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 paper describes the design of a solar photovoltaic (PV) system using simulation of PVsyst software. This work involves the simulation of bifacial and mono-facial PV solar in a large-scale solar system. For a bifacial system, the yearly total energy to the grid is 1699.6 MWh, with an average of 4.57 kWh/kWp/day. For a mono-facial system, the total energy to the grid over the year is 1645.3 MWh, with an average of 4.05 kWh/kWp/day. The average collection losses obtained for bifacial and mono-facial modules were 0.33 kWh/kWp/day and 0.82 kWh/kWp/day, respectively, with system losses of 0.15 kWh/kWp/day (bifacial) and 0.18 kWh/kWp/day (mono-facial). The average performance ratio for bifacial and mono-facial was 0.904 and 0.801, respectively. The bifacial PV system was able to generate profit in terms of Return on investment (ROI), around 357%, and reach a breakeven around 7 years. The payback period for a mono-facial PV system was around 8.1 years, with an ROI of 290.4%. This work mainly focuses on a comparative analysis of bifacial and mono-facial photovoltaics, emphasizing the effectiveness and the implementation suitability of bifacial photovoltaics over mono-facial PV solar systems.

The mushroom farming industry is on the rise and there is a growing demand for high-quality, sustainable production methods. However, mushroom cultivation can be challenging for growers, as it requires careful environmental control and can be labour-intensive. This paper presents the results and analysis of a mushroom classification system, designed to accurately classify two types of mushrooms: Shiitake and oyster mushrooms. The system utilizes advanced machine learning algorithms and an extensive dataset to achieve accurate classification results. Technology replaces human inspection with computer vision, providing farmers with a quick and precise tool for classifying various mushroom species. The augmented and labelled mushroom dataset is divided into three distinct subsets: validation, testing and training. In this study, 80% of the data is allocated for training, while 20% is reserved for testing. The outcomes have proven the efficacy of You Only Look Once version 8 (YOLOv8) model, with an astounding accuracy of over 90%. This study suggests that the YOLOv8 model has the potential to be an accurate method for distinguishing different varieties of mushrooms. Farmers can significantly reduce the time and effort they previously expended on manual inspections by utilizing this approach.

The integration of Electric Vehicle Charging Stations (EVCSs) into distribution networks presents significant challenges, including increased power losses and voltage instability. This paper presents an optimization framework for the optimal placement of EVCSs in radial distribution systems with existing distributed generation. The objective is to minimize total active power loss over a 24-hour period while adhering to operational constraints, including bus voltage limits and line thermal capacities. A deterministic Non-Linear Programming (NLP) model is formulated and solved using the Interior Point Optimizer (IPOPT). The methodology is validated on the IEEE 33-bus and 69-bus networks, which incorporate fixed photovoltaic and biomass generation units. The proposed deterministic approach successfully identified optimal EVCS locations that significantly reduce system-wide power losses and improve voltage stability. To validate its reliability, the proposed method is benchmarked against three popular metaheuristic algorithms: Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Grey Wolf Optimizer (GWO). The results demonstrate that while the deterministic NLP framework entails a higher computational cost, it consistently outperforms all three metaheuristic methods in solution quality. This advantage is particularly pronounced in the larger 69-bus network, where the NLP model achieved a 47.15% reduction in power losses, whereas the metaheuristic algorithms failed to exceed 7.1% due to premature convergence. These findings indicate that, for standard radial distribution systems, the deterministic optimization framework offers a superior trade-off between solution optimality and reliability compared to stochastic metaheuristics, albeit at a higher computational cost. While the NLP model guarantees convergence to a high-quality solution, the study highlights the importance of selecting the appropriate optimization strategy based on the specific network size and available computational resources.

This paper describes the design of a solar photovoltaic (PV) system using simulation of PVsyst software. This work involves the simulation of bifacial and mono-facial PV solar in a large-scale solar system. For a bifacial system, the yearly total energy to the grid is 1699.6 MWh, with an average of 4.57 kWh/kWp/day. For a mono-facial system, the total energy to the grid over the year is 1645.3 MWh, with an average of 4.05 kWh/kWp/day. The average collection losses obtained for bifacial and mono-facial modules were 0.33 kWh/kWp/day and 0.82 kWh/kWp/day, respectively, with system losses of 0.15 kWh/kWp/day (bifacial) and 0.18 kWh/kWp/day (mono-facial). The average performance ratio for bifacial and mono-facial was 0.904 and 0.801, respectively. The bifacial PV system was able to generate profit in terms of Return on investment (ROI), around 357%, and reach a breakeven around 7 years. The payback period for a mono-facial PV system was around 8.1 years, with an ROI of 290.4%. This work mainly focuses on a comparative analysis of bifacial and mono-facial photovoltaics, emphasizing the effectiveness and the implementation suitability of bifacial photovoltaics over mono-facial PV solar systems.