG)-based GFMI. The comparative analysis assesses the performance and robustness of these four control strategies across various operational scenarios in frequency and time domains. Initially, the impedance-based stability analysis method is employed to evaluate these control strategies across different case studies in terms of grid strengths, grid impedance ratios, the dynamics with/without virtual impedance and inner voltage and current loops, and variations in the inverter's operating points. Subsequently, time-domain verification using the electromagnetic transient models is conducted for these case studies as well as to assess the power tracking capability of these control strategies in response to changes in power references. Finally, the robustness of these four controllers is explored against external grid disturbances, including grid frequency deviations, phase jumps, and voltage sags, considering varying levels of disturbance magnitudes in both weak and strong grid connections. In conclusion, the evaluation of these control techniques in various operational scenarios reveals their strengths and weaknesses, offering valuable guidance for the selection of the most appropriate control technique to suit desired practical applications.
并网逆变器:频域和时域不同控制策略的比较研究
并网逆变器(GFMI)预计将在未来的电力系统中发挥主导作用。与采用锁相环与电网电压同步并依赖稳定电网连接的同类电网跟随型逆变器相比,GFMI 主要采用基于功率的同步概念来形成电压。因此,它们不仅能在电网强度较低的区域稳定运行,还能为电力系统提供重要的辅助服务,包括电压、频率和惯性支持。GFMI 采用了多种控制策略,因此了解其稳定性特征对于分析小信号稳定性和低频振荡至关重要。本文研究了 GFMI 配备四种不同控制策略时的性能,即基于下垂的 GFMI、基于虚拟同步发电机 (VSG) 的 GFMI、基于补偿广义 VSG (CGVSG) 的 GFMI 和基于自适应 VSG (AVSG) 的 GFMI。对比分析评估了这四种控制策略在频域和时域各种运行情况下的性能和鲁棒性。首先,采用基于阻抗的稳定性分析方法,从电网强度、电网阻抗比、有/无虚拟阻抗和内部电压电流环路的动态以及逆变器工作点的变化等方面,对不同案例研究中的控制策略进行评估。随后,利用电磁瞬态模型对这些案例研究进行了时域验证,并评估了这些控制策略在响应功率基准变化时的功率跟踪能力。最后,考虑到弱电网和强电网连接中不同程度的扰动幅度,探讨了这四种控制器对外部电网扰动的鲁棒性,包括电网频率偏差、相位跃变和电压骤降。总之,在各种运行情况下对这些控制技术的评估揭示了它们的优缺点,为选择最合适的控制技术以适应所需的实际应用提供了宝贵的指导。
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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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