An Optimal Synchronization Control Method of PLL Utilizing Adaptive Dynamic Programming to Synchronize Inverter-Based Resources With Unbalanced, Low-Inertia, and Very Weak Grids

When it comes to integrating inverter-based resources (IBRs) into modern grids with varying characteristics like unbalanced systems, low-inertia networks, or very weak grids, synthesizing the synchronization control method (SCM) of the IBR&#x2019;s phase-locked loop can be a challenging task. This paper provides a unique solution to enhance the three-phase IBR&#x2019;s SCM using the adaptive dynamic programming (ADP) method based on reinforcement learning. By making the SCM more intelligent and self-learning, IBRs can be easily integrated into diverse grids. To this end, this article investigates the synchronization process&#x2019;s detailed dynamics, including all incorporating disturbances and parameters required for the first step in designing the ADP method. Afterward, this research synthesizes an optimal controller using an ADP method. It is a data-driven and practically sound approach to the problem under investigation. The new methodology is based on the adaptive optimal control employing measurement feedback to control the output regulation problem of uncertain synchronization process dynamics via the internal model principle. The proposed SCM design deploys an ADP learning methodology to tackle uncertain parameters and unknown disturbance signals to synchronize IBRs during transients, thereby enhancing IBRs&#x2019; synchronization in challenging conditions of modern power systems with unbalanced, low-inertia, and very weak grids. For comparison purposes, this paper applies a robust controller based on the well-established <inline-formula> <tex-math notation="LaTeX">$\mu$</tex-math> </inline-formula> synthesis approach (benefiting from the well-known <inline-formula> <tex-math notation="LaTeX">$D\text{-}K$</tex-math> </inline-formula> iteration process). Comparative simulations are performed; experiments are conducted to reveal the effectiveness and practicality of the ADP-based optimal SCM proposed in this paper. <italic>Note to Practitioners</italic>&#x2014;As different nations strive to combat global warming and accelerate decarbonization, power and energy systems are undergoing a significant shift. Inverter-based resources are being used as an essential component to achieve these goals. However, studies have revealed that designing synchronization control methods of the inverter-based resources&#x2019; phase-locked loop in unbalanced, low-inertia, and very weak grids is challenging due to the need for accurate dynamic models and other factors. This study revisits the synchronization process&#x2019;s detailed dynamics. It also proposes a novel adaptive dynamic programming strategy using intelligent self-learning approaches to the synchronization control method associated with inverter-based resources. This method utilizes an optimal control to synthesize the adaptive dynamic programming control strategy for the inverter-based resources&#x2019; synchronization process. Besides, it employs measurement feedback to control the output regulation problem of uncertain dynamics of inverter-based resources&#x2019; synchronization process via the internal model principle. As a result, this paper makes this process data-driven. It utilizes a learning methodology using adaptive dynamic programming to address uncertain parameters and unknown disturbance signals associated with the dynamics derived and formulated for the problem under investigation. Thus, the proposed method applies to controlling inverter-based resources&#x2019; synchronization process even in cases with slow parameter variations caused by different factors. It can compensate for all functional disturbance signals affecting the dynamics of the systems. In fact, unlike traditional methods that need an exact dynamic model of the inverter-based resources&#x2019; synchronization process to design and tune the controller to achieve a proper transient response, the proposed control system trains itself and does so. This study&#x2019;s simulations and experiments reveal that the above points give the proposed approach a competitive edge over the existing methodologies.