Wonsuk Ko

Integrating large-scale photovoltaic (PV) systems into the electrical grid presents significant challenges due to power fluctuations caused by the intermittent nature of the solar irradiance. This study introduces a novel physics-informed Generative Artificial Intelligence (GAI) control framework designed for Maximum Power Point Tracking (MPPT) and power smoothing within a 1 MW grid-connected PV-Battery system. Therefore, the core novelty lies in replacing traditional physical perturbations with a virtual manifold search and utilizing adaptive nonlinear hyperbolic tangent activation functions to achieve a highly stable and deterministic control. The proposed framework utilizes two distinct GAI approaches to meet different operational aims: the GAI-MPPT focuses on voltage optimization to maximize PV power extraction through a boost converter, while the GAI-Battery Energy Storage System (GAI-BESS) regulates battery current (charging/discharging) via a bidirectional buck–boost converter for dynamic PV power smoothing. The performance of both GAI approaches is compared with conventional Incremental Conductance–Proportional Integral (InC-PI) and Particle-Swarm-Optimization-based PI (PSO-PI) methods, across four seasonal profiles. The simulation results indicate that the proposed GAI-MPPT approach outperforms the InC-PI and PSO-PI methods, achieving up to a 4% increase in peak power extraction efficiency against InC-PI and a 1.5% improvement against PSO-PI during summer season. Furthermore, the GAI-BESS delivers smoother transient responses and minimizes storage component stress by reducing the required peak battery discharge power by up to 33.3 kW and 14.0 kW compared to the InC-PI and PSO-PI methods, respectively. Ultimately, these quantifiable enhancements prove that the proposed method is a robust solution for large-scale renewable energy integration.

High-Voltage Alternating Current (HVAC) transmission systems form the backbone of modern power grids, enabling efficient long-distance and high-capacity power delivery. In Saudi Arabia, ongoing initiatives to modernize and strengthen grid infrastructure demand advanced solutions to ensure system reliability, operational stability, and the minimization of economic losses caused by faults. Traditional fault detection and classification methods often depend on the manual interpretation of voltage and current signals, which is both labor-intensive and prone to human error. Although data-driven approaches such as Artificial Neural Networks (ANNs) and Deep Learning have been applied to automate fault analysis, their performance is often constrained by the quality and size of available training datasets, leading to poor generalization and physically inconsistent outcomes. This study proposes a novel hybrid fault detection and classification framework for the 380 kV Marjan–Safaniyah HVAC transmission line by integrating Deep Learning with Physics-Informed Neural Networks (PINNs). The PINN model embeds fundamental electrical laws, such as Kirchhoff’s Current Law (KCL), directly into the learning process, thereby constraining predictions to physically plausible behaviors and enhancing robustness and accuracy. Developed in MATLAB/Simulink using the Deep Learning Toolbox, the proposed framework performs fault detection and fault type classification within a unified architecture. A comparative analysis demonstrates that the hybrid PINN approach significantly outperforms conventional Deep Learning models, particularly by reducing false negatives and improving class discrimination. Furthermore, this study highlights the crucial role of balanced and representative datasets in achieving a reliable performance. Validation through confusion matrices and KCL residual histograms confirms the enhanced physical consistency and predictive reliability of the model. Overall, the proposed framework provides a powerful and scalable solution for real-time monitoring, fault diagnosis, and intelligent decision-making in high-voltage power transmission systems.

The integration of photovoltaic (PV) into electric power systems has been widely explored and adopted to address the problems associated with the depletion of fossil fuels and the release of greenhouse gases. PV panels convert sunlight into electricity, minimizing the reliance on fossil fuels and mitigating environmental pollution. It is crucial to optimally utilize the PV power in the system; hence maximum power point tracking (MPPT) algorithms have been developed to ensure optimal performance of grid-connected PV systems at the maximum power point (MPP) despite changes in weather conditions. Moreover, deep reinforcement learning (DRL) developments provide a promising approach for optimizing grid-connected PV systems, replacing the conventional proportional-integral-derivative (PID) controllers. However, there is limited research evaluating the efficiency of these systems using DRL techniques. This paper proposes a new dandelion optimizer (DO)-based DRL for MPPT of grid-connected photovoltaic systems and evaluates the proposed method for a 100-MW PV plant connected to a 33-kV distribution system. The proposed DRL technique uses proximal policy optimization (PPO) and deep deterministic policy gradient (DDPG) algorithms for continuous states and discrete or continuous action spaces to adjust the PV-measured voltage based on a reference one produced via DO-PPO and DO-DDPG methods. To test the effectiveness and practicality of the introduced methods, simulations were conducted using actual input data of a 100 MW PV plant connected to a 33-kV distribution system for typical days in summer and winter seasons using MATLAB/Simulink software. The proposed implemented methods were evaluated by comparing their simulation results with other techniques: DO-PID, particle swarm optimization (PSO), and incremental conductance (InC-PI). The findings revealed that the efficiencies of the DC-DC boost and the voltage source converters using the introduced methods were 84.25%-85.90%, and 78.33%-81.10% on a summer day while they were 92.77%-95% and 86.70%-89.50% on a winter day, respectively, which proves that these methods were efficient and effective, indicating their promising potential for future applications.

The efficiency of grid-connected photovoltaic (PV) systems can be improved using a suitable maximum power point tracking (MPPT) control. Many MPPT techniques have been developed. However, when atmospheric conditions change rapidly, the PV modules respond nonlinearly and the performance of these methods especially the conventional ones is significantly reduced. Therefore, to successfully pursue the maximum power point (MPP) in PV systems, a robust MPPT integrated with sliding mode control (SMC) is employed. This paper proposes a circle search algorithm based super twisting SMC (CSA-STSMC) for tracking the MPP for 100-kW of different commercial grid-connected PV modules coupled with an energy storage system that used to smooth their output fluctuations. Moreover, the STSMC and PI controllers’ gains are optimized using the CSA and Grey Wolf Optimizer (GWO) methods and the performance of proposed method is demonstrated through comparisons with CSA based Proportional-Integral controller (CSA-PI), GWO-STSMC, and Incremental Conductance based PI controller (InC-PI) methods. The optimization and simulation analysis are executed in A MATLAB/Simulink environment. It is observed that the proposed method outperforms the considered methods in terms of robustness, tracking speed, best convergence to the minimum error’s value and better tracking efficiency in the maximizing the extracted power from the introduced PV systems.

The prototype development of an automatic and floating structured hydro power plant constitutes a pioneering initiative aimed at addressing the critical issue of energy scarcity in remote areas. This research is centered on creation of an innovative and sustainable energy solution that capitalizes on diverse water flow conditions. It involves design and construction of a floating hydro power plant that seamlessly integrates a Pico hydro turbine, generator, and control system. The IoT technology is incorporated to enable real-time monitoring and visualization of essential electrical parameters through mobile app. In light of the escalating global demand for renewable energy solutions, this research holds immense significance. By extending access to electricity to off-grid communities, especially those residing in remote regions where conventional energy infrastructure is impractical, the proposed floating hydro power plant offers a reliable, eco-friendly power source. Its deployment in rivers and water bodies underscores its adaptability and potential to significantly alleviate energy deficits in under-served areas. As the world grapples with the pressing need for a transition toward sustainable energy sources, this project serves as a beacon of ingenuity and progress. By broadening the scope of the original research and exploring avenues for further enhancement, this endeavor underscores the commitment to a future where accessible, clean, and efficient power generation can transform the lives of communities plagued by energy scarcity. The outcomes and implications of this research endeavor not only contribute to the field of renewable energy but also provide a foundation for future innovations in sustainable power generation systems.

Long-distance offshore wind power transmission systems utilize multi-terminal high voltage direct current (MT-HVDC) connections based on voltage source converters (VSCs). In addition to having the potential to work around restrictions, the VSC-based MT-HVDC transmission system has significant technical and economic merits over the HVAC transmission system. Offshore wind farms (OWFs) will inevitably grow because of their outstanding resistance to climate change and ability to provide sustainable energy without producing hazardous waste. Due to stronger and more persistent sea winds, the OWF often has a higher generation capacity with less negative climate effects. The majority of modern installations are distant from the shore and produce more power than the early OWF sites, which are situated close to the shore. This paradigm shift has compelled industry and professional researchers to examine transmission choices more closely, specifically HVAC and HVDC transmission. This article conducts a thorough analysis of grid connection technologies for massive OWF integration. In comparison to earlier assessments, a more detailed discussion of HVDC and HVAC topologies, including HVDC based on VSCs and line-commutated converters (LCCs), and all DC transmission systems, is offered. Finally, a selection criterion for HVDC transmission is advised, and its use is argued to be growing.

The rapid revolution in the solar industry over the last several years has increased the significance of photovoltaic (PV) systems. Power photovoltaic generation systems work in various outdoor climate conditions; therefore, faults may occur within the PV arrays in the power system. Fault detection is a fundamental task needed to improve the reliability, efficiency, and safety of PV systems, and, if not detected, the cost associated with the loss of power generated from PV modules will be quite high. Moreover, maintenance staff will take more time and effort to fix undetermined faults. Due to the current-limiting nature and nonlinear output characteristics of PV arrays, fault detection is not that easy and the application of artificial intelligence is proposed for the sake of fault detection in PV systems. The idea behind this approach is to compare the faulty PV module with its accurate model (factory fingerprint) by checking every PV array’s I–V and P–V curves using the Artificial Neural Network (ANN) logarithm as a subsection of the Artificial Intelligence’s (AI) techniques. This proposed approach achieves a high performance of fault detection and gives the advantage of determining what type of fault has occurred. The results confirm that the proposed logarithm performance becomes better as the number of distinguishing points extend, providing great value to the Solar PV industry.

An effective maximum power point tracking (MPPT) technique plays a crucial role in improving the efficiency and performance of grid-connected renewable energy sources (RESs). This paper uses the African Vulture Optimization Algorithm (AVOA), a metaheuristic technique inspired by nature, to tune the proportional–integral (PI)-based MPPT controllers for hybrid RESs of solar photovoltaic (PV) and wind systems, as well as the PI controllers in a storage system that are used to smooth the output fluctuations of those RESs in a hybrid system. The performance of the AVOA is compared with that of the widely used the particle swarm optimization (PSO) technique, which is commonly acknowledged as the foundation of swarm intelligence. As a result, this technique is introduced in this study to draw a comparison. It is observed that the proposed algorithm outperformed the PSO algorithm in terms of the tracking speed, robustness, and best convergence to the minimum value. A MATLAB/Simulink model was built, and optimization and simulation for the proposed system were carried out to verify the introduced algorithms. In conclusion, the optimization and simulation results showed that the AVOA is a promising method for solving a variety of engineering problems.