Over the last decade, energy demand from the power grid has increased significantly due to the increasing number of users and the emergence of high-power industries. This has led to a significant increase in global emissions with conventional energy generation. Therefore, the penetration of renewable energy resources into the power grid has increased significantly. Photovoltaic systems have become the most popular resources as their protentional is enormous, thus, the worldwide installed PV capacity has increased to more than 635 gigawatts (GW), covering approximately 2% of the global electricity demand. Power electronics are an essential part of photovoltaic generation; the drive for efficient power electronic converters is gaining more and more momentum. Presently, multilevel inverters (MLI) have become more attractive to researchers compared to two-level inverters due to their abilities to provide lower electromagnetic interference, higher efficiency, and larger DC link voltages. This paper reviews multilevel inverters based on their classifications, development, and challenges with practical recommendations in utilizing them in renewable energy systems. Moreover, PV systems with various maximum power point tracking (MPPT) methods have been extensively considered in this paper as well. The importance and the development of a modified multilevel inverter are also highlighted in this review. In general, this paper focuses on utilizing multilevel inverters for PV systems to motivate and guide society to focus on inventing an efficient and economical multilevel inverter that has the combined capabilities of these converters reported in the literature.

This review comprehensively examines the burgeoning field of intelligent techniques to enhance power systems’ stability, control, and protection. As global energy demands increase and renewable energy sources become more integrated, maintaining the stability and reliability of both conventional power systems and smart grids is crucial. Traditional methods are increasingly insufficient for handling today’s power grids’ complex, dynamic nature. This paper discusses the adoption of advanced intelligence methods, including artificial intelligence (AI), deep learning (DL), machine learning (ML), metaheuristic optimization algorithms, and other AI techniques such as fuzzy logic, reinforcement learning, and model predictive control to address these challenges. It underscores the critical importance of power system stability and the new challenges of integrating diverse energy sources. The paper reviews various intelligent methods used in power system analysis, emphasizing their roles in predictive maintenance, fault detection, real-time control, and monitoring. It details extensive research on the capabilities of AI and ML algorithms to enhance the precision and efficiency of protection systems, showing their effectiveness in accurately identifying and resolving faults. Additionally, it explores the potential of fuzzy logic in decision-making under uncertainty, reinforcement learning for dynamic stability control, and the integration of IoT and big data analytics for real-time system monitoring and optimization. Case studies from the literature are presented, offering valuable insights into practical applications. The review concludes by identifying current limitations and suggesting areas for future research, highlighting the need for more robust, flexible, and scalable intelligent systems in the power sector. This paper is a valuable resource for researchers, engineers, and policymakers, providing a detailed understanding of the current and future potential of intelligent techniques in power system stability, control, and protection.

The objective of the study was to analyze chemical structure of date palm (Phoenix dactylifera L.) by employing ultimate, proximate and thermo-gravimetric techniques. Samples from different anatomical parts of date palm, namely trunk, frond base, frond midrib, leaflets, coir, fruit stem, date stone, and fruit empty bunches were considered for the experiments. Based on the findings in this work palm leaflet samples gave the highest amount of extractives content (32.9%), followed by date palm stone specimens with 31.5%. Cellulose content values of 32.8% and 47.5% were obtained for date palm stone and palm coir samples, respectively. Overall the hemicellulose contents of all samples were relatively similar to those of typical wood or non-wood lignocellulosic materials with the two exceptions of palm coir and palm leaflets. Both palm coir and palm leaflet specimens had 12.6% and 16.1% hemicellulose content. Volatile matter values of 74.3% and 87.5% were determined for leaflets and fruit empty bunch samples. The ash content of the samples ranged from 1.4% for date stone to 15.2% for palm leaflet samples. The thermal decomposition was completed below a temperature of 500 °C with an exception of those samples taken from palm leaflets. Taken together the data indicate that date palm stone and palm coir revealed could be more viable for renewable energy production than the other specimens considered in this work.