Multilevel inverters (MLIs) have become fundamental in contemporary power electronics, providing enhanced performance compared to conventional two-level inverters regarding their output voltage quality, efficiency, and scalability. This study comprehensively assesses multilevel inverter technologies, including their topologies, control systems, and various applications. The study starts with a comprehensive examination of the core concepts of MLIs, subsequently embarking on a detailed evaluation of both conventional and innovative topologies, such as diode-clamped, flying capacitor, cascaded H-bridge, and modular multilevel converters. The study further examines the control systems used in MLIs, including Pulse Width Modulation (PWM), space vector modulation (SVM), and Model Predictive Control (MPC), emphasizing their benefits and drawbacks. The applications of MLIs in renewable energy systems, electric cars, industrial drives, and grid integration are comprehensively examined. The study closes by examining growing trends, difficulties, and future research paths, emphasizing the ability of MLIs to transform power conversion systems.

This paper proposed a red deer algorithm (RDA)-based selective harmonic elimination (SHE) method for multilevel inverters (MLIs). To eliminate the desired harmonic orders, the optimum switching angles of the MLI have been calculated using the proposed RDA. The calculated switching angles have been applied to the 3-phase cascaded H-bridged 11-level inverter. In addition, the performance of the proposed RDA method was compared with the results of methods such as the Newton-Raphson (NR) method, LSHADE/EpSin technique (LSHADE), whale optimization algorithm (WOA), and particle swarm optimization (PSO) used for the SHE problem in the literature. The results obtained prove that the proposed RDA optimization solves the SHE problems more effectively than other methods. It has also been observed that RDA produces good solutions in different modulation indexes.

Unmanned Aerial Vehicles (UAVs) have revolutionized data acquisition across various domains, presenting immense potential for image processing and semantic segmentation. This literature review encompasses a thorough exploration of advancements, techniques, challenges, and datasets pertaining to UAV image semantic segmentation. It begins by introducing the fundamental concepts of UAVs, highlighting their pivotal role in capturing high-resolution imagery that serves diverse applications. The integration of deep learning algorithms with UAVs is emphasized, unlocking new horizons in autonomous flight, security, and environmental monitoring. Delving into the core principles of semantic segmentation, the review elucidates the critical task of classifying every pixel in an image. Convolutional Neural Networks (CNNs) are presented as the cornerstone technology, tracing their evolution from traditional CNNs to the highly adaptable Fully Convolutional Networks (FCNs). A substantial portion of the review is dedicated to FCNs, underscoring their ability to process images of varying dimensions while maintaining spatial coherence in the output. Their pivotal role in semantic segmentation, encompassing both classification and localization, is articulated. The subsequent sections delve into a comprehensive survey of state-of-the-art models, including SegNet, PSPNet, DeepLabNet, EfficientNet, DenseNet-C, and LinkNet. Each model's unique strengths and applications contribute to the evolving landscape of semantic segmentation tasks. The versatility of the U-Net architecture takes center stage in the latter parts of the review. Its fundamental structure is elucidated, followed by a comprehensive examination of its manifold adaptations—3D-U-Net, ResU-Net, U-Net++, Adversarial U-Net, Cascaded U-Net, and Improved U-Net 3+. These modifications address intrinsic challenges such as limited receptive fields and class imbalances, propelling U-Net to the forefront of image segmentation techniques. The subsequent sections pivot toward the application of U-Net in UAV image segmentation, illustrating its efficacy in diverse tasks, including land cover and crop classification. Nevertheless, persisting challenges, such as the scarcity of annotated datasets and the need for model generalization across varied environmental conditions, remain key areas of concern. The review culminates by underlining the significance of large, authentic datasets and data augmentation techniques. Furthermore, a brief exploration of publicly available UAV image datasets is presented, enhancing our understanding of the resources accessible for training and evaluating models. This comprehensive literature review encapsulates the dynamism of UAV image processing and semantic segmentation, illuminating recent developments and avenues for future research in this burgeoning field.

Modern power electronics depend heavily on Multilevel Inverters (MLIs) to drive high-power systems operating in renewable energy systems electric vehicles along with industrial motor drives. MLIs create AC signals of high quality by joining multiple DC voltage sources which leads to minimal harmonic distortion outputs. The Cascaded H-Bridge MLI (CHB-MLI) stands out as a first choice among different topologies of MLI for photovoltaic (PV) applications because it includes modular features with fault tolerance capabilities and excellent multi-DC source integration. To achieve effective operation MLIs need optimized control strategies that reduce harmonics while maintaining highest performance. Using SHE-PWM technology provides an effective technique for harmonic frequency reduction which allows the improvement of waveform integrity. Technical restrictions make the solution of SHE-PWM nonlinear equations exceptionally challenging to implement. The resolution of complex non-linear equations requires implementation of GA combined with PSO and BO for optimal switching angle determination. The research investigates an 11-level asymmetric CHB-MLI using five solar panels where SHE-PWM switching angles are optimized through GA, PSO and BO applications. Simulation tests validate that the implemented algorithms succeed in minimizing Total Harmonic Distortion (THD) and removing fundamental harmonic disturbances. The evaluation demonstrates distinct capabilities of each optimization approach between accuracy rates and computational speed performance. These optimization methods yield practical advantages which boost the performance of multi-level inverters. The researchers who follow should study actual hardware deployments together with combined control approaches to enhance power electronic applications.

This paper has investigated the application of the definite time over-current (DTOC) which reacts to protect the breaker from damage during the occurrence of over-current in the transmission lines. After a distance relay, this kind of over-current relay is utilized as backup protection. The overcurrent relay will provide a signal after a predetermined amount of time delay, and the breaker will trip if the distance relay does not detect a line failure. As a result, this over-current relay functions with a time delay that is just slightly longer than the combined working times of the distance relay and the breaker. This DTOC is tested for various types of faults which are 3- phase fault occurring at load 1, 3-phase fault occurring at load 2, a 3-phase fault occurring before primary protection, and the behaviour of voltage and current with a failed primary protection. All the results will be obtained using the MATLAB/Simulink software package.