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.

This study thoroughly compares multiple single bidirectional and multiport converters (MPCs), highlighting the significant role of MPCs in multi-input and multi-output (MIMO) systems. MPCs offer a more efficient and cost-effective solution than multiple single converters, especially in applications involving photovoltaic (PV), electric vehicles (EVs) with storage systems, and power grids. This research emphasizes the importance of multi-input converters (MICs) in integrating diverse voltage sources. It notes the rising popularity of multi-output DC-DC converters in portable electronics, owing to their reduced component count, lower costs, and compact design. This paper emphasizes comparisons based on diverse aspects and applications, shedding light on recent developments in basic bidirectional converters. Additionally, it delves into the advancements in MPC topologies, focusing on efficiency, reliability, and modularity improvements. These advancements are crucial for harnessing cost reduction, simplicity, and compactness. Furthermore, this paper introduces an innovative multiport DC-DC converter tailored for integrating and managing renewable sources. This new converter design enhances PV system and battery storage performance by reducing power conversion steps, using fewer components, and improving voltage-boosting capabilities. Its unique bidirectional buck-boost structure allows for versatile connections between sources and loads with varying voltage and power requirements. The performance of this novel converter is evaluated through MATLAB/Simulink simulations under different scenarios. Experimental studies further validate its effectiveness, marking a significant contribution to power conversion and management in integrating renewable sources such as DC microgrids.