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’s phase-locked loop can be a challenging task. This paper provides a unique solution to enhance the three-phase IBR’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’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’ 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$\mu$synthesis approach (benefiting from the well-known$D\text{-}K$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.Note to Practitioners—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’ 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’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’ synchronization process. Besides, it employs measurement feedback to control the output regulation problem of uncertain dynamics of inverter-based resources’ 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’ 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’ 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’s simulations and experiments reveal that the above points give the proposed approach a competitive edge over the existing methodologies.

Three-phase grid-following (GFL) inverter-based resources (IBRs) play a vital role as an interface for integrating renewable energy resources and flexible loads, such as electric vehicles, into the power grid. This paper introduces a novel set-theoretic adaptive control scheme for the primary control of three-phase GFL IBRs, designed to mitigate the impacts of uncertainties or non-ideal conditions affecting the control layer. These uncertainties will risk losing the stability and intended operation of three-phase GFL IBRs by potentially influencing the control commands transmitted to pulse width modulators. In order to address this issue, this study proposes an add-on control signal generated through an adaptive architecture to retrofit the existing (pre-designed) state feedback controller of GFL IBRs. As the name implies, this retrofit control strategy entails upgrading or modifying the existing feedback control instead of completely replacing it. The proposed control scheme is based on a set-theoretic adaptive controller design that employs generalized restricted potential functions. A notable aspect of this framework is its ability to ensure that the reference tracking error bound remains below a user-defined threshold, making it “computable” by providing the control design parameters. The stability of the closed-loop system and the approximate reference tracking performance of the proposed control scheme for GFL IBRs are validated through a theoretical analysis employing the Lyapunov theory. Simulation-based and experimental results further confirm the efficacy of the proposed GFL IBR controller.Note to Practitioners—Given the strive to focus on renewables-intensive and modern power grids, inverters must exhibit enhanced intelligence and versatility to accommodate various functionalities. However, uncertainties or non-ideal conditions originating from diverse sources significantly threaten the optimal operation of inverter-based resources. These uncertainties introduce errors into the control loop of grid-following inverter-based resources, potentially compromising their stability and performance. In order to tackle this compelling challenge, this paper proposes a novel set-theoretic adaptive current control scheme for grid-following inverter-based resources using an add-on control signal. The aim is to mitigate the adverse effects of uncertainties affecting the control commands in grid-following inverter-based resources. By incorporating the additional control signals into the feedback controller, reference tracking is assured despite uncertainties. This approach offers a more cost-effective solution by enhancing the existing feedback control instead of entirely replacing it. Lyapunov stability theory provides a theoretical framework for analyzing stability and ensuring the uniform boundedness of output trajectories in grid-following inverter-based resources. Simulation and experimental results confirm the feasibility and effectiveness of the proposed approach in mitigating the uncertainties affecting the control commands.

Grid modernization and large-scale integration of inverter-based resources (IBRs) into distribution systems have resulted in the development of new control strategies relying on information and communication technologies. To this end, this article proposes a distributed secondary control algorithm using an event-triggered mechanism for exchanging information among IBRs over digital communication channels in islanded modern microgrids. Unlike the existing event-triggered studies, the proposed method is based on a nonlinear mapping technique for encoding shared data over digital communication channels, making it suitable for real-world applications. It enables the control system to use digitized and encoded data instead of typical continuous analog information, resulting in the more efficient usage of communication infrastructures. As a result, it can be regarded as a practical algorithm for stabilizing voltage and frequency during the transient and steady-state response of autonomous modern microgrids considering computational constraints and the limited bandwidth of communication systems. Finally, comparative simulation studies and experimental results validate the performance and effectiveness of the proposed algorithm.

This paper proposes a multivariable, adaptive, robust (MAR) control strategy for islanded inverter-based resources (IBRs) operating as grid-forming inverters. The proposed method is employed in the inner control loop of the primary layer in the hierarchical or decentralized structures for the islanded operation of microgrids. The MAR control scheme is responsible for stabilizing IBRs’ output voltage in autonomous operations of microgrids, considering mismatched input voltage disturbances from the grid side and a large amount of system uncertainty. The control methodology introduced in this paper does not rely on the system’s physical parameters, such as microgrid topology, load dynamics, LCL filters, and output connectors. As a result, there is no need to know the nominal values or the bounds of uncertainties in system dynamics. The MAR control method uses online adaptation rules first to identify and then adjust the control parameters of the closed-loop system based on an arbitrary dynamic model. In other words, the MAR method replaces the actual dynamics of IBRs with predetermined dynamics of interest. Simulation results in the MATLAB/Simulink environment confirm the capability of the scheme introduced for the closed-loop stabilization and voltage regulation in the presence of disturbances and a significant amount of uncertainty under various case studies; moreover, comparative simulations by comparing the presented method with other studies using sliding mode control are provided. Finally, experiments verify the effectiveness and practicality of the proposed MAR control scheme.Note to Practitioners—Inverter-based resources are integral parts of current and especially future power and energy systems; with increasing concerns about carbon footprints, the tendency to substitute traditional synchronous generators with inverter-based resources increases. This transition towards the widespread use of power electronics devices needs careful studies regarding the stability and control of power converters. Although existing studies are addressing potential control system challenges, they suffer from complex mathematical computations and the need for the system’s preliminary information. With this in mind, this study proposes a multivariable, adaptive, robust control strategy for the inner voltage control loop of grid-forming inverters. This method utilizes online estimation algorithms to identify inverter-based resources’ parameters and tune control system parameters simultaneously, making it applicable even to cases with slow parameter variations caused by aging or environmental changes. It can compensate for potential voltage disturbances from the grid side and enable the designer to replace undesirable dynamics of inverter-based resource units with arbitrary and stable dynamics of interest. In fact, unlike traditional methods that need control parameters and gains to be tuned to achieve a proper dynamic response, the control system designer can choose reference dynamics and enforce the closed-loop system to imitate the dynamical model selected. This model is usually chosen based on established priorities, such as response time and other transient behaviors. Moreover, this method does not require complex mathematical and algebraic calculations to design and implement. It can be easily applied to inverter-based resource units after selecting the desired reference dynamics, as shown through this study’s experimental result. The above points give this method a competitive edge over the existing algorithms, especially in practical applications.

This article proposes an adaptive, optimal, data-driven control approach based on reinforcement learning and adaptive dynamic programming to the three-phase grid-connected inverter employed in virtual synchronous generators (VSGs). This article takes into account unknown system dynamics and different grid conditions, including balanced/unbalanced grids, voltage drop/sag, and weak grids. The proposed method is based on value iteration, which does not rely on an initial admissible control policy for learning. Considering the premise that the VSG control should stabilize the closed-loop dynamics, the VSG outputs are optimally regulated through the adaptive, optimal control strategy proposed in this article. Comparative simulations and experimental results validate the proposed method's effectiveness and reveal its practicality and implementation.

In many pilot research and development (R&D) microgrid projects, engine-based generators are employed in their power systems, either generating electrical energy or being mixed with the heat and power technology. One of the critical tasks of such engine-based generation units is the frequency regulation in the islanded mode of modernized microgrid (MMG) operation; MMGs are microgrids equipped with advanced controls to address more emerging scenarios in smart grids. For having a stable and reliable MMG, we need to synthesize an optimal, robust, primary frequency controller for the islanded mode of MMG of the future. This task is challenging because of unknown mechanical parameters, occurrence of uncertain disturbances, uncertainty of loads, operating point variations, and the appearance of engine delays, and hence nonminimum phase dynamics. This article presents an innovative primary frequency control for the engine generators regulating the frequency of an islanded MMG in the context of smart grids. The proposed approach is based on an adaptive optimal output-feedback control algorithm using adaptive dynamic programming (ADP). The convergence of algorithms, along with the stability analysis of the closed-loop system, is also shown in this article. Finally, as experimental validation, hardware-in-the-loop (HIL) test results are provided in order to examine the effectiveness of the proposed methodology practically.Note to Practitioners—This article was motivated by the problem of primary frequency controls in modernized microgrids (MMGs) using engine generators, which are still one of the prime sources of regulating frequency in pilot research and development (R&D) microgrid projects. Although MMGs will be integral parts of the smart grid of the future, their primary controls in the islanded mode are not advanced enough and not considering existing theoretical challenges scientifically. Existing approaches to regulate frequency using industrially accepted methods are highly model-based and not optimal. Besides, they are not considering the nonminimum phase dynamics. These dynamics are mainly associated with the engine delays—an inherent issue of mechanical parts—for islanded microgrids. This article suggests a new adaptive optimal output-feedback control approach based on the adaptive dynamic programming (ADP) to the abovementioned problem under consideration. By using the proposed methodology, MMGs can deal with the issues mentioned earlier, which are challenging. The proposed approach is optimally rejecting uncertain disturbances (considering the load uncertainty and operating point variations) and reducing the impacts of nonminimum phase dynamics caused by the engine delay. Based on our currently available hardware-in-the-loop (HIL) device’s capability of modeling power systems’ components in real time, our HIL-based experiments demonstrate that this approach is feasible.

Throughout the search process, the user's gaze on inspected SERPs and websites can reveal his or her search interests. Gaze behavior can be captured with eye tracking and described with word-eye-fixations. Word-eye-fixations contain the user's accumulated gaze fixation duration on each individual word of a web page. In this work, we analyze the role of word-eye-fixations for predicting query terms. We investigate the relationship between a range of in-session features, in particular, gaze data, with the query terms and train models for predicting query terms. We use a dataset of 50 search sessions obtained through a lab study in the social sciences domain. Using established machine learning models, we can predict query terms with comparably high accuracy, even with only little training data. Feature analysis shows that the categories Fixation, Query Relevance and Session Topic contain the most effective features for our task.