Autonomous Underwater Vehicles (AUVs) epitomize a revolutionary stride in underwater exploration, seamlessly assuming tasks once exclusive to manned vehicles. Their collaborative prowess within joint missions has inaugurated a new epoch of intricate applications in underwater domains. This study’s primary aim is to scrutinize recent technological advancements in AUVs and their role in navigating the complexities of underwater environments. Through a meticulous review of literature and empirical studies, this review synthesizes recent technological strides, spotlighting developments in biomimicry models, cutting-edge control systems, adaptive navigation algorithms, and pivotal sensor arrays crucial for exploring and mapping the ocean floor. The article meticulously delineates the profound impact of AUVs on underwater robotics, offering a comprehensive panorama of advancements and illustrating their far-reaching implications for underwater exploration and mapping. This review furnishes a holistic comprehension of the current landscape of AUV technology. This condensed overview furnishes a swift comparative analysis, aiding in discerning the focal points of each study while spotlighting gaps and intersections within the existing body of knowledge. It efficiently steers researchers toward complementary sources, enabling a focused examination and judicious allocation of time to the most pertinent studies. Furthermore, it functions as a blueprint for comprehensive studies within the AUV domain, pinpointing areas where amalgamating multiple sources would yield a more comprehensive understanding. By elucidating the purpose, employing a robust methodology, and anticipating comprehensive results, this study endeavors to serve as a cornerstone resource that not only encapsulates recent technological strides but also provides actionable insights and directions for advancing the field of underwater robotics.

Nowadays, photovoltaics (PV) has gained popularity among other renewable energy sources because of its excellent features. However, the instability of the system’s output has become a critical problem due to the high PV penetration into the existing distribution system. Hence, it is essential to have an accurate PV power output forecast to integrate more PV systems into the grid and to facilitate energy management further. In this regard, this paper proposes a stacked ensemble algorithm (Stack-ETR) to forecast PV output power one day ahead, utilizing three machine learning (ML) algorithms, namely, random forest regressor (RFR), extreme gradient boosting (XGBoost), and adaptive boosting (AdaBoost), as base models. In addition, an extra trees regressor (ETR) was used as a meta learner to integrate the predictions from the base models to improve the accuracy of the PV power output forecast. The proposed model was validated on three practical PV systems utilizing four years of meteorological data to provide a comprehensive evaluation. The performance of the proposed model was compared with other ensemble models, where RMSE and MAE are considered the performance metrics. The proposed Stack-ETR model surpassed the other models and reduced the RMSE by 24.49%, 40.2%, and 27.95% and MAE by 28.88%, 47.2%, and 40.88% compared to the base model ETR for thin-film (TF), monocrystalline (MC), and polycrystalline (PC) PV systems, respectively.

A voltage source inverter (VSI) is the key component of grid-tied AC Microgrid (MG) which requires a fast response, and stable, robust controllers to ensure efficient operation. In this paper, a fuzzy logic controller (FLC)-based direct power control (DPC) method for photovoltaic (PV) VSI was proposed, which was modelled by modulating MG’s point of common coupling (PCC) voltage. This paper also introduces a modified grid synchronization method through the direct power calculation of PCC voltage and current, instead of using a conventional phase-locked loop (PLL) system. FLC is used to minimize the errors between the calculated and reference powers to generate the required control signals for the VSI through sinusoidal pulse width modulation (SPWM). The proposed FLC-based DPC (FLDPC) method has shown better tracking performance with less computational time, compared with the conventional MG power control methods, due to the elimination of PLL and the use of a single power control loop. In addition, due to the use of FLC, the proposed FLDPC exhibited negligible steady-state oscillations in the output power of MG’s PV-VSI. The proposed FLDPC method performance was validated by conducting real-time simulations through real time digital simulator (RTDS). The results have demonstrated that the proposed FLDPC method has a better reference power tracking time of 0.03 s along with reduction in power ripples and less current total harmonic distortion (THD) of 1.59%.