The limitations of fossil fuel-based energy sources, such as rising prices, environmental harm, scarcity, and pollution, underscore the urgency of transitioning to renewable energy. Harnessing wind energy efficiently requires implementing various control strategies due to its unpredictable nature. This paper introduces a 3L multilevel converter integrated with a Permanent Magnet Synchronous Generator (PMSG)-based wind energy conversion system (WECS). It delves into the design considerations of WECS components, encompassing the fundamental models of two drivetrains and wind turbines. The adaptive fuzzy logic control, coupled with a PI type maximum power point tracking (MPPT) algorithm, optimizes wind energy extraction. Additionally, the utilization of 3-L multilevel converters enhances the system's efficiency. The primary objective is to maximize energy extraction from the wind, ensuring the system's high efficiency. To achieve maximum power point tracking across diverse conditions, the developed control algorithm undergoes rigorous testing using simulation software. By integrating diode rectifiers, 3-L boost converters, and 3-L multilevel inverters, the system maintains balanced and regulated output voltage, current, and power amidst varying input/output conditions and disturbances. Compared to conventional methods, the Intelligent Control Strategy proves superior, exhibiting enhanced stability, precision, and performance with robust dynamic response under fluctuating wind speeds.

This research proposes a solution to the challenges posed by the intermittent nature of renewable energy sources (RES) like wind and solar electricity through the utilization of a hybrid RES system, comprising a solar photovoltaic (PV) array and a wind turbine generator (WTG). The system's power delivery and quality are enhanced using AI technology, particularly neural networks employed in an innovative eye-based control technique. By integrating energy storage systems (ESS) and employing advanced AI-based control methods, the power quality of the hybrid RES system is significantly improved. This improvement, characterized by reduced voltage and frequency stress, enables the system to operate reliably across various weather conditions, thus ensuring consistent and dependable power supply. Standalone hybrid renewable energy systems, combining solar, wind, and energy storage devices, offer a viable solution for delivering safe and reliable power in rural areas. However, challenges such as voltage swings and harmonic distortion may arise due to the erratic nature of renewable energy sources. In this study, an AI-driven approach is proposed to address these challenges. The system predicts the output power of solar and wind systems using techniques like artificial neural networks (ANN) and fuzzy logic (FL), enabling the energy storage system to balance power generation and consumption based on anticipated values. This dynamic adjustment of output power to match load demand enhances power quality. To ensure the accuracy of prediction models and optimize system effectiveness, a real-time monitoring system is implemented. MATLAB/Simulink simulations validate the proposed approach, demonstrating significant improvements in power quality metrics such as total harmonic distortion (THD) reduction and power factor (PF) enhancement. Additionally, the proposed technique maintains a consistent power output across varying weather conditions, thereby enhancing system reliability.

Drone technology is advancing quickly; effective and secure autonomous navigation systems are crucial. This paper presents a novel approach to enhance autonomous navigation and collision avoidance in drone technology using Deep Reinforcement [11] Learning (DRL). Our objective is to apply DRL algorithms to improve drone decision-making abilities, enabling them to navigate complex environments more effectively and safely.