The keys factor in making wind power one of the main power sources to meet the world's growing energy demands is the reliability improvement of wind turbines (WTs). However, the eventuality of fault occurrence on WT components cannot be avoided, especially for doubly-fed induction generator (DFIG) based WTs, which are operating in severe environments. The maintenance need increases due to unexpected faults, which in turn leads to higher operating cost and poor reliability. Extensive investigation into DFIG internal fault detection techniques has been carried out in the last decade. This paper presents a detailed review of these techniques. It discusses the methods that can be used to detect internal electrical faults in a DFIG stator, rotor, or both. A novel sorting technique is presented which takes into consideration different parameters such as fault location, detection technique, and DFIG modelling. The main mathematical representation used to detect these faults is presented to allow an easier and faster understanding of each method. In addition, a comparison is carried out in every section to illustrate the main differences, advantages, and disadvantages of every method and/or model. Some real monitoring systems available in the market are presented. Finally, recommendations for the challenges, future work, and main gaps in the field of internal faults in a DFIG are presented. This review is organized in a tutorial manner, to be an effective guide for future research for enhancing the reliability of DFIG-based WTs.

Electric vehicles (EVs) rely heavily on lithium-ion battery packs as essential energy storage components. However, inconsistencies in cell characteristics and operating conditions can lead to imbalanced state of charge (SOC) levels, resulting in reduced capacity and accelerated degradation. This study presents an active cell balancing method optimized for both charging and discharging scenarios, aiming to equalize SOC across cells and improve overall pack performance. The proposed system includes two balancing strategies: a charging balance that redistributes excess charge from high-SOC cells to maximize capacity, and a discharging balance that addresses low-SOC cells to extend discharge duration. Experimental results confirm that this method effectively reduces SOC disparities, enhancing both charging and discharging capacities. Additionally, to accurately predict battery lifespan and remaining useful life (RUL), seven machine learning models are evaluated using R-squared (R²) and Mean Absolute Error (MAE) metrics. Among these, k-nearest Neighbors and Random Forest models deliver the highest accuracy, achieving R² values of 0.996 and above with low MAE, demonstrating strong predictive capability. The integration of active balancing and RUL prediction enables a feedback loop where balanced SOC levels promote battery health, and RUL predictions inform optimal balancing strategies. This comprehensive approach advances EV battery management, enhancing lifespan and reliability through proactive balancing and predictive insights.

Shipboard microgrids (SBMGs) are becoming increasingly popular in the power industry due to their potential for reducing fossil-fuel usage and increasing power production. However, operating SBMGs poses significant challenges due to operational and environmental constraints. To address these challenges, intelligent control, management, and protection strategies are necessary to ensure safe operation under complex and uncertain conditions. This paper provides a comprehensive review of SBMGs, including their classifications, control, management, and protection, as well as the most recent research statistics in these areas. The state-of-the-art SBMG types, propulsion systems, and power system architectures are discussed, along with a comparison of recent research contributions and issues related to control, uncertainties, management, and protection in SBMGs. In addition, a bibliometric analysis is performed to examine recent trends in SBMG research. This paper concludes with a discussion of research gaps and recommendations for further investigation in the field of SBMGs, highlighting the need for more research on the optimization of SBMGs in terms of efficiency, reliability, and cost-effectiveness, as well as the development of advanced control and protection strategies to ensure safe and stable operation.

Electric vehicles (EVs) offer substantial environmental benefits but introduce challenges to power grids due to capacity limitations. Effective deployment and coordination of EV Charging Stations (EVCS) are crucial, as improper placement can lead to increased power losses and voltage fluctuations. This paper presents a comprehensive approach to address these challenges by proposing a deep learning-based, two-tier optimization strategy for EVCS placement within distribution networks. A hybrid forecasting model that combines autoregressive integrated moving average (ARIMA) and long short-term memory (LSTM) techniques is employed to accurately predict EV charging load. The ARIMA model captures seasonality and transient patterns, while the LSTM model identifies long-term dependencies, enhancing forecasting accuracy. Additionally, the paper introduces a Hybrid Archimedes-Genetic Algorithm (HAG), which merges the Archimedes Optimization Algorithm (AOA) with the Genetic Algorithm (GA). This multi-objective approach aims to minimize both active and reactive power losses and control voltage deviations. Tested on IEEE 33-bus and 69-bus systems using MATLAB and Python, the HAG method demonstrated significant improvements, with reductions in active losses of up to 33% and 10.7%, and reactive losses of up to 33% and 10.8% compared to GA and AOA alone. These findings underscore the effectiveness of combining accurate EV charging load forecasting with advanced optimization techniques to optimize EVCS placement while minimizing network impact.